WO2000034910A2 - Customer relationship management system and method - Google Patents

Customer relationship management system and method Download PDF

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Publication number
WO2000034910A2
WO2000034910A2 PCT/US1999/029247 US9929247W WO0034910A2 WO 2000034910 A2 WO2000034910 A2 WO 2000034910A2 US 9929247 W US9929247 W US 9929247W WO 0034910 A2 WO0034910 A2 WO 0034910A2
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Prior art keywords
computer
volume
campaign
implemented
marketing campaign
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PCT/US1999/029247
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French (fr)
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WO2000034910A8 (en
Inventor
Evangelos Simoudis
Prakash Mayank
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Customer Analytics, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Customer Analytics, Inc. filed Critical Customer Analytics, Inc.
Priority to AU21716/00A priority Critical patent/AU2171600A/en
Publication of WO2000034910A2 publication Critical patent/WO2000034910A2/en
Publication of WO2000034910A8 publication Critical patent/WO2000034910A8/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the present invention relates generally to software for marketing applications, and more particularly to a system and method of planning and implementing customer relationship management.
  • What is further needed is a system that provides software tools for use in all phases of customer relationship management in an integrated manner. What is further needed is a software system and method that can maximize results in customer relationship management by performing specification, analysis, design, execution, and tracking functionality for the entire life cycle. What is further needed is a software system and method capable of integrating analytics such as data mining, predictive modeling, statistical and OLAP processing, neural networks, and the like, into a single framework designed to support the life cy- die of customer relationship management.
  • the present invention thus enables businesses to employ economies of scale of larger operations and institutions, so as to reduce costs, while retaining the ability to tailor product and service offerings, as well as marketing efforts, to individual customers.
  • the invention thus promotes the building of long-term relationships with customers, rather than treating each interaction as a stand-alone transaction. This is beneficial for both the business establishment and its customers: customer loyalty is enhanced, which reduces customer development costs; improved targeting of marketing campaigns reduces customer acquisition costs; and customers feel that the business is becoming more responsive to their needs and treating them as individuals.
  • the present invention accomplishes these goals by using a multi-phase closed- loop approach to customer relationship management operations.
  • Five phases are implemented, although in alternative embodiments a different number of phases may be implemented.
  • the five phases track the complete life cycle of a database marketing project, including specification, analysis, design, execution, and tracking.
  • the specification phase includes defining the goal of the marketing project, defining constraints, and defining the customer universe.
  • the analysis phase includes developing and scoring market segments, and validating constraints and goals.
  • the design phase includes creating "cells" (described below), assigning offers to cells, and assigning channels through which to make the offers.
  • the execution phase includes defining output formats, establishing a campaign schedule, and de- Uvering data to channel databases.
  • the tracking phase includes posting the responses to the offers made in this campaign, analyzing this data, and adjusting the campaign accordingly.
  • phases are described in more detail in the Detailed Description of the Preferred Embodiments. In alternative embodiments, the phases may include different sub-phases than those listed here, without departing from the spirit or essential characteristics of the claimed invention.
  • FIG. 1 is a diagram of the overall flow of the method of the present invention.
  • Fig. 2 is a flow diagram of a specification phase according to one embodiment of the present invention.
  • Fig. 3 is a flow diagram of an analysis phase according to one embodiment of the present invention.
  • Fig. 4 is a flow diagram of a design phase according to one embodiment of the present invention.
  • Fig. 5 is a flow diagram of an execution phase according to one embodiment of the present invention.
  • Fig. 6 is a flow diagram of a tracking phase according to one embodiment of the present invention.
  • Fig. 7 is a block diagram of a system architecture according to one embodiment of the present invention.
  • Fig. 8 is a block diagram showing operation of a segmentation module.
  • Fig. 9 is a block diagram showing operation of a scoring module.
  • Fig. 10 is a flowchart showing a method of testing the quality of a selected predictive model.
  • Fig. 11 is a block diagram showing operation of a campaign manager module.
  • the techniques of the present invention can be applied to both product-centric and customer-centric campaigns and programs.
  • product-centric campaigns the user's focus is to identify the customers or households that will be targeted for a particular product or service. Such an approach may be used most commonly, for example, for one-step single-channel campaigns such as direct-mail campaigns.
  • product-centric methodologies may also be used for multi-step and/ or multi-channel campaigns.
  • customer-centric campaigns the user's focus is to identify the most appropriate products to be offered to a particular customer or household, and through what sequence of offers and channels to approach the customer. Such campaigns tend to be more complex than corresponding product-centric campaigns. Though the description provided below presents the present invention in terms of customer-centric marketing campaigns, the techniques may also be applied to software for a product-centric campaign.
  • Fig. 1 there is shown a diagram of the overall flow of a method according to one embodiment of the present invention. This flow is presented as a database marketing project life cycle, having five phases: specification phase 101, analysis phase 102, design phase 103, execution phase 104, and tracking phase 105. Each phase will be described in turn, in terms of a user practicing the present invention. As will be understood by those skilled in the art, the steps of the present invention can be performed by an automated system, such as a software application.
  • the invention evaluates customers and/ or prospective customers in terms of a number of metrics to establish their current positions in a multi-dimensional conceptual space.
  • the customers are also evaluated with respect to their predicted positions in this space at some user-defined time.
  • specification, segmentation and other tasks are performed with respect to positions and potential positions in this space. This measurement and evaluation process allows the user to gauge the relative success or failure of an ongoing campaign and to make adjustments to the campaign plan accordingly.
  • Fig. 2 there is shown a flow diagram of specification phase 101, according to one embodiment of the present invention.
  • Specification phase 101 begins by defining 201 the marketing campaign's overall goal. For example, a goal in a customer-centric campaign might be to increase the profitability of its low-profit but high-potential customers by 20% in eighteen months. This might be achieved through a single campaign or through a series of related campaigns.
  • customers are characterized by their position in a multidimensional space with each axis measuring a particular parameter such a profitability, loyalty, risk level, and the like.
  • the campaign's goal is defined in terms of a position in the multi-dimensional customer space, with each specific goal corresponding to a distinct position in the space, thus defining a path to the desired position.
  • the goal is realized by moving the customers within this space.
  • the goal of the campaign is expressed as the desired value of a target variable. Constraints on achieving the target value may be defined 202, to represent other business goals or restrictions. For example, for the customer-centric campaign discussed above, one constraint might be to focus only on a particular geographic area, and to avoid using loan products due to the company's asset/ liability situation.
  • constraints are represented in the system as restrictions on values of parameters. For example one constraint might be represented as:
  • TOTAL_COMPANY_PROFITS_BEFORE ⁇ TOTAL_COMPANY_PROFITS_AFTER (Eq. 1)
  • This constraint specifies that the profits must not decrease as a result of the marketing campaign. The constraint can be checked both during the analysis and design phases, 102, 103, by comparing the predicted total profits to the initial profits, or during the tracking phase 105 by comparing the actual total profits to the initial profits.
  • Eq. 2 This constraint specifies that the campaign have a positive contribution to company profits. Checking the constraint of Eq. 2 can be accompUshed using domain knowledge, which is embedded in the system so as to faciUtate such constraint evaluation.
  • Step 202 may also include, as an additional constraint, specifying a budget for the campaign, or for a set of related campaigns.
  • a budget takes into account the various costs of executing the campaign, processing the responders, and rnaintaining the responders. Specific examples include mailing costs, credit check costs, monthly statements, and the like.
  • the invention then includes defining 203 the customer universe that will be targeted through the campaign and the products that will be offered in these markets.
  • the campaign might target households that have secured a mortgage to purchase their first residence in the last six months.
  • Particular products, such as gold and platinum versions of a particular credit card, might be offered to such households.
  • Fig. 3 there is shown a flow diagram of analysis phase 102 according to one embodiment of the present invention.
  • the invention analyzes the goal of the campaign and determines its feasibility.
  • Analysis phase 102 verifies that the goal can be met within the constraints set in specification phase 101, and in one embodiment determines what sequences of promotions are to be appUed to each customer to achieve the goal.
  • the system measures and records variables that measure customers, as well as variables that measure campaign parameters such as response rates, budgets, expected and actual expenses, and the like. Variables can be classified into two categories — independent variables, which can be directly controlled, and dependent variables, which are controllable indirectly by modifying directly controlled variables. Generally, target variables are dependent variables.
  • the central problem addressed by the marketing campaign is to find values for independent variables that will meet the campaign goal, while satisfying the defined constraints.
  • the invention employs calculators and reports to generate relevant values for dependent variables.
  • Calculators include mechanisms for performing analysis by performing various calculations on variables, such as the break-even calculator described below.
  • Reports are a mechanism aUowing the user to interactively modify parameters and see the effects on other parameters, in a "what if" form of analysis. Reports display various parameters of interest to the user in a visuaUy meaningful manner, such as a graph, spreadsheet, or chart. The user can modify certain variables and see the results of such changes, in a manner similar to a spreadsheet application. Reports are supported by a set of calculators and models. The calculators are used to compute dependent variables, while the models provide the abiUty to predict values for variables. In one embodiment, such models operate using known statistical methods.
  • Analysis phase 102 begins by performing an exploratory analysis 308. This may include, for example, a break-even analysis to determine the expected breakeven point for the campaign. Based on the cost of contacting each potential customer, and the expected profit from each acquired customer, a target response rate is determined.
  • Table 1 A top-down break-even calculator
  • Table 1 shows a "top-down" calculator, in that it takes as input the various costs and the campaign goal, and calculates the needed budget for the campaign.
  • a bottom-up calculator can be employed, which starts with the campaign budget and determines if the campaign goals can be met. More sophisticated analysis may also be performed, including for example, taking into account attrition figures and other factors. Also, other types of calculators may be employed, as will be apparent to those skilled in the art.
  • Analysis phase 102 continues by defining 301 the segments in the identified market that will be targeted by the campaign. For example, first-year college students may be one segment. Segments may also be defined by other means, such as geographic criteria, income levels, family size, home ownership, and the like. These segments are scored with respect to certain criteria, such as risk, propensity to accept an offer, and the like. Scoring includes determining a score for each segment that measure a particular characteristic, such as the propensity to buy a product, the likelihood of defecting within a year, and the like. For example, one such segment might be defined as "households with no children and low credit risk that have taken a mortgage to purchase their first residence in the last six months.”
  • Segments can be defined in a variety of ways, including for example:
  • Each segment has its own characteristics, such as response rate, channel sensitivity, propensity to purchase different products, and the like. To take into account these characteristics in order to determine Ukely success or failure of the campaign, a correlation model may be used.
  • Correlation models capture empirical relationships among variables, and may be used when direct relationships are not known. Correlation models are built using a variety of techniques, including statistical analysis, neural networks, rule-based systems, and the like. In the present invention, such relationships are captured in a set of predictive models that model common relationships. One example is a predictive model that predicts whether a household or customer wiU accept a particular offer of a particular product, such as a gold credit card. In one embodiment, predictive models are employed to capture correlation models. Based on the cost of contacting each potential customer, and the expected profit from each acquired customer, a target response rate is determined.
  • a dependency network may be used to describe dependency relationships among variables.
  • a dependency network is defined when a formula is used to derive a variable from one or more other variables.
  • the dependency network traces such dependencies, and establishes a precise relationship among variables. Such a relationship can then be used to perform analysis on the campaign and gauge its Ukelihood of success according to defined goals and parameters.
  • dependency networks are captured in the calculators.
  • step 301 also includes defining the number and type of campaigns that will be executed on each segment. This substep may be performed for each defined product of the campaign, or for each established customer or customer segment. For example, the no-children low-risk households mentioned above might be targeted with two campaigns, such as an awareness campaign followed by a solicitation campaign.
  • step 301 also includes determining methods and channels for each campaign. This determination is made based on the break-even analysis and capacity availability of each channel to accommodate the workload imposed by the campaigns. Following the example being discussed, the gold and platinum credit cards might be offered through direct mail and telephone soUcitation.
  • a product path assignment for each segment may be developed as weU.
  • the product path assignment is a sequence of products that wiU be offered successively to the customers in a segment, in an effort to meet the campaign goals.
  • the product path assignment is based on a report that shows, for each customer:
  • Analysis phase 102 also includes validating 304 the constraints and goals of the marketing campaign. This includes, for example, identifying any conflicts between the products offered in the campaign and products offered by any other campaigns already in production, possibly by other parts of the same organization. This step attempts to avoid sending conflicting offers or messages to potential customers. The expected success of the campaign in terms of the defined goal is assessed. If the goal is not satisfied 306, analysis phase 102 returns to step 308. If the goal is satisfied 306, the phase ends 307.
  • analysis phase 102 takes place in accordance with positions and potential positions of customers in the multi-dimensional space described above. Customers are segmented according to their real and potential positions. A path through the multi-dimensional space is determined, that takes the customers from the current position to a position that wiU satisfy the campaign goal. In one embodiment, the path through multi-dimensional space is implemented using a set of predictive models 901. For example, a "propensity to buy" model may be appUed to each customer to determine which product he or she is most Ukely to buy.
  • Additional predictive models may then be appUed to determine where such a purchase will lead the customer in the multi-dimensional space. This determines the first step in the path for each customer.
  • the "propensity to buy” model may be appUed to each customer again, but this time with the assumption that the first product has already been purchased. Repeating the process results in a series of steps on the path that the customer is Ukely to follow. The user can decide that he or she would Uke the customer to foUow a different path, in which case the models provide information that is helpful in designing new products and campaigns that will steer the customer along the desired path.
  • Design phase 103 begins by selecting 404 a market segment and organizing 401 the segment into smaller groups called "cells". The members of a cell receive the same offer. A cell is thus the smallest unit of customers that is the target of a promotion.
  • An example of a ceU is the top five percent (in terms of profitabiUty) of the no-children, low-risk households; another example is a random sample of ten percent of the entire segment. CeUs are specified in terms of filters or queries applied to a segment that may include, for example:
  • ceUs are further refined 405 using a set of business rules.
  • a rule might state "do not promote a customer more than once every three months.”
  • a promotion is then created 406 and associated 402 with each ceU.
  • the cell containing the top five percent of the most profitable households might be offered a platinum credit card with no interest for one year and a $15,000 credit limit.
  • a channel is assigned, through which the offer wiU be made (such as telephone soUcitation, direct mail, and the Uke).
  • the offer wiU be made such as telephone soUcitation, direct mail, and the Uke.
  • the present invention is able to ascertain whether the assigned channel is able to accommodate the task assigned to it. Available channel capacity is measured during the time period the offer wiU be made.
  • Fig. 5 there is shown a flow diagram of execution phase 104 according to one embodiment of the present invention.
  • Output formats are specified 501 as required by each channel.
  • the campaign schedule is then developed 502. FinaUy, the necessary data is delivered 503 to each channel so that the campaign can be put into effect.
  • the format expected for the promotions depends on the channel. For example, a mail center may expect magnetic copies of mail-ready files in the format of their mailing software. A caU center may expect scripts that the telephone representative will follow during conversations with prospective customers. An automated teller machine may expect different messages to be displayed when a customer inserts a bank card. Delivery of data 503 is performed according to the expected format for the channel being used.
  • Tracking phase 105 is performed by collecting 601 in a marketing database data about the results of the campaign; for example, the responses to the offers associated with the appropriate cell.
  • Data is coUected at some predetermined frequency, depending on the organization and on the channel being used. For example, data from call centers and World Wide Web sites may be collected daily, if appropriate, while data for mail channels may be more suited for weekly collection.
  • the campaign's effectiveness is analyzed 602 by cell, channel, and by product/ offer. The campaign is then adjusted 603 based on the analyzed data, for greater effectiveness and/ or efficiency.
  • tracking phase 105 analyzes results in terms of the multidimensional space described earlier. Customer positions are compared with expected positions, to determine whether the campaign is on target, or if the path through the multi-dimensional space needs to be modified. This process is repeated for each step in the path, until the goal is met and the campaign is over. The campaign thus consists of moving the customers through a series of steps to the target position.
  • Fig. 7 there is shown a block diagram of a system architecture 700 according to one embodiment of the present invention, for performing the process described above.
  • the architecture 700 shown in Fig. 7 may be implemented, for example, in a client/ server computing environment.
  • the cUent portion runs under an operating system such as Windows 95, Windows 98, or
  • the server portion runs, for example, under the Windows NT 4.0 operating system, also from Microsoft Corporation.
  • a Unix-based server may be employed, particularly for larger volumes of data.
  • the primary database for use in architecture 700 is customer data mart (CDM)
  • Data model 702 provides a framework for accessing and interpreting data from customer data mart 701.
  • Data model 702 includes base attributes and derived attributes describing customers and potential customers.
  • Data for data model 702 is determined based on the needs of various calculators and reports of segmentation module 704, report analysis and data mining module 705, campaign manager module 706, and predictive model library 901.
  • Data model 702 is designed to provide efficient and convenient access to many different types of information.
  • customer information is stored in a dimensional structure to faciUtate analysis.
  • the campaign and tracking information is stored in relational tables.
  • Derived data attributes are stored in denormaUzed and flattened tables for efficiency, if appropriate.
  • Information in data model 702 is linked using appropriate keys, as needed.
  • Data access layer 703 contains routines for accessing and manipulating data from data model 702 and CDM 701. Those skilled in the art wiU recognize that such routines are generally known for such operations in connection with databases.
  • Modules 704, 705, 706, and 901 may caU routines from layer 703 as needed.
  • Segmentation module 704 performs segmentation operations in connection with the marketing project life cycle, as wiU be explained in more detail below.
  • Report analysis and data mining module 705 determines scores for market segments, and makes the scores available to the user, as will be explained in more detail below.
  • Campaign manager module 706 produces as its output a marketing campaign, as will be explained in more detail below.
  • Segmentation module 704 accepts data from CDM 701 and data model 702 as input.
  • Module 704 produces segments set 801, which contains descriptions of market segments for use in other components of the system. Segments set 801 can be stored in CDM 701, or in file space of a client or server machine.
  • module 704 accepts reports Ubrary 802 as input in generating segments set 801.
  • Reports library 802 aUows easy interaction with data from CDM 701 by faciUtating the use of standardized reports.
  • customer understanding reports 803 which provide an assessment of the type and level of relationship between customers and the company
  • campaign effectiveness reports 804 which provide analysis on the results of previously executed marketing campaigns so that the campaign can be refined and improved.
  • customer understanding reports include:
  • Segmentation module 704 generates segments set 801 by performing two functions: selecting 805 a customer universe, and partitioning 806 the customer universe into segments. Selecting 805 involves defining a group of customers (individuals or households), which may include either the entire CDM 701 or a subset of the CDM 701.
  • the user may specify such a subset through the use of a query, which may include inclusion and/ or exclusion clauses.
  • the subset may be defined in terms of a view in the CDM 701.
  • Partitioning 806 is performed by defining specific criteria for one or more segments in the customer universe.
  • segments are created interactively, in response to the user posing queries against the customer universe selected in 805 through a relational and/ or multidimensional query tool.
  • a segment may be created by posing a relational query requesting all households that own two banking products.
  • Segments may also be organized hierarchically, so that the segment defined as "household that own two banking products” may include additional defined segments such as "households that own two banking products and have a car” and "households that own two banking products and have a house”.
  • Interactive segmentation as performed in 806 may be done in two substeps: quickcounts, and record reaUzation.
  • the quickcounts substep determines the size of each segment, so that the user can elect to reject a segment as being too large or too small before the records associated with the segment are actually retrieved from CDM 701.
  • the user may refine a query for a rejected segment in order to obtain better results, if desired.
  • the segments specified as "accepted" by the user are processed.
  • An identification code is assigned to each accepted segment, and the appropriate segment identification code is written to each record that belongs to the customer universe, as appropriate. Since a particular record may belong to more than one segment, and the user may elect to aUow dupUcate records in segments if desired, all appropriate identification codes must be associated with the corresponding record.
  • segmentation module 704 has performed the partition into segments, the segments are stored, for example at the client machine or in CDM 701. If stored in CDM 701, appropriate security is implemented for each segment so that only designated users may access, copy, and/ or modify the segment.
  • Segmentation module 704 also provides graphic display of the distribution of values of the records found within a particular segment.
  • the user may specify the attributes whose values are to be displayed, and user interface 707 then displays the data in a graphical form.
  • the display may include a bar graph Usting the number or percentage of potential customers belonging to several different groups forming the segment.
  • Other methods of segmentation such as automated segmentation, may also be implemented in alternative embodiments.
  • automated segmentation using predictive models, as described above is employed. The customer universe is segmented according to the first most Ukely product to buy. Each segment is then subdivided according to the next most Ukely product to buy. Continuing in this manner, a set of segments is developed, so that each segment foUows along the same path in the multi-dimensional space.
  • An alternative technique for automated segmentation is based on known statistical clustering algorithms.
  • a clustering algorithm breaks the customer universe into a set of clusters, so that the customers in each cluster are uniform with respect to some criteria, and differ among different clusters according to the same criteria. These clusters then form the segments.
  • Module 705 accepts input from segments set 801 (generated by segmentation module 704, as described above) as well as predictive model Ubrary 901. Output from report analysis and data mining module 705 is scored segments set 902 which may then be stored in CDM 701.
  • Predictive model Ubrary 901 contains a number of predictive models that may be used in scoring segments. Predictive models predict the expected value of some parameter. Such models are built by analyzing data having known values for the target variable, incorporating the information about the correlation between the target variable and other variables. When appUed to new target data, the model can then use the learned correlations to predict values for target variables.
  • library 901 may include the foUowing models:
  • Report analysis and data mining module 705 performs two main functions in generating scored segment set 902. It tests 903 the quaUty of a selected predictive model from library 901, and it applies 904 the selected predictive model to segments set 801.
  • Fig. 10 there is shown a flowchart of a method of testing 903 the quaUty of a selected predictive model.
  • the model is appUed to a set of customers for which the target variable has a known value.
  • the results of the model are then compared against the known value to determine quality of the model.
  • module 705 accepts 1001 the user's selection of a model from Ubrary 901. Generally, the user selects a predictive model based on the type of task (acquisition, attrition, etc.) and the product (credit card, loan, etc.). Module 705 then accepts 1002 the user's selection of a data set with recorded response data. Next, module 705 accepts 1003 user selection of the attributes of the data set that will be pertinent to the model testing. Alternatively, all attributes can be selected as being pertinent. If any attributes have special meaning, the user can identify 1004 these. Module 705 then checks 1005 that the selected data and attributes conform to the model's specification. This includes, for example, checking that the names of the independent variables in the data set are the same as the corresponding attributes of the selected model, checking that the dependent variable is the same, and the Uke.
  • module 705 appUes 1006 the model to the selected data set and compares 1007 the model's predictions to the known responses. In one embodiment, these results are plotted graphically, such as through a gains chart. FinaUy, the system accepts 1008 the user's acceptance or rejection of the model based on the comparison. The procedure of Fig. 10 may be repeated for each of the selected models. Once the selected model is tested in 903, it can be applied 904 to segments set 801, according to known techniques in the art of predictive model appUcation. A set of scores from the model is generated. The scores are written as separate values in each record in the selected segment, and stored in CDM 701 as appropriate.
  • Module 706 is used in design phase 103 and execution phase 104 of the marketing life cycle.
  • Module 706 accepts as input either segments set 801 from segmentation module 704, or scored segments set 902 from report analysis and data mining module 705.
  • Module 706 produces a marketing campaign 1101 including ceUs 1103, offers 1104, channels 1105, and schedules 1106. Marketing campaign 1101 may also be related to or integrated with other marketing campaigns, as desired.
  • Marketing campaign 1101 is identified by a unique identifier.
  • Typical data that may be associated with campaign 1101 may include, for example: • the campaign owner
  • the campaign type e.g. renewal, acquisition, cross-sell, and the Uke
  • Cells 1103 are created, as described above, by applying various filtering criteria to segments.
  • the user may specify that module 706 should store in a temporary data source (such as a flat file) the number of customers that "faU off" a campaign because of the application of filters to a particular segment or ceU. For example, if a segment contains 1000 customers, and a filter causes 200 customers to be left out of the campaign, these 200 names could be saved along with an identification of the filter that caused their removal, so that the user can retrieve this information if needed.
  • a temporary data source such as a flat file
  • Cells may include control cells (a random sample of a segment, forming a control group that can be used to evaluate effectiveness of selection techniques, test ceUs (included in the campaign but do not receive the offer), and generic ceUs (members receive the offers through the prescribed channels.
  • Module 706 is also able to identify the percent of dupUcates among a set of selected cells, so that the user can be notified of such dupUcates.
  • DupUcate records among a set of previously selected cells may be displayed for the user if desired. Also, the user may specify that duplicates be removed from selected cells, or that a subset of such duplicates be removed.
  • Campaign manager module 706 maintains a link between cells 1103 and the parent segment from which the ceUs were derived.
  • a database table is developed and stored, which contains descriptions of the unique attributes of each cell 1103. Such attributes include, for example: • a description of the customer group being targeted in the ceU
  • these attributes are stored in the same table as the segment data itself.
  • CeUs 1103 portion of marketing campaign 1101 shows the user the hierarchical organization of ceUs 1103, where appropriate. Offers 1104 associated with a cell are also displayed. The channels 1105 through which offers 1104 will be made are displayed. For each offer 1104, there is specified a list of acceptable actions the recipient of the offer may take. These include, for example, accepting the offer, requesting additional information, declining the offer, requesting removal from the Ust, and the like. Depending on the action taken, the recipient of an offer may or may not be included in subsequent campaigns.
  • Campaign manager module 706 also analyzes the response to offers 1104 in terms of success criteria, in order to derive a determination as to the overall success of the marketing campaign.
  • Offers 1104 that are associated with cells 1103 may be mutually exclusive in some cases (though not always). The user can specify such mutual exclusivity where appropriate, so that module 706 can take such factors into account when generating campaign 1101.
  • Offers 1104 output by module 706 also include the script to be used in making the offer. For example, in a direct mail channel the script is the text of the letter to be sent to the prospective customer. In a telephone solicitation channel, the script is the text that the customer support representative will read in communicating with the prospective customer.
  • the offers 1104 output may point to a script that is stored in some format, such as a Microsoft Word file for example.
  • Channels 1105 are specified for the offers 1104 being made. These may include, for example, direct mail, telemarketing, fulfiUment, newspaper ads, and the Uke.
  • various treatments are specified depending on the nature of the channel. These treatments describe distinct elements and specifications for executing the offer through the corresponding channel. Table 2 shows treatments for several examples of channels.
  • the user of the system can select from a master list of actions, as appropriate to the particular response of the customer. Such responses include, for example: “not interested”, “contact again in three months”, “accept”, “contact using another channel”, and the Uke.
  • the user can define what actions should be taken. These can include, for example: making a different offer (perhaps increasing the incentive), scheduling for re-sending of the offer at a later time, changing the channel, and the Uke.
  • the system aUows the user to specify the action for each expected customer response.
  • Campaign 1101 also contains a schedule 1106 describing when each offer 1104 will be made to members of a particular cell 1103.
  • Schedule 1106 also identifies and displays to the user other campaigns that are running at the same time as the campaign being created.
  • Output can be generated, for example, by product, department, cell, segment, household, account, and/ or customer. Such reports can be generated from available data by conventional report generation means.
  • Marketing campaign 1101 is provided as output 1107 to the user, to be displayed, printed, transmitted, and/ or stored in a conventional manner.
  • output 1107 takes the form of data files for supporting the back end of a direct marketing process.
  • Output 1107 may include, for example, formats or templates for external direct mail production houses and caU centers. These templates facilitate population of data fields, calculated values, tracking codes, and test fields.
  • the templates might include form letters, where the customer name and address will be fined in for each customer before printing.
  • the templates might be in the form of scripts to be foUowed by a telephone representative, as weU as guidelines for handling interactions not covered in the script.
  • freeze files capture record identifiers and selected data elements at the time the campaign 1101, segment, and cell were created to faciUtate more accurate response analysis and modeling
  • longitudinal marketing files identify permanent segments and marking records in the database for extended periods of time
  • Household_key General Attributes Branch .key Age band I type Branch. .address Childi cii I . income Branch. .city Inco e . band I . hea _name Branch name Marital . status I address Branch. . state Sex I . city Month_key Branch state Race I . state Account_key Branch_SCF Occupation l_zip Product.key
  • Cross sell report for account, household, region, and branch
  • Campaign/offer by customer by month, by quarter, by year
  • Campaign/offer by household by month, by quarter, by year
  • Household_profitability Household_revenue- ⁇ (HH_acquisition_cost+HH_service_cost+HH_retention_cost) over all campaigns
  • Customer_profitability Customer_revenue- ⁇ (Customer_acquisition_cost+Customer_service_cost+Customer_retention_cost) over all campaigns

Abstract

A customer relationship management system and method provides and implements a multi-phase closed-loop approach, including specifying, analyzing, designing, executing, and tracking operations associated with a marketing campaign. Marketing campaigns are developed based on defined constraints, and subdivided according to segments and cells. Full support for distinct marketing channels, each having unique characteristics, is provided.

Description

CUSTOMER RELATIONSHIP MANAGEMENT SYSTEM AND METHOD
Inventors: Evangelos Simoudis Mayank Prakash
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates generally to software for marketing applications, and more particularly to a system and method of planning and implementing customer relationship management.
2. Description of Background Art
Historically, marketing techniques relied on individual contact between the proprietor and each customer of a business. Because of the personal relationship that was able to develop in such an environment, the proprietor knew all of his or her customers and could provide personal service to them. For example, in a banking environment, where the proprietor was a banker, he or she would keep track of the needs and means of each customer, how much credit risk each customer represented, how to maintain customer satisfaction in a individualized way, and other characteristics. Large-scale customer service operations and stores no longer have the luxury of such individual contact. Stores, banks, and other such establishments are able to maintain customer satisfaction, to some extent, by attempting to classify customers into groups, and treating all customers in each group alike. This segmentation is applied to the services and products offered to customers, as well as to the marketing efforts employed to attract and maintain customers. Much of this marketing is done through mass media, which results in a loss of the ability to distinguish among individual customers and potential customers. Another common marketing technique is direct mail, wherein an offer is mailed to a large number of prospective customers. Direct mail marketing techniques suffer from many of the same problems listed above. In addition, these large-scale techniques tend to diminish the capability of building long-term relationships, instead treating each transaction as a separate, isolated event.
As marketing tactics and techniques become more complex, the tasks of mamtaining, tracking, and developing customer relationships become more difficult. Marketeers have relied upon a combination of tools, including databases, expert systems, decision support software, and the like, to perform customer relationship management functions. In many cases, these diverse tools and techniques do not operate in an integrated manner; as a result, inefficiencies are introduced and customer relationships suffer. Marketing resources are not used efficiently. Customers may receive conflicting and/ or contradictory messages from the same organization, the wrong market segments may be targeted, potential customers may be missed, or existing customers may be driven off through error or mistreatment. Such problems and pitfalls are often caused by the failure of existing tools to provide a systematic, closed-loop customer relationship management system. Several tools are available for decision support and customer tracking, such as Valex from Exchange Applications, Analytix from Experian's Customer Insight Group, P/CIS from Harte Hanks, and Pinnacle from Harland, among others. In addition, other database-driven tracking systems are available for perf orming various types of analysis and breakdowns of customer data; these include, for example Modell from Unica, SAS Enterprise Miner, SPSS, and the like. Limited campaign management support is available from products such as Vales from Exchange Applications, Prime Vantage from Prime Response, and the like. None of these systems, however, provides an integrated approach that can use the data to design a customer relationship management plan, and execute and track the results of the plan. What is needed is a system that can manage the complete life cycle of a customer relationship management project. What is further needed is a system that provides software tools for use in all phases of customer relationship management in an integrated manner. What is further needed is a software system and method that can maximize results in customer relationship management by performing specification, analysis, design, execution, and tracking functionality for the entire life cycle. What is further needed is a software system and method capable of integrating analytics such as data mining, predictive modeling, statistical and OLAP processing, neural networks, and the like, into a single framework designed to support the life cy- die of customer relationship management.
SUMMARY OF THE INVENTION In accordance with the present invention, there is provided a system and method of planning and implementing customer relationship management that address the above limitations of the prior art. The present invention thus enables businesses to employ economies of scale of larger operations and institutions, so as to reduce costs, while retaining the ability to tailor product and service offerings, as well as marketing efforts, to individual customers. The invention thus promotes the building of long-term relationships with customers, rather than treating each interaction as a stand-alone transaction. This is beneficial for both the business establishment and its customers: customer loyalty is enhanced, which reduces customer development costs; improved targeting of marketing campaigns reduces customer acquisition costs; and customers feel that the business is becoming more responsive to their needs and treating them as individuals.
The present invention accomplishes these goals by using a multi-phase closed- loop approach to customer relationship management operations. Five phases are implemented, although in alternative embodiments a different number of phases may be implemented. In general, the five phases track the complete life cycle of a database marketing project, including specification, analysis, design, execution, and tracking. The specification phase includes defining the goal of the marketing project, defining constraints, and defining the customer universe. The analysis phase includes developing and scoring market segments, and validating constraints and goals. The design phase includes creating "cells" (described below), assigning offers to cells, and assigning channels through which to make the offers. The execution phase includes defining output formats, establishing a campaign schedule, and de- Uvering data to channel databases. Finally, the tracking phase includes posting the responses to the offers made in this campaign, analyzing this data, and adjusting the campaign accordingly.
Each of these phases is described in more detail in the Detailed Description of the Preferred Embodiments. In alternative embodiments, the phases may include different sub-phases than those listed here, without departing from the spirit or essential characteristics of the claimed invention.
BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a diagram of the overall flow of the method of the present invention. Fig. 2 is a flow diagram of a specification phase according to one embodiment of the present invention.
Fig. 3 is a flow diagram of an analysis phase according to one embodiment of the present invention.
Fig. 4 is a flow diagram of a design phase according to one embodiment of the present invention.
Fig. 5 is a flow diagram of an execution phase according to one embodiment of the present invention.
Fig. 6 is a flow diagram of a tracking phase according to one embodiment of the present invention. Fig. 7 is a block diagram of a system architecture according to one embodiment of the present invention.
Fig. 8 is a block diagram showing operation of a segmentation module. Fig. 9 is a block diagram showing operation of a scoring module. Fig. 10 is a flowchart showing a method of testing the quality of a selected predictive model.
Fig. 11 is a block diagram showing operation of a campaign manager module.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The techniques of the present invention can be applied to both product-centric and customer-centric campaigns and programs. In product-centric campaigns, the user's focus is to identify the customers or households that will be targeted for a particular product or service. Such an approach may be used most commonly, for example, for one-step single-channel campaigns such as direct-mail campaigns. In some cases, product-centric methodologies may also be used for multi-step and/ or multi-channel campaigns.
In customer-centric campaigns, the user's focus is to identify the most appropriate products to be offered to a particular customer or household, and through what sequence of offers and channels to approach the customer. Such campaigns tend to be more complex than corresponding product-centric campaigns. Though the description provided below presents the present invention in terms of customer-centric marketing campaigns, the techniques may also be applied to software for a product-centric campaign.
Referring now to Fig. 1, there is shown a diagram of the overall flow of a method according to one embodiment of the present invention. This flow is presented as a database marketing project life cycle, having five phases: specification phase 101, analysis phase 102, design phase 103, execution phase 104, and tracking phase 105. Each phase will be described in turn, in terms of a user practicing the present invention. As will be understood by those skilled in the art, the steps of the present invention can be performed by an automated system, such as a software application.
In one embodiment of the present invention, in the course of performing the phases depicted in Fig. 1, the invention evaluates customers and/ or prospective customers in terms of a number of metrics to establish their current positions in a multi-dimensional conceptual space. The customers are also evaluated with respect to their predicted positions in this space at some user-defined time. As will be seen below, specification, segmentation and other tasks are performed with respect to positions and potential positions in this space. This measurement and evaluation process allows the user to gauge the relative success or failure of an ongoing campaign and to make adjustments to the campaign plan accordingly. Referring now to Fig. 2, there is shown a flow diagram of specification phase 101, according to one embodiment of the present invention. Specification phase 101 begins by defining 201 the marketing campaign's overall goal. For example, a goal in a customer-centric campaign might be to increase the profitability of its low-profit but high-potential customers by 20% in eighteen months. This might be achieved through a single campaign or through a series of related campaigns.
If applicable, customers are characterized by their position in a multidimensional space with each axis measuring a particular parameter such a profitability, loyalty, risk level, and the like. The campaign's goal is defined in terms of a position in the multi-dimensional customer space, with each specific goal corresponding to a distinct position in the space, thus defining a path to the desired position. The goal is realized by moving the customers within this space.
The goal of the campaign is expressed as the desired value of a target variable. Constraints on achieving the target value may be defined 202, to represent other business goals or restrictions. For example, for the customer-centric campaign discussed above, one constraint might be to focus only on a particular geographic area, and to avoid using loan products due to the company's asset/ liability situation.
For a product-centric campaign, for example, if the campaign goal is to add new credit card customers, one strategy might be to offer no-interest credit cards. Although such a strategy would achieve the immediate goal of adding new credit customers, it would defeat the overall purpose of increasing profits, since revenue from interest payments would be eliminated. A constraint such as "do not reduce total company profits in the campaign" could be put into effect to eliminate the strategy. In one embodiment, constraints are represented in the system as restrictions on values of parameters. For example one constraint might be represented as:
TOTAL_COMPANY_PROFITS_BEFORE <= TOTAL_COMPANY_PROFITS_AFTER (Eq. 1) This constraint specifies that the profits must not decrease as a result of the marketing campaign. The constraint can be checked both during the analysis and design phases, 102, 103, by comparing the predicted total profits to the initial profits, or during the tracking phase 105 by comparing the actual total profits to the initial profits.
Another example of a constraint might be: CAMPAIGN_CONTRIBUTION_TO_COMPANY_PROFITS >= 0
(Eq. 2) This constraint specifies that the campaign have a positive contribution to company profits. Checking the constraint of Eq. 2 can be accompUshed using domain knowledge, which is embedded in the system so as to faciUtate such constraint evaluation.
Step 202 may also include, as an additional constraint, specifying a budget for the campaign, or for a set of related campaigns. Such a budget takes into account the various costs of executing the campaign, processing the responders, and rnaintaining the responders. Specific examples include mailing costs, credit check costs, monthly statements, and the like.
The invention then includes defining 203 the customer universe that will be targeted through the campaign and the products that will be offered in these markets. For example, the campaign might target households that have secured a mortgage to purchase their first residence in the last six months. Particular products, such as gold and platinum versions of a particular credit card, might be offered to such households.
Referring now to Fig. 3, there is shown a flow diagram of analysis phase 102 according to one embodiment of the present invention. In this phase, the invention analyzes the goal of the campaign and determines its feasibility. Analysis phase 102 verifies that the goal can be met within the constraints set in specification phase 101, and in one embodiment determines what sequences of promotions are to be appUed to each customer to achieve the goal. The system measures and records variables that measure customers, as well as variables that measure campaign parameters such as response rates, budgets, expected and actual expenses, and the like. Variables can be classified into two categories — independent variables, which can be directly controlled, and dependent variables, which are controllable indirectly by modifying directly controlled variables. Generally, target variables are dependent variables.
The central problem addressed by the marketing campaign is to find values for independent variables that will meet the campaign goal, while satisfying the defined constraints. In performing this function, the invention employs calculators and reports to generate relevant values for dependent variables. Calculators include mechanisms for performing analysis by performing various calculations on variables, such as the break-even calculator described below.
Reports are a mechanism aUowing the user to interactively modify parameters and see the effects on other parameters, in a "what if" form of analysis. Reports display various parameters of interest to the user in a visuaUy meaningful manner, such as a graph, spreadsheet, or chart. The user can modify certain variables and see the results of such changes, in a manner similar to a spreadsheet application. Reports are supported by a set of calculators and models. The calculators are used to compute dependent variables, while the models provide the abiUty to predict values for variables. In one embodiment, such models operate using known statistical methods.
Analysis phase 102 begins by performing an exploratory analysis 308. This may include, for example, a break-even analysis to determine the expected breakeven point for the campaign. Based on the cost of contacting each potential customer, and the expected profit from each acquired customer, a target response rate is determined.
One example of a break-even calculator is shown in Table 1:
Figure imgf000011_0001
Table 1: A top-down break-even calculator
Table 1 shows a "top-down" calculator, in that it takes as input the various costs and the campaign goal, and calculates the needed budget for the campaign. Alternatively, a bottom-up calculator can be employed, which starts with the campaign budget and determines if the campaign goals can be met. More sophisticated analysis may also be performed, including for example, taking into account attrition figures and other factors. Also, other types of calculators may be employed, as will be apparent to those skilled in the art.
Analysis phase 102 continues by defining 301 the segments in the identified market that will be targeted by the campaign. For example, first-year college students may be one segment. Segments may also be defined by other means, such as geographic criteria, income levels, family size, home ownership, and the like. These segments are scored with respect to certain criteria, such as risk, propensity to accept an offer, and the like. Scoring includes determining a score for each segment that measure a particular characteristic, such as the propensity to buy a product, the likelihood of defecting within a year, and the like. For example, one such segment might be defined as "households with no children and low credit risk that have taken a mortgage to purchase their first residence in the last six months."
Segments can be defined in a variety of ways, including for example:
• Applying a query to the customer universe, or another segment
• Combining two or more segments
• Selecting a quartile or a decal based on a score • Randomly or manually removing customers from a segment
• Removing customers that are duplicated in another segment, or in another campaign
Each segment has its own characteristics, such as response rate, channel sensitivity, propensity to purchase different products, and the like. To take into account these characteristics in order to determine Ukely success or failure of the campaign, a correlation model may be used.
Correlation models capture empirical relationships among variables, and may be used when direct relationships are not known. Correlation models are built using a variety of techniques, including statistical analysis, neural networks, rule-based systems, and the like. In the present invention, such relationships are captured in a set of predictive models that model common relationships. One example is a predictive model that predicts whether a household or customer wiU accept a particular offer of a particular product, such as a gold credit card. In one embodiment, predictive models are employed to capture correlation models. Based on the cost of contacting each potential customer, and the expected profit from each acquired customer, a target response rate is determined.
In an alternative embodiment, a dependency network may be used to describe dependency relationships among variables. A dependency network is defined when a formula is used to derive a variable from one or more other variables. The dependency network traces such dependencies, and establishes a precise relationship among variables. Such a relationship can then be used to perform analysis on the campaign and gauge its Ukelihood of success according to defined goals and parameters. In one embodiment, dependency networks are captured in the calculators.
In one embodiment, step 301 also includes defining the number and type of campaigns that will be executed on each segment. This substep may be performed for each defined product of the campaign, or for each established customer or customer segment. For example, the no-children low-risk households mentioned above might be targeted with two campaigns, such as an awareness campaign followed by a solicitation campaign.
In one embodiment, step 301 also includes determining methods and channels for each campaign. This determination is made based on the break-even analysis and capacity availability of each channel to accommodate the workload imposed by the campaigns. Following the example being discussed, the gold and platinum credit cards might be offered through direct mail and telephone soUcitation.
In one embodiment, a product path assignment for each segment may be developed as weU. The product path assignment is a sequence of products that wiU be offered successively to the customers in a segment, in an effort to meet the campaign goals. The product path assignment is based on a report that shows, for each customer:
• the products currently owned
• the profitabiUty from those products
• the propensity of the customer to buy each additional product • the expected profitabiUty from each of those products
From this report, the user can determine the optimal sequence of products to offer the customers in each segment, in order to maximize the contribution to the campaign goals, while meeting relevant constraints. Analysis phase 102 also includes validating 304 the constraints and goals of the marketing campaign. This includes, for example, identifying any conflicts between the products offered in the campaign and products offered by any other campaigns already in production, possibly by other parts of the same organization. This step attempts to avoid sending conflicting offers or messages to potential customers. The expected success of the campaign in terms of the defined goal is assessed. If the goal is not satisfied 306, analysis phase 102 returns to step 308. If the goal is satisfied 306, the phase ends 307.
In one embodiment, analysis phase 102 takes place in accordance with positions and potential positions of customers in the multi-dimensional space described above. Customers are segmented according to their real and potential positions. A path through the multi-dimensional space is determined, that takes the customers from the current position to a position that wiU satisfy the campaign goal. In one embodiment, the path through multi-dimensional space is implemented using a set of predictive models 901. For example, a "propensity to buy" model may be appUed to each customer to determine which product he or she is most Ukely to buy. Additional predictive models (such as a "profitability model", a "risk analysis model", a "loyalty index model", and the Uke) may then be appUed to determine where such a purchase will lead the customer in the multi-dimensional space. This determines the first step in the path for each customer. Next, the "propensity to buy" model may be appUed to each customer again, but this time with the assumption that the first product has already been purchased. Repeating the process results in a series of steps on the path that the customer is Ukely to follow. The user can decide that he or she would Uke the customer to foUow a different path, in which case the models provide information that is helpful in designing new products and campaigns that will steer the customer along the desired path.
Referring now to Fig. 4, there is shown a flow diagram of design phase 103 according to one embodiment of the present invention. Design phase 103 begins by selecting 404 a market segment and organizing 401 the segment into smaller groups called "cells". The members of a cell receive the same offer. A cell is thus the smallest unit of customers that is the target of a promotion. An example of a ceU is the top five percent (in terms of profitabiUty) of the no-children, low-risk households; another example is a random sample of ten percent of the entire segment. CeUs are specified in terms of filters or queries applied to a segment that may include, for example:
• sampling (nth, random, or stratified)
• fixed quantity, percent (quantises, deciles, etc.), nth factor
• ranking based on scores
• duplication (members of a selected cell that also belong to other ceUs) In one embodiment, ceUs are further refined 405 using a set of business rules.
For example, a rule might state "do not promote a customer more than once every three months."
A promotion is then created 406 and associated 402 with each ceU. For example, the cell containing the top five percent of the most profitable households might be offered a platinum credit card with no interest for one year and a $15,000 credit limit. A channel is assigned, through which the offer wiU be made (such as telephone soUcitation, direct mail, and the Uke). By taking into account the size of the ceU, and the capacity of the assigned channel, the present invention is able to ascertain whether the assigned channel is able to accommodate the task assigned to it. Available channel capacity is measured during the time period the offer wiU be made. Referring now to Fig. 5, there is shown a flow diagram of execution phase 104 according to one embodiment of the present invention. Output formats are specified 501 as required by each channel. The campaign schedule is then developed 502. FinaUy, the necessary data is delivered 503 to each channel so that the campaign can be put into effect.
The format expected for the promotions depends on the channel. For example, a mail center may expect magnetic copies of mail-ready files in the format of their mailing software. A caU center may expect scripts that the telephone representative will follow during conversations with prospective customers. An automated teller machine may expect different messages to be displayed when a customer inserts a bank card. Delivery of data 503 is performed according to the expected format for the channel being used.
Referring now to Fig. 6, there is shown a flow diagram of tracking phase 105 according to one embodiment of the present invention. Tracking phase 105 is performed by collecting 601 in a marketing database data about the results of the campaign; for example, the responses to the offers associated with the appropriate cell. Data is coUected at some predetermined frequency, depending on the organization and on the channel being used. For example, data from call centers and World Wide Web sites may be collected daily, if appropriate, while data for mail channels may be more suited for weekly collection. Based on the collected data, the campaign's effectiveness is analyzed 602 by cell, channel, and by product/ offer. The campaign is then adjusted 603 based on the analyzed data, for greater effectiveness and/ or efficiency. In one embodiment, tracking phase 105 analyzes results in terms of the multidimensional space described earlier. Customer positions are compared with expected positions, to determine whether the campaign is on target, or if the path through the multi-dimensional space needs to be modified. This process is repeated for each step in the path, until the goal is met and the campaign is over. The campaign thus consists of moving the customers through a series of steps to the target position. Referring now to Fig. 7, there is shown a block diagram of a system architecture 700 according to one embodiment of the present invention, for performing the process described above. The architecture 700 shown in Fig. 7 may be implemented, for example, in a client/ server computing environment. The cUent portion runs under an operating system such as Windows 95, Windows 98, or
Windows NT Workstation, aU from Microsoft Corporation. The server portion runs, for example, under the Windows NT 4.0 operating system, also from Microsoft Corporation. Alternatively, a Unix-based server may be employed, particularly for larger volumes of data. The primary database for use in architecture 700 is customer data mart (CDM)
701, which may be implemented for example using SQL Server 6.5, Oracle 8.X for NT, or similar. A sharable meta-data layer may be implemented to permit all components to share data effectively, as is known in the art. Data model 702 provides a framework for accessing and interpreting data from customer data mart 701. Data model 702 includes base attributes and derived attributes describing customers and potential customers. An example of data model
702, including base attributes and derived attributes, can be found in Appendix A. Data for data model 702 is determined based on the needs of various calculators and reports of segmentation module 704, report analysis and data mining module 705, campaign manager module 706, and predictive model library 901.
Data model 702 is designed to provide efficient and convenient access to many different types of information. In one embodiment, customer information is stored in a dimensional structure to faciUtate analysis. The campaign and tracking information is stored in relational tables. Derived data attributes are stored in denormaUzed and flattened tables for efficiency, if appropriate. Information in data model 702 is linked using appropriate keys, as needed.
Data access layer 703 contains routines for accessing and manipulating data from data model 702 and CDM 701. Those skilled in the art wiU recognize that such routines are generally known for such operations in connection with databases.
Modules 704, 705, 706, and 901 may caU routines from layer 703 as needed.
Segmentation module 704 performs segmentation operations in connection with the marketing project life cycle, as wiU be explained in more detail below.
Report analysis and data mining module 705 determines scores for market segments, and makes the scores available to the user, as will be explained in more detail below.
Campaign manager module 706 produces as its output a marketing campaign, as will be explained in more detail below.
Graphical user interface 707 provides a front-end on the cUent computer for faciUtating user access to the various functional modules of the overall system. Referring now to Fig. 8, there is shown a block diagram depicting operation of segmentation module 704 according to one embodiment. Segmentation module 704 accepts data from CDM 701 and data model 702 as input. Module 704 produces segments set 801, which contains descriptions of market segments for use in other components of the system. Segments set 801 can be stored in CDM 701, or in file space of a client or server machine.
In one embodiment, module 704 accepts reports Ubrary 802 as input in generating segments set 801. Reports library 802 aUows easy interaction with data from CDM 701 by faciUtating the use of standardized reports. For example, two types of reports may be included in library 802: customer understanding reports 803, which provide an assessment of the type and level of relationship between customers and the company; and campaign effectiveness reports 804, which provide analysis on the results of previously executed marketing campaigns so that the campaign can be refined and improved. Examples of customer understanding reports include:
• Marketing Campaign Management Status / Tracking Report
• Cross-SeU Status Summary Report
• Product Combinations Status Report
• Household Detail Status Report • Officer Assignment Status Report
• PortfoUo Segment Migration Report
• PortfoUo Household Detail Changes Report
• Cross-Sell Sequences Report
• Organizational Levels Performance Status Reports • Product Status Comparisons Reports
• Organizational Levels Performance Trends Reports
• PortfoUo Entities Performance Trends Reports Examples of campaign effectiveness reports include:
• Campaign-Specific Incentives Effectiveness Report Campaign-Specific Customer Trends Report Up-Sell Campaigns Effectiveness Report Transaction Channels Status Report Transaction Channels Trends Report Transaction Activity Status Report Organizational Levels Performance Deviations Reports Cross-Sell and Product Bundling Performance Deviations Report Transaction Channels Status by Organizational Levels Reports All of these reports are generated by performing calculations on data from CDM 701. Segmentation module 704 generates segments set 801 by performing two functions: selecting 805 a customer universe, and partitioning 806 the customer universe into segments. Selecting 805 involves defining a group of customers (individuals or households), which may include either the entire CDM 701 or a subset of the CDM 701. The user may specify such a subset through the use of a query, which may include inclusion and/ or exclusion clauses. Alternatively, the subset may be defined in terms of a view in the CDM 701. Partitioning 806 is performed by defining specific criteria for one or more segments in the customer universe.
In one embodiment, segments are created interactively, in response to the user posing queries against the customer universe selected in 805 through a relational and/ or multidimensional query tool. For example, a segment may be created by posing a relational query requesting all households that own two banking products. Segments may also be organized hierarchically, so that the segment defined as "household that own two banking products" may include additional defined segments such as "households that own two banking products and have a car" and "households that own two banking products and have a house".
Interactive segmentation as performed in 806 may be done in two substeps: quickcounts, and record reaUzation. The quickcounts substep determines the size of each segment, so that the user can elect to reject a segment as being too large or too small before the records associated with the segment are actually retrieved from CDM 701. The user may refine a query for a rejected segment in order to obtain better results, if desired.
During the record realization substep, the segments specified as "accepted" by the user are processed. An identification code is assigned to each accepted segment, and the appropriate segment identification code is written to each record that belongs to the customer universe, as appropriate. Since a particular record may belong to more than one segment, and the user may elect to aUow dupUcate records in segments if desired, all appropriate identification codes must be associated with the corresponding record. Once segmentation module 704 has performed the partition into segments, the segments are stored, for example at the client machine or in CDM 701. If stored in CDM 701, appropriate security is implemented for each segment so that only designated users may access, copy, and/ or modify the segment.
Segmentation module 704 also provides graphic display of the distribution of values of the records found within a particular segment. The user may specify the attributes whose values are to be displayed, and user interface 707 then displays the data in a graphical form. For example, the display may include a bar graph Usting the number or percentage of potential customers belonging to several different groups forming the segment. Other methods of segmentation, such as automated segmentation, may also be implemented in alternative embodiments. In one such embodiment, automated segmentation using predictive models, as described above, is employed. The customer universe is segmented according to the first most Ukely product to buy. Each segment is then subdivided according to the next most Ukely product to buy. Continuing in this manner, a set of segments is developed, so that each segment foUows along the same path in the multi-dimensional space.
An alternative technique for automated segmentation is based on known statistical clustering algorithms. A clustering algorithm breaks the customer universe into a set of clusters, so that the customers in each cluster are uniform with respect to some criteria, and differ among different clusters according to the same criteria. These clusters then form the segments.
Referring now to Fig. 9, there is shown a block diagram depicting operation of report analysis and data mining module 705 according to one embodiment. Module 705 accepts input from segments set 801 (generated by segmentation module 704, as described above) as well as predictive model Ubrary 901. Output from report analysis and data mining module 705 is scored segments set 902 which may then be stored in CDM 701.
Predictive model Ubrary 901 contains a number of predictive models that may be used in scoring segments. Predictive models predict the expected value of some parameter. Such models are built by analyzing data having known values for the target variable, incorporating the information about the correlation between the target variable and other variables. When appUed to new target data, the model can then use the learned correlations to predict values for target variables. For example, library 901 may include the foUowing models:
• "Potential life-time value": Predicts net present value of a customer if he or she fuUy utilized all appropriate products and services
• "Propensity to buy product": Predicts likelihood that a customer wiU buy a product • "Risk level": Predicts amount of risk associated with a customer
• "Loyalty index": Predicts likelihood that a customer will defect
• "Predicted response": Predicts likely response of a customer to a given offer
Report analysis and data mining module 705 performs two main functions in generating scored segment set 902. It tests 903 the quaUty of a selected predictive model from library 901, and it applies 904 the selected predictive model to segments set 801.
Referring now to Fig. 10, there is shown a flowchart of a method of testing 903 the quaUty of a selected predictive model. The model is appUed to a set of customers for which the target variable has a known value. The results of the model are then compared against the known value to determine quality of the model.
First, module 705 accepts 1001 the user's selection of a model from Ubrary 901. Generally, the user selects a predictive model based on the type of task (acquisition, attrition, etc.) and the product (credit card, loan, etc.). Module 705 then accepts 1002 the user's selection of a data set with recorded response data. Next, module 705 accepts 1003 user selection of the attributes of the data set that will be pertinent to the model testing. Alternatively, all attributes can be selected as being pertinent. If any attributes have special meaning, the user can identify 1004 these. Module 705 then checks 1005 that the selected data and attributes conform to the model's specification. This includes, for example, checking that the names of the independent variables in the data set are the same as the corresponding attributes of the selected model, checking that the dependent variable is the same, and the Uke.
Next, module 705 appUes 1006 the model to the selected data set and compares 1007 the model's predictions to the known responses. In one embodiment, these results are plotted graphically, such as through a gains chart. FinaUy, the system accepts 1008 the user's acceptance or rejection of the model based on the comparison. The procedure of Fig. 10 may be repeated for each of the selected models. Once the selected model is tested in 903, it can be applied 904 to segments set 801, according to known techniques in the art of predictive model appUcation. A set of scores from the model is generated. The scores are written as separate values in each record in the selected segment, and stored in CDM 701 as appropriate.
Referring now to Fig. 11, there is shown a block diagram depicting operation of campaign manager module 706 according to one embodiment. Module 706 is used in design phase 103 and execution phase 104 of the marketing life cycle. Module 706 accepts as input either segments set 801 from segmentation module 704, or scored segments set 902 from report analysis and data mining module 705. Module 706 produces a marketing campaign 1101 including ceUs 1103, offers 1104, channels 1105, and schedules 1106. Marketing campaign 1101 may also be related to or integrated with other marketing campaigns, as desired.
Marketing campaign 1101 is identified by a unique identifier. Typical data that may be associated with campaign 1101 may include, for example: • the campaign owner
• the campaign type (e.g. renewal, acquisition, cross-sell, and the Uke)
• customer universe size
• campaign budget
• actual expenses • forecast response
• actual response
• number of segments in campaign
• number of cells in campaign
• campaign duration • channels used.
Each of the elements of marketing campaign 1101 wiU now be described. Cells 1103 are created, as described above, by applying various filtering criteria to segments. In one embodiment, the user may specify that module 706 should store in a temporary data source (such as a flat file) the number of customers that "faU off" a campaign because of the application of filters to a particular segment or ceU. For example, if a segment contains 1000 customers, and a filter causes 200 customers to be left out of the campaign, these 200 names could be saved along with an identification of the filter that caused their removal, so that the user can retrieve this information if needed. Cells may include control cells (a random sample of a segment, forming a control group that can be used to evaluate effectiveness of selection techniques, test ceUs (included in the campaign but do not receive the offer), and generic ceUs (members receive the offers through the prescribed channels. Module 706 is also able to identify the percent of dupUcates among a set of selected cells, so that the user can be notified of such dupUcates. DupUcate records among a set of previously selected cells may be displayed for the user if desired. Also, the user may specify that duplicates be removed from selected cells, or that a subset of such duplicates be removed.
Campaign manager module 706 maintains a link between cells 1103 and the parent segment from which the ceUs were derived. A database table is developed and stored, which contains descriptions of the unique attributes of each cell 1103. Such attributes include, for example: • a description of the customer group being targeted in the ceU
• the channel used to approach and contact the group described in the ceU
• the treatment used to market to this group
• the aUocated budget for the ceU
• the expected results of the campaign for this cell • the actual results (when available)
In one embodiment, these attributes are stored in the same table as the segment data itself.
CeUs 1103 portion of marketing campaign 1101 shows the user the hierarchical organization of ceUs 1103, where appropriate. Offers 1104 associated with a cell are also displayed. The channels 1105 through which offers 1104 will be made are displayed. For each offer 1104, there is specified a list of acceptable actions the recipient of the offer may take. These include, for example, accepting the offer, requesting additional information, declining the offer, requesting removal from the Ust, and the like. Depending on the action taken, the recipient of an offer may or may not be included in subsequent campaigns.
Campaign manager module 706 also analyzes the response to offers 1104 in terms of success criteria, in order to derive a determination as to the overall success of the marketing campaign. Offers 1104 that are associated with cells 1103 may be mutually exclusive in some cases (though not always). The user can specify such mutual exclusivity where appropriate, so that module 706 can take such factors into account when generating campaign 1101. Offers 1104 output by module 706 also include the script to be used in making the offer. For example, in a direct mail channel the script is the text of the letter to be sent to the prospective customer. In a telephone solicitation channel, the script is the text that the customer support representative will read in communicating with the prospective customer. In an alternative embodiment, the offers 1104 output may point to a script that is stored in some format, such as a Microsoft Word file for example.
Channels 1105 are specified for the offers 1104 being made. These may include, for example, direct mail, telemarketing, fulfiUment, newspaper ads, and the Uke. For each channel, various treatments are specified depending on the nature of the channel. These treatments describe distinct elements and specifications for executing the offer through the corresponding channel. Table 2 shows treatments for several examples of channels.
Channel Corresponding Treatment Describes
Direct Mail Contents of mailing package, creative offer, component costs
Telemarketing (inbound & outbound) Vendor, scripts, offer, component costs
FulfiUment Contents of fulfiUment package, creative, appUcation, offer, and component costs
Newspaper Ads PubUcation, placement, circulation, creative, offer, and component costs
Table 2: Treatments for Several Examples of Channels
For each offer 1104, one or more possible actions can be specified. The user of the system can select from a master list of actions, as appropriate to the particular response of the customer. Such responses include, for example: "not interested", "contact again in three months", "accept", "contact using another channel", and the Uke. For each such response, the user can define what actions should be taken. These can include, for example: making a different offer (perhaps increasing the incentive), scheduling for re-sending of the offer at a later time, changing the channel, and the Uke. The system aUows the user to specify the action for each expected customer response.
Campaign 1101 also contains a schedule 1106 describing when each offer 1104 will be made to members of a particular cell 1103. Schedule 1106 also identifies and displays to the user other campaigns that are running at the same time as the campaign being created. Output can be generated, for example, by product, department, cell, segment, household, account, and/ or customer. Such reports can be generated from available data by conventional report generation means.
Marketing campaign 1101 is provided as output 1107 to the user, to be displayed, printed, transmitted, and/ or stored in a conventional manner. In one embodiment, output 1107 takes the form of data files for supporting the back end of a direct marketing process. Output 1107 may include, for example, formats or templates for external direct mail production houses and caU centers. These templates facilitate population of data fields, calculated values, tracking codes, and test fields. For example, for a mailing, the templates might include form letters, where the customer name and address will be fined in for each customer before printing. For a telephone channel, the templates might be in the form of scripts to be foUowed by a telephone representative, as weU as guidelines for handling interactions not covered in the script. Additional support and output information may also be provided, such as freeze files (capture record identifiers and selected data elements at the time the campaign 1101, segment, and cell were created to faciUtate more accurate response analysis and modeling) and longitudinal marketing files (identify permanent segments and marking records in the database for extended periods of time). From the above description, it wiU be apparent that the invention disclosed herein provides a novel and advantageous system and method of planning and implementing customer relationship management. The foregoing discussion discloses and describes merely exemplary methods and embodiments of the present invention. As wiU be understood by those famiUar with the art, the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.
CUSTOMER RELATIONSHIP MANAGEMENT SYSTEM AND METHOD
APPENDIX A:
BASE ATTRIBUTES FOR DATA MODEL
DERIVED ATTRIBUTES FOR DATA MODEL
Demographic_key
Household_key General Attributes Branch .key Age band I type Branch. .address Childi cii I . income Branch. .city Inco e.band I. hea _name Branch name Marital.status I address Branch. .state Sex I.city Month_key Branch state Race I.state Account_key Branch_SCF Occupation l_zip Product.key
I Iυmeowner l.revenue Branch, key
Moved last 6 month I _acquisitιon_cost Household. key
Customer_key I service_cost
Prospect.key l.retention.cost Demographic.key I. marketing.budgi t Status.key
Checking_Accounts_key
Savings_Accounts_Regular_ke)
Account _key Savings. Accounts_IRA.key
Account .addrebs CU.Regular.key Customer, key
Account .city CD.lRA.key Pirst. name
Account .stale Mortgage. key I ast .name
Account .zip Home.Equity.Line.key
SSN
Account. .SCI: Autoloan_key
Birthday
Account. Installment_loan_key
-a ine_of. Credit_key Status
Date_opened
Credit_card_key Customer_revenue
Primary _age Investments_Regular_key Status_key Customer_acquisition_co.' I
Primary.. marital Investments_IRA_key Closed_account_flag
Customer_service_cost
Primary. sex Branch_Transactions_key , New_account_flag
Customer_retention_cost
Primary. surname rOS_Transactions_key Stalus_description
Customer_profitability*
Secondary _surnairu Wire_Transfers_key Status_reason
Marketing_budget
Phone_Transactions_key
Mail_Transactions_key
Needs_key PC_Transaclions_key Prospect_key Need_description ATM_Transactions_key First_name Household me be Credit_Card_Transactions_key
Events.key Last.name
Needs_key SSN
Product_key Campaign_key Birthday
Category Promotional_history_key Status
Product_descriptioι Contact_history_key Marketing_budge
Type FACTS IN EXCEL Syndicated data
Month_key Event_key
Fiscal_quarter Event ype
Month Confidential and Proprietary Event_date
Year Household membe
Accounts
Savings_Accounts_IRA_ke) Credil_card_key date_opened card_type campaign_response flag expiratιon_dale channei.used annual_fee_amount
Iocation_opened cc.VIP_account_.flag initial.deposit cc_frequent_flιer member flag interes rnte last_card_maιl.date annual_ιnterest_paιd cc_dιrect_maιl_flag total_interest_paid cc_telemarket_flag overdraftjimit cc.annual. fee_amount number. of.ATM.cards cc_acxount_delιnqιιency_fiag overdrafts_flag cc_last_credιt_lιne_ιncrease automatιc_deposιts_flag cc_amount_of_last_credit_line_inerease automatιc_withdrawal_flag cc_requested_ last_credil_lιne_ιncrease_fl automatic_transfers_ flag initiated_balance_transfer.ιn_flag fees_waιved_flag inιtiated_balance_transfer out. flag charges_waived_flag cc_dropped_credιt_card
Figure imgf000030_0001
early_withdrawal_flag cc_stopped_credιt_card_use
CD. Regular.key CD_IRA_key date_opened date_opened Inveslments_Regular_key Investments_IRA_key campaign_response_flag campaign_response_flag date_opened date_opened channei.used channel_used campaign_response_flag campaign_response_flag location.opened location_opened channel_used channei.used initial.deposit inιtιal_deposιt location_opened locatιon_opened
CD term CD_term initial_deposit initιal_deposit inιtιal_deposιt inιtιal_deposιt gross_rate_of_return gross_rate_of_return
Lnterest_rate interest_rate tax_benefιt tax.benefit months_untιl_maturity months_unti ..maturity net_rate net.rate fees _ aιved_flag fees_waived_flag In estment_type Investment. type servιce_charges_waived_flaj servιce_charges_waιved_flaj Shares_held Shares.held fees paid fees_paid Investments_annual_fee_amount Inveslments_annual_fee_amou servιce_charges_paιd servιce_charges_paιd Investments_VIP_account_flag Investmenls VII _.account_flag early_withdrawal_flag lnvestments_credit_lιne fees_waived_fiag
Investments_last_credit_lιne_ιncrease service_charges.waived.flag lnvestments_amount_of_last_credιt_lιne. .increase early_withdrawal_flag lnvestments_requested_ last_credιt_line_ increase
Confidential and Proprietary
Loans (1)
Autoloan_key ortgage_key Home_Equity_Line_key Installment. Loan.key date_opened date_opened date_opened date_opened campaign .response.flag campaign_response_flag campaign_response_flag campaign_response l g channei. used channe used channel_used channel used location_opened lυcation_opened location_opened locatiυn.opened amount_borrowed amount_borrowed amount_borrowed amount.borrovved inlerest.rate interest_rate interest_rate interest_rate term fixed_interest_rate_flag fixed_interest_rate_flag fixed_interest_rate_flag months_to_maturity variable_interest_rate_flag variable_interest_rate_flag variable_interest_rate_flag collateral_value teπri term term monthly_payment rnonths_to_maturity months_to_maturity , months_lo_maturity aulomatic_paymen t_flag mortgage_type collateral_value collateral_value early, payment.flag first. mortgage_amount monthly_payment monthly_payment paynιent..method second_mortgage_amount automatic_payment_flag automatic_payment.flag
30_day_delinquency_flag collateral_value early_payment_flag early_payment_flag
6()_ day_delinquency_flag monthly_payment payment_method payment_method
90..day_delinquency_flag automatic_payment_flag fees_vvaived_flag fees .waived.flag
120+_day_delinquency_fla; early_payment_flag service_charges_waived_flag service_charges_waived._flag payment_metlιod 30_day_delinquency_flag 30_day_delinquency_flag fees_vvaived_flag 60_day_delinquency_flag 60_day_delinquency_flag service_charges_waived_flaj 90_day_delinquency_flag 90_day_delinquency_fl.ιg
30_day_delinquency_flag 120+_day_delinquency_flag 120+_day_delinquency_flag
60_day_delinquency_flag last_credit_line_increase last_credit_Iine_increase
90_day_delinquency_flag amount_of_last_credi t ine_increase amount_of_last_credit_line_increase
120+_day_delinquency_flag requested_ Iast_credit_line_increase_fl, requested_ last_credit_Iine_increase_fl, credit _life_insurance_f lag overlimits_flag overlimits_flag credit_life insurance_amouι overlimit_max_amount overlimit_max_amounl credit Jife_insurance_flag credit_life_insurance_flag credit_life_insurance_amount credit_life_insurance_amount
Confidential and Proprietary
Loans (2)
Line_of_Credit_key
I OC.date opened
I QC_ camρaιgn_response_flag
1 OC_ channt'Lused
1 OC_ locatιon_opened
I C)C amount_borrovved
1 C)C_ mterest_rate
I OC_ fιxed_ιnterest_rate_flag
I OC_ varιable_ιnterest_rate_flag
I OC. term
I OC _ credit .line amount
I C_ annual_fee
I C_ months_untιl_renewal
I OC_ collaleral_value
I OC_ monthly_payment
I OC_ automatιc_payment_flag
I OC_ early_payment_flag
I OC_ check_debιts_flag
I OC_ lransfer_debιts_flag
I OC_ payment_melhod
I OC_ fees_waιved_flag
I OC_ servιce_charges_waιved_flag
I OC_ 30_day_dehnquency_flag
I OC_ 60_day_dehnquency_flag
I OC_ 90_day_delιnquency_flag
I OC_ 120+_day_delιnquency_flag
I OC_ last.credit.lme.increase
I OC_ amount_of_last_credιt_hne_uιcrease
I OC_ requested. last_credιt_line_increase_fllg
1 OC_ overlιmits_flag
1 C_ overlιmit_max_amount
LOC_ credιt_lιfe_ιnsurance_flag
LOC_ credit_life_ιnsurance_amount
Confidential and Proprietary
Transactions
Figure imgf000033_0001
Confidential and Proprietary
Campaigns
Campaign.key
Campaign_description Contact_history_key
Promotional_history_key
Response_description Last_contact_date proιnotion_reason
Start_date I.ast_contact_reason product_type
End_dale Last_contact_channel
Last_promotion_date
Campaign_type (event, targe t_mt I) ast_contact_description
Last_promotion_channel
Campaign_priority Last_contact_product
Last_promotion_resolution
Campaign_target Last_contact_resolution
Last_offer_accepted
Campaign_offer Contacts_teller_reason_in _last_3_month
Campaign_budget Last_offer_accepted_dale
Contacts_teller_reason_in Jas t_6_month
Last_offer_accepted_channe
Targeted.population Contacts_teIIer_reason_in _last_12_monll ιs
Number_of_cells Contacts_VRU_reason_in. _Iast_3_month:
Campaign_first_channel Contacts_VRU_reason_in. _last_6_month:
FC_contact_date Contacts_VRU_reason_in. _last_12_montl s
FC_per_customer_cost Con tacts_CSR_reason_in_ last_3_months
Campaign_second_channel Con tacts_CSR_reason_in_ last_6_months
SC_contact_date Contacts_CSR_reason_in_ last_12_month i
SC_per_customer_cost Con tacts_ Web_reason_in_ last_3_month<
Campaign_third_channel Con tacts_ Web_reason_in_ last_6_month5
TC_contact_date Contacts_Web reason_in_ last 12_montr s
TC_per_customer_cost
Response_channel
Offered_accepted_date
Confidential and Proprietary
Reports
Customer value trend Household value trend
Cross sell report for account, household, region, and branch
Household report
Product report
Account balance demographics by product
Product activity by quarter Campaigns by month by geography Campaigns by month Campaign by geography Campaigns by household type Campaigns by risk level
Campaign/offer by customer (by month, by quarter, by year) Campaign/offer by household (by month, by quarter, by year) Campaign by type by time (month, quarter, year) Campaigns by channel by time (month, quarter, year) ω Campaigns by channel by household (or by customer) w Cost per contact
Cost of campaign by cell by channel Campaign effectiveness by month Campaign response by cell Campaign response by geography Campaign response by channel
Confidential and Proprietary
Calculations
Household_profitability=Household_revenue-Σ (HH_acquisition_cost+HH_service_cost+HH_retention_cost) over all campaigns Customer_profitability=Customer_revenue-Σ (Customer_acquisition_cost+Customer_service_cost+Customer_retention_cost) over all campaigns
Ul
Confidential and Proprietary
Fact Table
Banking Model Measures
Customer Analytics Inc.
Confidential and Proprietary
Pπmary_balance
Branch_transactιon_count*
Branch_transactιon_types
Avg_branch_transactιon_sιze*
Most_frequent_branch_transactιon_tιme*
Most_frequent_branch_transactιon_locatιon*
Days_below_mιn_balance*
Days_overdrawn* num_days_sιnce_last_payment* ratιo_payments_to_balance* ratιo_current_balance_to_last_year_bal*
HH_relatιonshrp_age
HH revenue
HH acquisition cost
HH_servιce_cost
HH retention cost
HH_profιtabιlιty*
HH_marketιng_budget
HH_future_profitabιlιty_score
HH πsk score
HH_LTV_score
HH_attntιon_score
HH_profϊtabιlιty*
HH_attιtudιnal_segment
HH_behavιoral_segment
HH_lιfestyle_segment
HH_actιfιty_segment
HH_attιtudιnal_segment
HH_behavιoral_segment
HH_lιfestyle_segment
HH_probabιlιty_of_accept_CD_offer
HH_probabιhty_of_accept_IRA_offer
HH_probabιlιty_of_accept_homeq_offer
HH_probabιhty_of_accept_savιngs_offer
HH_Max_number_of_campaιgns*
HH_Mm_ number_of_campaιgns*
Customer_relatιonshιp_age
Customer_acquιsιtιon_cost
Customer_servιce_cost
Customer_retentιon_cost
Customer_profιtabιlιty*
Customer_marketιng_budget
Customer_future_profitabιlιty_score
Customer_πsk_score
Customer LTV score
Page 1 Fact Table
Customer attrition score
Customer_attitudinal_segment
Customer_acquisition_cost
Customer_lifestyle_segment
Customer_activity_segment
Customer_needs_segment
Customer_adhoc_segment
Customer_probability_of_accept_card_offer
Customer_probability_of_accept_CD_offer
Customer_probability_of_accept_IRA_offer
Customer_probability_of_accept_homeq_offer
Customer_ρrobability_of_accept_savings_offer
Customer_Max_number_of_campaigns*
Customer_Min_ number_of_campaigns*
CC_profit_score
CC_segmentarion_lifestyle_score
CC_segmentation_behavior_score
CC_segmentation_attitude_score
CC_segmentation_activity_score
CC_attrition_score
CC_segmentation_lifestyle_score
CC_segmentation_behavior_score
CC_segmentation_attitude_score
CC_segmentation_activity_score
Figure imgf000038_0001
Accounts
Number of Accounts Account Openings
Number of Account Openings
Number of Account Openings as a % Total Accounts
Account Closings
Number of Account Closings
Number of Account Closings as a % Total Accounts
Balance
Page 2 Fact Table
Net Balance (Deposits - Loans) Transactions (Credits + Debits)
Business Volume (Deposits + Loans)
Number of Transactions
Transaction Volume
Transaction Size
Credits
Number of Credits
Credit Volume
Credit Size
Number of Credits as a % of Number of Transactions
Credit Volume as a % of Transaction Volume
Credit Volume as a % of Business Volume
Debits
Number of Debits
Debit Volume
Debit Size
Debit Number of as a % of Number of Transactions
Debit Volume as a % of Transaction Volume
Debit Volume as a % of Business Volume
Fees and Service Charges
Number of Fees/ Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Number of Transactions
Fee/Service Charge Volume as a % of Transaction Volume
Fee/Service Charge Volume as a % of Business Volume
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Number of Transactions
Fee/Service Charge Waived Volume as a % of Transaction Volume
Fee/Service Charge Waived Volume as a % of Business Volume
Accounts
Number of Accounts
Balance
Average Balance
Number of Accounts as a % of Total Accounts
Balance as a % of Total Balance
Account Openings
Number of Account Openings
Number of Account Openings as a % Total Account Openings
Number of Account Openings as a % Total Accounts
Account Closings
Page 3 Fact Table
Number of Account Closings
Number of Account Closmgs as a % Total Account Closings Number of Account Closmgs as a % Total Accounts Interest Rate Deposits
Number of Deposits
Deposit Volume
Deposit Size
Number of Deposits as a % of Total Transactions
Deposit Volume as a % of Total Transaction Volume
Deposit Volume as a % of Balance
Cash Deposits
Number of Cash Deposits
Cash Deposit Volume
Cash Deposit Size
Cash Deposit Number of as a % of Deposit Number of
Cash Deposit Volume as a % of Deposit Volume
Check Deposits
Number of Check Deposits
Check Deposit Volume
Check Deposit Size
Check Deposits Number of as a % of Deposits Number of
Check Deposit Volume as a % of Deposit Volume
Transfer Deposits
Number of Transfer Deposits
Transfer Deposit Volume
Transfer Deposit Size
Transfer Deposit Number of as a % of Deposit Number of
Transfer Deposit Volume as a % of Deposit Volume
Direct Deposits
Number of Direct Deposits
Direct Deposit Volume
Direct Deposit Size
Direct Deposit Number of as a % of Deposit Number of
Direct Deposit Volume as a % of Deposit Volume
POS Credits
Number of POS Credits
POS Credit Volume
POS Credit Size
POS Credit Number of as a % of Deposit Number of
POS Credit Volume as a % of Deposit Volume
Withdrawals
Number of Withdrawals
Withdrawal Volume
Withdrawal Size
Withdrawal Number of as a % of Total Transactions
Withdrawal Volume as a % of Total Transaction Volume
Withdrawal Volume as a % of Balance
Cash Withdrawals
Page 4 Fact Table
Number of Cash Withdrawals
Cash Withdrawal Volume
Cash Withdrawal Size
Cash Withdrawal Number of as a % of Withdrawal Number of
Cash Withdrawal Volume as a % of Withdrawal Volume
Check Withdrawals
Number of Check Withdrawals
Check Withdrawal Volume
Check Withdrawal Size
Check Withdrawal Number of as a % of Withdrawal Number of
Check Withdrawal Volume as a % of Withdrawal Volume
Transfer Withdrawals
Number of Transfer Withdrawals
Transfer Withdrawal Volume
Transfer Withdrawal Size
Transfer Withdrawal Number of as a % of Withdrawal Number of
Transfer Withdrawal Volume as a % of Withdrawal Volume
Direct Debits
Number of Direct Debits
Direct Debit Volume
Direct Debit Size
Direct Debit Number of as a % of Withdrawal Number of
Direct Debit Volume as a % of Withdrawal Volume
POS Debits
Number of POS Debits
POS Debit Volume
POS Debit Size
POS Debit Number of as a % of Withdrawal Number of
POS Debit Volume as a % of Withdrawal Volume
Fees and Service Charges
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Total Transaction Number of
Fee/Service Charge Volume as a % of Total Transaction Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Total Transaction Number of Fee/Service Charge Waived Volume as a % of Total Transaction Volume Fee/Service Charge Waived Volume as a % of Balance
Figure imgf000041_0001
Accounts
Number of Accounts
Page 5 Fact Table
Balance
Average Balance
Number of Accounts as a % of Total Accounts
Balance as a % of Total Balance
Account Openings
Number of Account Openings
Number of Account Openmgs as a % Total Account Openings
Number of Account Openings as a % Total Accounts
Account Closings
Number of Account Closmgs
Number of Account Closings as a % Total Account Closings
Number of Account Closmgs as a % Total Accounts
Interest Rate
Overdraft Credit Line
Number of Overdraft Credit Lines
Percentage of Checking Accounts covered by Overdraft Line
Total Line Size of Overdraft Credit Line
Average Line Size of Overdraft Credit Line
ATM Cards
Number of ATM Cards
Percentage of Accounts linked with ATM Card(s)
Percentage of Account Balances linked with ATM Card(s)
Deposits
Number of Deposits
Deposit Volume
Deposit Size
Number of Deposits as a % of Total Transactions
Deposit Volume as a % of Total Transaction Volume
Deposit Volume as a % of Balance
Cash Deposits
Number of Cash Deposits
Cash Deposit Volume
Cash Deposit Size
Cash Deposit Number of as a % of Deposit Number of
Cash Deposit Volume as a % of Deposit Volume
Check Deposits
Number of Check Deposits
Check Deposit Volume
Check Deposit Size
Check Deposits Number of as a % of Deposits Number of
Check Deposit Volume as a % of Deposit Volume
Transfer Deposits
Number of Transfer Deposits
Transfer Deposit Volume
Transfer Deposit Size
Transfer Deposit Number of as a % of Deposit Number of
Transfer Deposit Volume as a % of Deposit Volume
Direct Deposits
Number of Direct Deposits
Page 6 Fact Table
Direct Deposit Volume
Direct Deposit Size
Direct Deposit Number of as a % of Deposit Number of
Direct Deposit Volume as a % of Deposit Volume
POS Credits
Number of POS Credits
POS Credit Volume
POS Credit Size
POS Credit Number of as a % of Deposit Number of
POS Credit Volume as a % of Deposit Volume
Withdrawals
Number of Withdrawals
Withdrawal Volume
Withdrawal Size
Withdrawal Number of as a % of Total Transactions
Withdrawal Volume as a % of Total Transaction Volume
Withdrawal Volume as a % of Balance
Cash Withdrawals
Number of Cash Withdrawals
Cash Withdrawal Volume
Cash Withdrawal Size
Cash Withdrawal Number of as a % of Withdrawal Number of
Cash Withdrawal Volume as a % of Withdrawal Volume
Check Withdrawals
Number of Check Withdrawals
Check Withdrawal Volume
Check Withdrawal Size
Check Withdrawal Number of as a % of Withdrawal Number of
Check Withdrawal Volume as a % of Withdrawal Volume
Transfer Withdrawals
Number of Transfer Withdrawals Transfer Withdrawal Volume
Transfer Withdrawal Size
Transfer Withdrawal Number of as a % of Withdrawal Number of
Transfer Withdrawal Volume as a % of Withdrawal Volume
Direct Debits
Number of Direct Debits
Direct Debit Volume
Direct Debit Size
Direct Debit Number of as a % of Withdrawal Number of
Direct Debit Volume as a % of Withdrawal Volume
POS Debits
Number of POS Debits
POS Debit Volume
POS Debit Size
POS Debit Number of as a % of Withdrawal Number of
POS Debit Volume as a % of Withdrawal Volume
Stopped Checks
Number of Stopped Checks
Page 7 Fact Table
Stopped Check Volume
Stopped Check Size
Stopped Checks as a % of Total Checks
Stopped Check Volume as a % of Total Check Volume
Returned Checks
Number_of_Returned_Checks
Returned Check Volume
Returned Check Size
Returned Checks as a % of Total Checks
Returned Check Volume as a % of Total Check Volume
Overdrafts
Number of Overdrafts
Overdraft Volume
Overdraft Size
Overdrafts as a % of Total Checks
Overdraft Volume as a % of Total Check Volume
Fees and Service Charges
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Total Transaction Number of
Fee/Service Charge Volume as a % of Total Transaction Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Total Transaction Number of
Fee/Service Charge Waived Volume as a % of Total Transaction Volume
Fee/Service Charεe Waived Volume as a % of Balance
5 ^ »ι> 'τ*-s: '''!«i(i*ιt «
Accounts
Number of Accounts
Balance
Average Balance
Number of Accounts as a % of Total Accounts
Balance as a % of Total Balance
Account Openings
Number of Account Openings
Number of Account Openings as a % Total Account Openings
Number of Account Openings as a % Total Accounts
Account Closings
Number of Account Closings
Number of Account Closings as a % Total Account Closings
Number of Account Closings as a % Total Accounts
Interest Rate
age ; Fact Table
ATM Cards
Number of ATM Cards
Percentage of Accounts linked with ATM Card(s)
Percentage of Account Balances linked with ATM Card(s)
Deposits
Number of Deposits
Deposit Volume
Deposit Size
Number of Deposits as a % of Total Transactions
Deposit Volume as a % of Total Transaction Volume
Deposit Volume as a % of Balance
Cash Deposits
Number of Cash Deposits
Cash Deposit Volume
Cash Deposit Size
Cash Deposit Number of as a % of Deposit Number of
Cash Deposit Volume as a % of Deposit Volume
Check Deposits
Number of Check Deposits
Check Deposit Volume
Check Deposit Size
Check Deposits Number of as a % of Deposits Number of
Check Deposit Volume as a % of Deposit Volume
Transfer Deposits
Number of Transfer Deposits
Transfer Deposit Volume
Transfer Deposit Size
Transfer Deposit Number of as a % of Deposit Number of
Transfer Deposit Volume as a % of Deposit Volume
Direct Deposits
Number of Direct Deposits
Direct Deposit Volume
Direct Deposit Size
Direct Deposit Number of as a % of Deposit Number of
Direct Deposit Volume as a % of Deposit Volume
POS Credits
Number of POS Credits
POS Credit Volume
POS Credit Size
POS Credit Number of as a % of Deposit Number of
POS Credit Volume as a % of Deposit Volume
Withdrawals
Number of Withdrawals
Withdrawal Volume
Withdrawal Size
Withdrawal Number of as a % of Total Transactions
Withdrawal Volume as a % of Total Transaction Volume
Withdrawal Volume as a % of Balance
Cash Withdrawals
Page 9 Fact Table
Number of Cash Withdrawals
Cash Withdrawal Volume
Cash Withdrawal Size
Cash Withdrawal Number of as a % of Withdrawal Number of
Cash Withdrawal Volume as a % of Withdrawal Volume
Check Withdrawals
Number of Check Withdrawals
Check Withdrawal Volume
Check Withdrawal Size
Check Withdrawal Number of as a % of Withdrawal Number of
Check Withdrawal Volume as a % of Withdrawal Volume
Transfer Withdrawals
Number of Transfer Withdrawals
Transfer Withdrawal Volume
Transfer Withdrawal Size
Transfer Withdrawal Number of as a % of Withdrawal Number of
Transfer Withdrawal Volume as a % of Withdrawal Volume
Direct Debits
Number of Direct Debits
Direct Debit Volume
Direct Debit Size
Direct Debit Number of as a % of Withdrawal Number of
Direct Debit Volume as a % of Withdrawal Volume
POS Debits
Number of POS Debits
POS Debit Volume
POS Debit Size
POS Debit Number of as a % of Withdrawal Number of
POS Debit Volume as a % of Withdrawal Volume
Fees and Service Charges
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Total Transaction Number of
Fee/Service Charge Volume as a % of Total Transaction Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Total Transaction Number of
Fee/Service Charge Waived Volume as a % of Total Transaction Volume
Fee/Service Charge Waived Volume as a % of Balance
Accounts
Number of Accounts
Page 10 Fact Table
Balance
Average Balance
Number of Accounts as a % of Total Accounts
Balance as a % of Total Balance
Account Openings
Number of Account Openings
Number of Account Openmgs as a % Total Account Openings
Number of Account Openmgs as a % Total Accounts
Account Closings
Number of Account Closings
Number of Account Closmgs as a % Total Account Closings
Number of Account Closmgs as a % Total Accounts
Interest Rate
ATM Cards
Number of ATM Cards
Percentage of Accounts linked with ATM Card(s)
Percentage of Account Balances linked with ATM Card(s)
Deposits
Number of Deposits
Deposit Volume
Deposit Size
Number of Deposits as a % of Total Transactions
Deposit Volume as a % of Total Transaction Volume
Deposit Volume as a % of Balance
Cash Deposits
Number of Cash Deposits
Cash Deposit Volume
Cash Deposit Size
Cash Deposit Number of as a % of Deposit Number of
Cash Deposit Volume as a % of Deposit Volume
Check Deposits
Number of Check Deposits
Check Deposit Volume
Check Deposit Size
Check Deposits Number of as a % of Deposits Number of
Check Deposit Volume as a % of Deposit Volume
Transfer Deposits
Number of Transfer Deposits
Transfer Deposit Volume
Transfer Deposit Size
Transfer Deposit Number of as a % of Deposit Number of
Transfer Deposit Volume as a % of Deposit Volume
Direct Deposits
Number of Direct Deposits
Direct Deposit Volume
Direct Deposit Size
Direct Deposit Number of as a % of Deposit Number of
Direct Deposit Volume as a % of Deposit Volume POS Credits
Page 11 Fact Table
Number of POS Credits
POS Credit Volume
POS Credit Size
POS Credit Number of as a % of Deposit Number of
POS Credit Volume as a % of Deposit Volume
Withdrawals
Number of Withdrawals
Withdrawal Volume
Withdrawal Size
Withdrawal Number of as a % of Total Transactions
Withdrawal Volume as a % of Total Transaction Volume
Withdrawal Volume as a % of Balance
Cash Withdrawals
Number of Cash Withdrawals
Cash Withdrawal Volume
Cash Withdrawal Size
Cash Withdrawal Number of as a % of Withdrawal Number of
Cash Withdrawal Volume as a % of Withdrawal Volume
Check Withdrawals
Number of Check Withdrawals
Check Withdrawal Volume
Check Withdrawal Size
Check Withdrawal Number of as a % of Withdrawal Number of
Check Withdrawal Volume as a % of Withdrawal Volume
Transfer Withdrawals
Number of Transfer Withdrawals
Transfer Withdrawal Volume
Transfer Withdrawal Size
Transfer Withdrawal Number of as a % of Withdrawal Number of
Transfer Withdrawal Volume as a % of Withdrawal Volume
Direct Debits
Number of Direct Debits
Direct Debit Volume
Direct Debit Size
Direct Debit Number of as a % of Withdrawal Number of
Direct Debit Volume as a % of Withdrawal Volume
POS Debits
Number of POS Debits
POS Debit Volume
POS Debit Size
POS Debit Number of as a % of Withdrawal Number of
POS Debit Volume as a % of Withdrawal Volume
Fees and Service Charges
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Total Transaction Number of
Fee/Service Charge Volume as a % of Total Transaction Volume
Fee/Service Charge Volume as a % of Balance
Page 12 Fact Table
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/ Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Total Transaction Number of
Fee/Service Charge Waived Volume as a % of Total Transaction Volume
Fee/Service Charge Waived Volume as a % of Balance
Accounts
Number of Accounts
Balance
Average Balance
Number of Accounts as a % of Total Accounts
Balance as a % of Total Balance
Account Openings
Number of Account Openmgs
Number of Account Openings as a % Total Account Openings
Number of Account Openmgs as a % Total Accounts
Account Closings
Number of Account Closmgs
Number of Account Closmgs as a % Total Account Closmgs
Number of Account Closings as a % Total Accounts
Interest Rate
Term
Months Until Maturity
Percentage Auto Renewal
Fees and Service Charges
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Total Transaction Number of
Fee/Service Charge Volume as a % of Total Transaction Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Total Transaction Number of
Fee/Service Charge Waived Volume as a % of Total Transaction Volume
Fee/Service Charge Waived Volume as a % of Balance
Accounts
Number of Accounts
Page 13 Fact Table
Balance
Average Balance
Number of Accounts as a % of Total Accounts
Balance as a % of Total Balance
Account Openings
Number of Account Openmgs
Number of Account Openings as a % Total Account Openmgs
Number of Account Openmgs as a % Total Accounts
Account Closings
Number of Account Closings
Number of Account Closings as a % Total Account Closings
Number of Account Closings as a % Total Accounts
Interest Rate
Term
Months Until Maturity
Percentage Auto Renewal
Fees and Service Charges
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Total Transaction Number of
Fee/Service Charge Volume as a % of Total Transaction Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Total Transaction Number of
Fee/Service Charge Waived Volume as a % of Total Transaction Volume
Fee/Service Charge Waived Volume as a % of Balance
BBiDBl
Accounts
Number of Accounts
Balance
Average Balance
Number of Accounts as a % of Total Accounts
Balance as a % of Total Balance
Account Openings
Number of Account Openings
Number of Account Openings as a % Total Account Openings
Number of Account Openings as a % Total Accounts
Account Closings
Number of Account Closings
Number of Account Closings as a % Total Account Closings
Number of Account Closings as a % Total Accounts
Rate of Return
Page 14 Fact Table
Gross Rate Tax Benefit Net Rate Investment Mix Bonds Government Corporate Mutual Funds Government Bond Corporate Bond Blue Chip Mid-Cap Small-Cap Technology Transportation Health Care Other Sector
International
Stocks
Blue Chip
Mid-Cap
Small-Cap
Technology
Transportation
Health Care
Other Sector
International
Other
Trades
Number of Trades
Trade Volume
Trade Size
Number of Trades as a % of Total Transactions
Trade Volume as a % of Total Transaction Volume
Trade Volume as a % of Balance
ATM Cards
Number of ATM Cards
Percentage of Accounts linked with ATM Card(s)
Percentage of Account Balances linked with ATM Card(s)
Deposits
Number of Deposits
Deposit Volume
Deposit Size
Number of Deposits as a % of Total Transactions
Deposit Volume as a % of Total Transaction Volume
Deposit Volume as a % of Balance
Cash Deposits
Number of Cash Deposits
Cash Deposit Volume
Page 15 Fact Table
Cash Deposit Size
Cash Deposit Number of as a % of Deposit Number of
Cash Deposit Volume as a % of Deposit Volume
Check Deposits
Number of Check Deposits
Check Deposit Volume
Check Deposit Size
Check Deposits Number of as a % of Deposits Number of
Check Deposit Volume as a % of Deposit Volume
Transfer Deposits
Number of Transfer Deposits
Transfer Deposit Volume
Transfer Deposit Size
Transfer Deposit Number of as a % of Deposit Number of
Transfer Deposit Volume as a % of Deposit Volume
Direct Deposits
Number of Direct Deposits
Direct Deposit Volume
Direct Deposit Size
Direct Deposit Number of as a % of Deposit Number of
Direct Deposit Volume as a % of Deposit Volume
POS Credits
Number of POS Credits
POS Credit Volume
POS Credit Size
POS Credit Number of as a % of Deposit Number of
POS Credit Volume as a % of Deposit Volume
Withdrawals
Number of Withdrawals
Withdrawal Volume
Withdrawal Size
Withdrawal Number of as a % of Total Transactions
Withdrawal Volume as a % of Total Transaction Volume
Withdrawal Volume as a % of Balance
Cash Withdrawals
Number of Cash Withdrawals
Cash Withdrawal Volume
Cash Withdrawal Size
Cash Withdrawal Number of as a % of Withdrawal Number of
Cash Withdrawal Volume as a % of Withdrawal Volume
Check Withdrawals
Number of Check Withdrawals
Check Withdrawal Volume
Check Withdrawal Size
Check Withdrawal Number of as a % of Withdrawal Number of
Check Withdrawal Volume as a % of Withdrawal Volume
Transfer Withdrawals
Number of Transfer Withdrawals
Transfer Withdrawal Volume
Page 16 Fact Table
Transfer Withdrawal Size
Transfer Withdrawal Number of as a % of Withdrawal Number of
Transfer Withdrawal Volume as a % of Withdrawal Volume
Direct Debits
Number of Direct Debits
Direct Debit Volume
Direct Debit Size
Direct Debit Number of as a % of Withdrawal Number of
Direct Debit Volume as a % of Withdrawal Volume
POS Debits
Number of POS Debits
POS Debit Volume
POS Debit Size
POS Debit Number of as a % of Withdrawal Number of
POS Debit Volume as a % of Withdrawal Volume
Fees and Service Charges
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Total Transaction Number of
Fee/Service Charge Volume as a % of Total Transaction Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Total Transaction Number of
Fee/Service Charge Waived Volume as a % of Total Transacnon Volume
Fee/Service Charge Waived Volume as a % of Balance
WHn«| mZi& S i i i'j o ;••
Accounts
Number of Accounts
Balance
Average Balance
Number of Accounts as a % of Total Accounts
Balance as a % of Total Balance
Account Openings
Number of Account Openings
Number of Account Openings as a % Total Account Openings
Number of Account Openings as a % Total Accounts
Account Closings
Number of Account Closings
Number of Account Closings as a % Total Account Closings
Number of Account Closings as a % Total Accounts
Rate of Return
Gross Rate
Page 17 Fact Table
Tax Benefit Net Rate Investment Mix Bonds
Government
Corporate
Mutual Funds
Government Bond
Corporate Bond
Blue Chip
Mid-Cap
Small-Cap
Technology
Transportation
Health Care
Other Sector
International
Stocks
Blue Chip
Small-Cap
Technology
Transportation
Health Care
Other Sector
International
Other
Trades
Number of Trades
Trade Volume
Trade Size
Number of Trades as a % of Total Transactions
Trade Volume as a % of Total Transaction Volume
Trade Volume as a % of Balance
ATM Cards
Number of ATM Cards
Percentage of Accounts linked with ATM Card(s)
Percentage of Account Balances linked with ATM Card(s)
Deposits
Number of Deposits
Deposit Volume
Deposit Size
Number of Deposits as a % of Total Transactions
Deposit Volume as a % of Total Transaction Volume
Deposit Volume as a % of Balance
Cash Deposits
Number of Cash Deposits
Cash Deposit Volume
Cash Deposit Size
Page 18 Fact Table
Cash Deposit Number of as a % of Deposit Number of
Cash Deposit Volume as a % of Deposit Volume
Check Deposits
Number of Check Deposits
Check Deposit Volume
Check Deposit Size
Check Deposits Number of as a % of Deposits Number of
Check Deposit Volume as a % of Deposit Volume
Transfer Deposits
Number of Transfer Deposits
Transfer Deposit Volume
Transfer Deposit Size
Transfer Deposit Number of as a % of Deposit Number of
Transfer Deposit Volume as a % of Deposit Volume
Direct Deposits
Number of Direct Deposits
Direct Deposit Volume
Direct Deposit Size
Direct Deposit Number of as a % of Deposit Number of
Direct Deposit Volume as a % of Deposit Volume
POS Credits
Number of POS Credits
POS Credit Volume
POS Credit Size
POS Credit Number of as a % of Deposit Number of
POS Credit Volume as a % of Deposit Volume
Withdrawals
Number of Withdrawals
Withdrawal Volume
Withdrawal Size
Withdrawal Number of as a % of Total Transactions
Withdrawal Volume as a % of Total Transaction Volume
Withdrawal Volume as a % of Balance
Cash Withdrawals
Number of Cash Withdrawals
Cash Withdrawal Volume
Cash Withdrawal Size
Cash Withdrawal Number of as a % of Withdrawal Number of
Cash Withdrawal Volume as a % of Withdrawal Volume
Check Withdrawals
Number of Check Withdrawals
Check Withdrawal Volume
Check Withdrawal Size
Check Withdrawal Number of as a % of Withdrawal Number of
Check Withdrawal Volume as a % of Withdrawal Volume
Transfer Withdrawals Number of Transfer Withdrawals Transfer Withdrawal Volume Transfer Withdrawal Size
Page 19 Fact Table
Transfer Withdrawal Number of as a % of Withdrawal Number of
Transfer Withdrawal Volume as a % of Withdrawal Volume
Direct Debits
Number of Direct Debits
Direct Debit Volume
Direct Debit Size
Direct Debit Number of as a % of Withdrawal Number of
Direct Debit Volume as a % of Withdrawal Volume
POS Debits
Number of POS Debits
POS Debit Volume
POS Debit Size
POS Debit Number of as a % of Withdrawal Number of
POS Debit Volume as a % of Withdrawal Volume
Fees and Service Charges
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Total Transaction Number of
Fee/Service Charge Volume as a % of Total Transaction Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Total Transaction Number of
Fee/Service Charge Waived Volume as a % of Total Transaction Volume
Fee/Service Charge Waived Volume as a % of Balance
atitfflMiw
Accounts
Number of Accounts
Balance
Average Balance
Number of Accounts as a % of Total Accounts
Balance as a % of Total Balance
Account Openings
Number of Account Openings
Number of Account Openings as a % Total Account Openings
Number of Account Openings as a % Total Accounts
Account Closings
Number of Account Closings
Number of Account Closings as a % Total Account Closings
Number of Account Closings as a % Total Accounts
Interest Rate
Fixed
Page 20 Fact Table
Variable Contract Amount
Contract Amount
Contract Amount as a % of Total Exposure
Term
Months to Maturity
Collateral (Secured Loans)
Collateral Value
Collateral as a % of Balance
Collateral as a % of Contract Amount
Credit Line (Lines of Credit)
Total Line
Average Line
Line as a % of Total Exposure
Line Increase (Lines of Credit)
Utilization (Lines of Credit) Number of Line Increases
Line Increase Volume
Average Line Increase
Number of Line Increases as a % of Accounts
Line Increase Volume as a % of Line
Utilization (Lines of Credit)
Utilization Rate
Average Utilization
Utilization as a % of Total Utilization
Payments
Number of Payments
Payment Volume
Average Payment Size
Number of Payments per Account
Payment Volume as a % of Minimum Amount Due
Payment Volume as a % of Balance
Payment Volume as a % of Total Payment Volume
Cash Payments
Number of Cash Payments
Cash Payment Volume
Cash Payment Size
Cash Payment Number of as a % of Payment Number of
Cash Payment Volume as a % of Payment Volume
Check Payments
Number of Check Payments
Check Payment Volume
Check Payment Size
Check Payments Number of as a % of Payments Number of
Check Payment Volume as a % of Payment Volume
Transfer Payments
Number of Transfer Payments
Transfer Payment Volume
Transfer Payment Size
Transfer Payment Number of as a % ot Payment Number of
Page 21 Fact Table
Transfer Payment Volume as a % of Payment Volume
POS Credits
Number of POS Credits
POS Credit Volume
POS Credit Size
POS Credit Number of as a % of Payment Number of
POS Credit Volume as a % of Payment Volume
Debits (Lines of Credit)
Number of Debits
Debit Volume
Average Debit Size
Number of Debits per Account
Debit Volume as a % of Line
Debit Volume as a % of Balance
Debit Volume as a % of Total Debit Volume
Cash Debits (Lines of Credit)
Number of Cash Debits
Cash Debit Volume
Cash Debit Size
Cash Debit Number of as a % of Debit Number of
Cash Debit Volume as a % of Debit Volume
Merchandise POS Debits (Lines of Credit)
Number of POS Debits
POS Debit Volume
POS Debit Size
POS Debit Number of as a % of Debit Number of
POS Debit Volume as a % of Debit Volume
Check Debits (Lines of Credit)
Number of Check Debits
Check Debit Volume
Check Debit Size
Check Debit Number of as a % of Debit Number of
Check Debit Volume as a % of Debit Volume
Transfer Debits (Lines of Credit)
Number of Transfer Debits
Transfer Debit Volume
Transfer Debit Size
Transfer Debit Number of as a % of Debit Number of
Transfer Debit Volume as a % of Debit Volume
Fees and Service Charge Debits
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Debits Number of
Fee/Service Charge Volume as a % of Debit Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Page 22 Fact Table
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Debit Number of
Fee/Service Charge Waived Volume as a % of Debit Volume
Fee/Service Charge Waived Volume as a % of Balance
Overlimits (Lines of Credit)
Number of Overlimits
Overlimit Volume
Average Overlimit
Number of Overlimits as a % of Accounts
Overlimit Volume as a % of L e
30 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
60 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
90 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
120 + Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
Behavior Score
Line Increase Requests (Lines of Credit)
Number of Increase Requests
Amount Requested
% Increase Requests Granted
% Amount Requested Granted
% Increase Requests Declined
___m
Page 23 Fact Table
Accounts
Number of Accounts
Balance
Average Balance
Equity
Number of Accounts as a % of Total Accounts
Balance as a % of Contract Amount
Balance as a % of Total Loan Balance
Account Openings
Number of Account Openings
Number of Account Openings as a % Total Account Openings
Number of Account Openings as a % Total Accounts
Account Closings
Number of Account Closings
Number of Account Closings as a % Total Account Closings
Number of Account Closings as a % Total Accounts
Contract Amount
Contract Amount
Contract Amount as a % of Total Exposure
Term
Months to Maturity
Collateral
Collateral Value
Collateral as a % of Balance
Collateral as a % of Contract Amount
Interest Rate
Fixed
Variable
Payments
Number of Payments
Payment Volume
Average Payment Size
Number of Payments per Account
Minimum Payment Amount
Payment Volume as a % of Minimum Amount Due
Payment Volume as a % of Balance
Payment Volume as a % of Total Payment Volume
Cash Payments
Number of Cash Payments
Cash Payment Volume
Cash Payment Size
Cash Payment Number of as a % of Payment Number of
Cash Payment Volume as a % of Payment Volume
Check Payments
Number of Check Payments
Check Payment Volume
Check Payment Size
Check Payments Number of as a % of Payments Number of
Check Pavment Volume as a % of Pavment Volume
Page 24 Fact Table
Transfer Payments
Number of Transfer Payments
Transfer Payment Volume
Transfer Payment Size
Transfer Payment Number of as a % of Payment Number of
Transfer Payment Volume as a % of Payment Volume
Fees and Service Charge Debits
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Debits Number of
Fee/Service Charge Volume as a % of Debit Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Debit Number of
Fee/Service Charge Waived Volume as a % of Debit Volume
Fee/Service Charge Waived Volume as a % of Balance
30 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
60 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
90 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
120 + Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
Behavior Score
..Page 25 Fact Table
Credit Life Insurance
Number of Accounts Covered % of Accounts Covered % of Balances Covered
Accounts
Number of Accounts
Balance
Average Balance
Number of Accounts as a % of Total Accounts
Balance as a % of Total Balance
Account Openings
Number of Account Openmgs
Number of Account Openings as a % Total Account Openmgs
Number of Account Openmgs as a % Total Accounts
Account Closings
Number of Account Closings
Number of Account Closings as a % Total Account Closings
Number of Account Closings as a % Total Accounts
Interest Rate
Fixed
Vaπable
Collateral (Equity)
Collateral Value
Collateral as a % of Balance
Collateral as a % of Credit Line
Credit Line
Total Line
Average Line
Line as a % of Total Exposure
Line Increase
Number of Line Increases
Line Increase Volume
Average Line Increase
Number of Line Increases as a % of Accounts
Line Increase Volume as a % of Line
Utilization
Utilization Rate
Average Utilization
Utilization as a % of Total Utilization
Payments
Number of Payments
Payment Volume
Average Payment Size
Number of Payments per Account
Payment Volume as a % of Minimum Amount Due
Payment Volume as a % of Balance
Payment Volume as a % of Total Payment Volume
Page 26 Fact Table
Cash Payments
Number of Cash Payments
Cash Payment Volume
Cash Payment Size
Cash Payment Number of as a % of Payment Number of
Cash Payment Volume as a % of Payment Volume
Check Payments
Number of Check Payments
Check Payment Volume
Check Payment Size
Check Payments Number of as a % of Payments Number of
Check Payment Volume as a % of Payment Volume
Transfer Payments
Number of Transfer Payments
Transfer Payment Volume
Transfer Payment Size
Transfer Payment Number of as a % of Payment Number of
Transfer Payment Volume as a % of Payment Volume
POS Credits
Number of POS Credits
POS Credit Volume
POS Credit Size
POS Credit Number of as a % of Payment Number of
POS Credit Volume as a % of Payment Volume
Debits
Number of Debits
Debit Volume
Average Debit Size
Number of Debits per Account
Debit Volume as a % of Line
Debit Volume as a % of Balance
Debit Volume as a % of Total Debit Volume
Check Debits
Number of Check Debits
Check Debit Volume
Check Debit Size
Check Debit Number of as a % of Debit Number of
Check Debit Volume as a % of Debit Volume
Fees and Service Charge Debits
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Debits Number of
Fee/Service Charge Volume as a % of Debit Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived Fee/Service Charge Waived Volume Average Fee/Service Charge Waived Size
Page 27 Fact Table
Fee/Service Charge Waived Number of as a % of Debit Number of
Fee/Service Charge Waived Volume as a % of Debit Volume
Fee/Service Charge Waived Volume as a % of Balance
Overlimits
Number of Overlimits
Overlimit Volume
Average Overlimit
Number of Overlimits as a % of Accounts
Overlimit Volume as a % of Line
30 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
60 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
90 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
120 + Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
Behavior Score
Line Increase Requests
Number of Increase Requests
Amount Requested
% Increase Requests Granted
% Amount Requested Granted
% Increase Requests Declined
Credit Life Insurance
Number of Accounts Covered
% of Accounts Covered
% of Balances Covered
Page 28 Fact Table
Accounts
Number of Accounts
Balance
Average Balance
Number of Accounts as a % of Total Accounts
Balance as a % of Contract Amount
Balance as a % of Total Loan Balance
Account Openings
Number of Account Openings
Number of Account Openmgs as a % Total Account Openings
Number of Account Openmgs as a % Total Accounts
Account Closings
Number of Account Closings
Number of Account Closings as a % Total Account Closings
Number of Account Closings as a % Total Accounts
Contract Amount
Contract Amount
Contract Amount as a % of Total Exposure
Term
Months to Maturity
Collateral
Collateral Value
Collateral as a % of Balance
Collateral as a % of Contract Amount
Interest Rate
Fixed
Variable
Payments
Number of Payments
Payment Volume
Average Payment Size
Number of Payments per Account
Minimum Payment Amount
Payment Volume as a % of Minimum Amount Due
Payment Volume as a % of Balance
Payment Volume as a % of Total Payment Volume
Cash Payments
Number of Cash Payments
Cash Payment Volume
Cash Payment Size
Cash Payment Number of as a % of Payment Number of
Cash Payment Volume as a % of Payment Volume
Check Payments
Number of Check Payments
Check Payment Volume
Check Pavment Size
Page 29 Fact Table
Check Payments Number of as a % of Payments Number of
Check Payment Volume as a % of Payment Volume
Transfer Payments
Number of Transfer Payments
Transfer Payment Volume
Transfer Payment Size
Transfer Payment Number of as a % of Payment Number of
Transfer Payment Volume as a % of Payment Volume
Fees and Service Charge Debits
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Debits Number of
Fee/Service Charge Volume as a % of Debit Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Debit Number of
Fee/Service Charge Waived Volume as a % of Debit Volume
Fee/Service Charge Waived Volume as a % of Balance
30 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
60 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
90 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
120 + Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Page 30 Fact Table
Utilization Rate Behavior Score Credit Life Insurance
Number of Accounts Covered % of Accounts Covered % of Balances Covered
Accounts
Number of Accounts
Balance
Average Balance
Number of Accounts as a % of Total Accounts
Balance as a % of Contract Amount
Balance as a % of Total Loan Balance
Account Openings
Number of Account Openings
Number of Account Openings as a % Total Account Openings
Number of Account Openmgs as a % Total Accounts
Account Closings
Number of Account Closings
Number of Account Closings as a % Total Account Closings
Number of Account Closings as a % Total Accounts
Contract Amount
Contract Amount
Contract Amount as a % of Total Exposure
Term
Months to Maturity
Collateral
Collateral Value
Collateral as a % of Balance
Collateral as a % of Contract Amount
Interest Rate
Fixed
Variable
Payments
Number of Payments
Payment Volume
Average Payment Size
Number of Payments per Account
Minimum Payment Amount
Payment Volume as a % of Minimum Amount Due
Payment Volume as a % of Balance
Payment Volume as a % of Total Payment Volume
Cash Payments
Number of Cash Payments
Cash Pavment Volume
Page 31 Fact Table
Cash Payment Size
Cash Payment Number of as a % of Payment Number of
Cash Payment Volume as a % of Payment Volume
Check Payments
Number of Check Payments
Check Payment Volume
Check Payment Size
Check Payments Number of as a % of Payments Number of
Check Payment Volume as a % of Payment Volume
Transfer Payments
Number of Transfer Payments
Transfer Payment Volume
Transfer Payment Size
Transfer Payment Number of as a % of Payment Number of
Transfer Payment Volume as a % of Payment Volume
Fees and Service Charge Debits
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Debits Number of
Fee/Service Charge Volume as a % of Debit Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Debit Number of
Fee/Service Charge Waived Volume as a % of Debit Volume
Fee/Service Charge Waived Volume as a % of Balance
30 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
60 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
90 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Page 32 Fact Table
Utilization Rate
120 + Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
Behavior Score
Credit Life Insurance
Number of Accounts Covered
% of Accounts Covered
% of Balances Covered
Accounts
Number_of_Accounts
Balance cc_Total_Months_Zero_Balance cc_Total_Months_Zero_Spending cc_Average_Balance
Number of Accounts as a % of Total Accounts
Balance as a % of Total Balance
CC_number_of_inquiπes_last_6_months
CC_interest_paιd_in_last_6_months
CC_over_limit_fee_amount_in_last_6_months
CC_late_fee_amount_paιd_ιn_last_6_months
CC_Number_of_Retaιl_Transactιons_Last_6_Months
CC_Number_of_Travel_Transactions_Last_6_Months
CC_Number_of_Restaurant_Transactιons_Last_6_Months
CC_Number_Cash_Advance_Transactιons_Last_6_Months
CC_Total_Retaιl_Transactιons_Amount_Last_6_Months
CC_Total_Travel_Transactιons_Amount_Last_6_Months
CC_Total_Restaurant_Transactιons_Amount_Last_6_Months
CC_Total_Cash_Advance_Transactιons_Amount_Last_6_Months
CC_Total_Cash_Advance_Fee_Amount_Last_6_Months
Account Openings
Number_of_Account_Openιngs
Number of Account Openings as a % Total Account Openings
Number of Account Openings as a % Total Accounts
Number_of_months_sιnce_first_openιng
Number_of_months_sιnce_last_openιng
Account Closings
Number of Account Closings
Number of Account Closings as a % Total Account Closings
Number of Account Closings as a % Total Accounts
Interest Rate
Page 33 Fact Table
Cash Rate
Merchandise Rate
Credit Line cc_Total_Lιne
Average Lme
Line as a % of Total Exposure
Number of Line Increases
Line Increase Volume
Average Line Increase
Number of Lme Increases as a % of Accounts
Line Increase Volume as a % of Line
Utilization cc_Utιlιzatιon_Rate cc_Average_Utιlιzatιon
Utilization as a % of Total Utilization cc Total Months Zero Utililzaπon
Number of Cards
Number of Cards
Average Number of Cards
Annual Fee
Fee
Average Fee
Months Until Renewal No Fee Accounts
Fee Accounts
Payments
Number_of_Payments
Payment Volume
Months_of_Zero_Payments
Months_of_Full_Payment
Average_Payment_Sιze
Number_of_Payments_per_Account
Payment_Volume_as_Percent_Mιnιmum_Amount_Due
Payment_Volume_as_Percent_of_Balance
Payment_Volume_as_Percent_of_Total_Payment_Volume
Cash Payments
Number_of_Cash_Payments
Cash_Payment_Volume
Cash_Payment_S lze
Cash_Payment_Number_Percent_of_Payment_Number
Cash_Payment_Volume_as_Percent_of_Payment_Volume
Check Payments
Number_of_Check_Payments
Check_Payment_Volume
CC_number_of_retumed_checks_ιn_last_6_months
Check Payment Size
Check_Payments_Number_as_Percent_of_Payments_Number
Check_Payment_Volume_as_Percent_of_Payment_Volume
Transfer Payments
Page 34 Fact Table
Number_of_Transfer_Payments
Transfer_Payment_Volume
Transfer_Payment_Sιze
Transfer_Payment_Number_as_Percent_of_Payment_Number
Transfer_Payment_Volume_as_Percent_of_Payment_Volume
POS Credit Volume as a % of Payment Volume
Debits
Number of Debits
Debit Volume
Average Debit Size
Number of Debits per Account
Debit Volume as a % of Lme
Debit Volume as a % of Balance
Debit Volume as a % of Total Debit Volume
Cash Debits
Number of Cash Debits
Cash Debit Volume
Cash Debit Size
Cash Debit Number of as a % of Debit Number of
Cash Debit Volume as a % of Debit Volume
Merchandise Debits
Number of POS Debits
POS Debit Volume
POS Debit Size
POS Debit Number of as a % of Debit Number of
POS Debit Volume as a % of Debit Volume
Check Debits
Number of Check Debits
Check Debit Volume
Check Debit Size
Check Debit Number of as a % of Debit Number of
Check Debit Volume as a % of Debit Volume
Transfer Debits
Number of Transfer Debits
Transter Debit Volume
Transfer Debit Size
Transfer Debit Number of as a % of Debit Number of
Transfer Debit Volume as a % of Debit Volume
Fees and Service Charge Debits
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Debits Number of
Fee/Service Charge Volume as a % of Debit Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume λverage Fee/Service Charge Waived Size
Page 35 Fact Table
Fee/Service Charge Waived Number of as a % of Debit Number of
Fee/Service Charge Waived Volume as a % of Debit Volume
Fee/Service Charge Waived Volume as a % of Balance
Overlimits
Number of Overlimits
Overlimit Volume
Average Overlimit
Number of Overlimits as a % of Accounts
Overlimit Volume as a % of Line
30 Day Delinquency
Number_of_Tιmes
Number_of_Accounts
Balances
Average_Balance
Number_of_Accounts_as_percent_of_Total_Accounts
Balances_as_percent_of_Total_Balance
Utihzation Rate
60 Day Delinquency
Number_of_Tιmes
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
90 Day Delinquency
Number_of_Tιmes
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
120 + Day Delinquency
Number_of_Tιmes
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
Behavior Score
Line Increase Requests
Number of Increase Requests
Amount Requested
% Increase Requests Granted
% Amount Requested Granted
% Increase Requests Declined Credit Life Insurance
Page 36 Fact Table
Number of Accounts Covered % of Accounts Covered % of Balances Covered Credit Card Registry Number of Accounts Covered % of Accounts Covered
Accounts
Number of Accounts
Balance
Average Balance
Number of Accounts as a % of Total Accounts
Balance as a % of Total Balance
Account Openings
Number of Account Openings
Number of Account Openmgs as a % Total Account Openings
Number of Account Openings as a % Total Accounts
Account Closings
Number of Account Closings
Number of Account Closings as a % Total Account Closings
Number of Account Closings as a % Total Accounts
Interest Rate
Cash Rate
Merchandise Rate
Credit Line
Total Line
Average Line
Line as a % of Total Exposure
Line Increase
Number of Line Increases
Line Increase Volume
Average Line Increase
Number of Lme Increases as a % of Accounts
Line Increase Volume as a % of Line
Utilization
Utilization Rate
Average Utilization
Utilization as a % of Total Utilization
Annual Fee
Fee
Average Fee
Months Until Renewal
No Fee Accounts
Fee Accounts
Payments
Number of Payments
Page 37 Fact Table
Payment Volume
Average Payment Size
Number of Payments per Account
Payment Volume as a % of Minimum Amount Due
Payment Volume as a % of Balance
Payment Volume as a % of Total Payment Volume
Cash Payments
Number of Cash Payments
Cash Payment Volume
Cash Payment Size
Cash Payment Number of as a % of Payment Number of
Cash Payment Volume as a % of Payment Volume
Check Payments
Number of Check Payments
Check Payment Volume
Check Payment Size
Check Payments Number of as a % of Payments Number of
Check Payment Volume as a % of Payment Volume
Transfer Payments
Number of Transfer Payments
Transfer Payment Volume
Transfer Payment Size
Transfer Payment Number of as a % of Payment Number of
Transfer Payment Volume as a % of Payment Volume
POS Credits
Number of POS Credits
POS Credit Volume
POS Credit Size
POS Credit Number of as a % of Payment Number of
POS Credit Volume as a % of Payment Volume
Debits
Number of Debits
Debit Volume
Average Debit Size
Number of Debits per Account
Debit Volume as a % of Line
Debit Volume as a % of Balance
Debit Volume as a % of Total Debit Volume
Check Debits
Number of Check Debits
Check Debit Volume
Check Debit Size
Check Debit Number of as a % of Debit Number of
Check Debit Volume as a % of Debit Volume
Transfer Debits
Number of Transfer Debits
Transfer Debit Volume
Transfer Debit Size
Transfer Debit Number of as a % of Debit Number ot
Page 38 Fact Table
Transfer Debit Volume as a % of Debit Volume
Fees and Service Charge Debits
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Debits Number of
Fee/Service Charge Volume as a % of Debit Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Debit Number of
Fee/Service Charge Waived Volume as a % of Debit Volume
Fee/Service Charge Waived Volume as a % of Balance
Overlimits
Number of Overlimits
Overlimit Volume
Average Overlimit
Number of Overlimits as a % of Accounts
Overlimit Volume as a % of Lme
30 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
60 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
90 Day Delinquency
Number of Accounts
Balances
Average Balance
Number of Accounts as a % of Total Accounts
Balances as a % of Total Balance
Utilization Rate
120 + Day Delinquency
Number of Accounts
Balances Average Balance
Number of Accounts as a % of Total Accounts Balances as a % of Total Balance Utilization Rate
Page 39 Fact Table
Behavior Score
Line Increase Requests
Number of Increase Requests Amount Requested % Increase Requests Granted % Amount Requested Granted % Increase Requests Declmed Credit Life Insurance Number of Accounts Covered % of Accounts Covered % of Balances Covered
Accounts
Number of Accounts
Cash Value
Average Cash Value
Number of Accounts as a % of Total Accounts
Cash Value as a % of Total Cash Value
Account Openings
Number of Account Openings
Number of Account Openmgs as a % Total Account Openings
Number of Account Openmgs as a % Total Accounts
Account Closings
Number of Account Closings
Number of Account Closings as a % Total Account Closings
Number of Account Closings as a % Total Accounts
Premium
Coverage Amount
Payments
Number of Payments
Payment Volume
Payment Size
Number of Payments as a % of Total Transactions
Payment Volume as a % of Total Transaction Volume
Payment Volume as a % of Cash Value
Cash Payments
Number of Cash Payments
Cash Payment Volume
Cash Payment Size
Cash Payment Number of as a % of Payment Number of
Cash Payment Volume as a % of Payment Volume
Check Payments
Number of Check Payments
Check Payment Volume
Check Payment Size
Page 40 Fact Table
Check Payments Number of as a % of Payments Number of
Check Payment Volume as a % of Payment Volume
Transfer Payments
Number of Transfer Payments
Transfer Payment Volume
Transfer Payment Size
Transfer Payment Number of as a % of Payment Number of
Transfer Payment Volume as a % of Payment Volume
Fees and Service Charges
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Total Transaction Number of
Fee/Service Charge Volume as a % of Total Transaction Volume
Fee/Service Charge Volume as a % of Cash Value
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Fee/Service Charge Waived Number of as a % of Total Transaction Number of
Fee/Service Charge Waived Volume as a % of Total Transaction Volume
Fee/Service Charge Waived Volume as a % of Cash Value
mm mmm
Safe Deposit Boxes
Number of Accounts
Number of Accounts as a % of Total Accounts
Account Openings
Number of Account Openings
Number of Account Openings as a % Total Account Openings
Number of Account Openings as a % Total Accounts
Account Closings
Number of Account Closings
Number of Account Closmgs as a % Total Account Closings
Number of Account Closings as a % Total Accounts
Fees and Service Charges
Number of Fees/Service Charges
Fee/Service Charge Volume
Average Fee/Service Charge Size
Fee/Service Charge Number of as a % of Total Transaction Number of
Fee/Service Charge Volume as a % of Total Transaction Volume
Fee/Service Charge Volume as a % of Balance
Fees and Service Charges Waived
Number of Fees/Service Charges Waived
Fee/Service Charge Waived Volume
Average Fee/Service Charge Waived Size
Page 41 Fact Table
Fee/Service Charge Waived Number of as a % of Total Transaction Number of
Fee/Service Charge Waived Volume as a % of Total Transaction Volume
Fee/Service Charge Waived Volume as a % of Balance
Other miscellaneous attributes
Campaιgn_break_even_target_percentage*
Campaιgn_total_cost*
Campaιgn_gross_profit*
Investments_number_of_trades_last_3_months*
Investments_number_of_trades_last_6_months*
Investments_number_of_trades_last_12_months*
Investments_πsk_score
Investments_profιt_score
Investments_segmentatιon_hfestyle_score
Investments_segmentatιon_behavιor_score
Investments_segmentatιon_attιtude_score
Investments_attπtιon_score
Investments_attπtιon_decιle
Investmens_dιrect_maιl_flag
Investments_telemarket_flag
Investments_over_lιmιt_last_6_months
Offers_accepted_ιn_last_3_months
Offers_receιved_ιn_last_3_months
Offers_accepted_in_last_6_months
Offers_receιved_ιn_last_6_months
Offers_accepted_m_last_ 12_months
Offers_receιved_ιn_last_ 12_months
Contacts_teller_m_last_3_months
Contacts_teller_ιn_last_6_months
Contacts_teller_ιn_last_ 12_months
Contacts_VRU_ιn_last_3_months
Unresolved_contacts_VRU_ιn last_3_months
Contacts_VRU_ιn_last_6_months
Unresolved_contacts_VRU_ιn last_6_months
Contacts_VRU_ιn_last_l 2_months
Unresolved_contacts_VRU_ιn last_ 12_months
Contacts_CSR_ιn_last_3_months
Unresolved_contacts_CSR_last_3_months
Contacts_CSR_ιn_last_6_months
Unresolved_contacts_CSR_last_6_months
Contacts_CSR_ιn_last_l 2_months
Unresolved_contacts_CSR_last_ 12_months
Contacts_Web_m_last_3_months
Unresolved_contacts_Web_last_3_months
Contacts_Web_m_last_6_months
Unresolved_contacts_Web_last_6_months
Contacts_Web_m_last_ 12_months
Unresolved contacts Web last 12 months
Page 42

Claims

What is Claimed is:
1. A computer-implemented customer relationship management method, comprising the steps of: a) defining goals and constraints for a marketing campaign; b) defining market segments and validating defined goals and constraints; c) designing the marketing campaign responsive to results of a) and b); d) executing the designed marketing campaign; e) capturing responses; and f) analyzing campaign results from e); wherein each of the steps is executed by a computer.
2. A computer-implemented customer relationship management system, comprising: a computer-implemented specification module, for defining goals and constraints for a marketing campaign; a computer-implemented analysis module, coupled to the specification module, for defining segments and validating goals and constraints; a computer-implemented design module, coupled to the analysis module and the specification module, for designing the campaign based on [the] results of the specification module and the analysis module; and a computer-implemented execution module, coupled to the design module, for generating output for the marketing campaign.
3. The system of claim 2, further comprising: a computer-implemented tracking module, coupled to the execution module, for capturing responses and analyzing campaign results.
4. The method of claim 1, wherein step a) comprises the substeps of: a.l) defining an overall goal for the marketing campaign; a.2) defining at least one constraint for the marketing campaign; and a.3) defining a set of customers for the marketing campaign.
5. The method of claim 4, wherein substep a.l) comprises defining a target value for a variable, the target variable representing a business goal.
6. The method of claim 4, wherein substep a.2) comprises defining at least one constraint for a parameter of the marketing campaign.
7. The method of claim 4, wherein step a) further comprises the substep of: a.4) specifying a budget for the marketing campaign.
8. The method of claim 1, wherein step b) comprises the substeps of: b.l) performing an exploratory analysis; b.2) defining a plurality of market segments to be targeted by the marketing campaign, each segment having characteristics; b.3) scoring the defined market segments according to a scoring metric; and b.4) validating the defined constraints.
9. The method of claim 8, wherein substep b.l) comprises performing a break-even analysis of the marketing campaign.
10. The method of claim 8, wherein substep b.3) comprises applying a correlation model to determine relationships among market segment characteristics.
11. The method of claim 10, wherein the correlation model comprises a predictive model.
12. The method of claim 8, wherein substep b.3) comprises applying a dependency network to determine relationships among market segment characteristics.
13. The method of claim 8, wherein step b) further comprises the substeps of: b.2.1) determining at least one marketing method for the marketing campaign; and b.2.2) determining at least one marketing channel for the marketing campaign.
14. The method of claim 8, wherein substep b.4) comprises determining whether appUcation of the defined constraints results in conflicts.
15. The method of claim 1, wherein step c) comprises the substeps of: c.l) selecting one of the defined market segments; c.2) dividing the selected market segment into a plurality of cells; c.3) for each of at least a subset of the cells, defining a marketing promotion and associating the defined marketing promotion with the cell.
16. The method of claim 15, wherein substep c.2) further comprises refining at least one of the cells.
17. The method of claim 1, wherein step d) comprises the substeps of: d.l) specifying an output format for the marketing campaign; d.2) developing a campaign schedule; and d.3) outputting data to at least one marketing channel according to the designed marketing campaign, using the specified output format.
18. The method of claim 1, wherein: step a) comprises defining goals and constraints for the marketing campaign by specifying positions in a multidimensional space representing values for target variables;
and wherein step f) comprises the substeps of: f.l) determining customer positions in the multidimensional space by measuring values of variables associated with the customers; and f.2) comparing the determined customer positions with the specified positions for the defined goals.
19. The method of claim 1, further comprising the step of: g) adjusting the designed marketing campaign responsive to results of f).
20. A computer-implemented customer relationship management system, comprising: a computer-implemented customer database, containing data describing customers and potential customers; a computer-implemented data model, coupled to the customer database, for accessing and interpreting data from the customer database; a computer-implemented data access layer, coupled to the data model, for accessing data from the data model; a computer-implemented segmentation module, coupled to the data access layer, for generating market segments; a computer-implemented report analysis and data mining module, coupled to the segmentation module, for determining scores for market segments; a computer-implemented campaign manager, coupled to the data access layer, for producing a marketing campaign; and an output device, coupled to the campaign manager, for outputting the marketing campaign.
21. The system of claim 20, further comprising: a graphical user interface, coupled to the campaign manager, for accepting user input regarding the marketing campaign.
22. The system of claim 20, wherein the segmentation module generates market segments using statistical clustering.
23. The system of claim 20, further comprising: a computer-implemented predictive model Ubrary, coupled to the report analysis and data mining module, for providing predictive models for appUcation to data from the data model.
24. The system of claim 23, wherein the segmentation module generates market segments using predictive models from the predictive model Ubrary.
25. The system of claim 23, wherein the report analysis and data mining module determines a scored segments set based on appUcation of the predictive model Ubrary to at least one segment.
26. The system of claim 20, wherein the campaign manager generates a marketing campaign including at least one selected from the group consisting of: cells; offers; channels; and schedules.
27. The system of claim 20, wherein the campaign manager analyzes responses to determine relative success of the marketing campaign.
28. A computer-implemented customer relationship management system, comprising: computer-implemented specification means for defining goals and constraints for a marketing campaign; computer-implemented analysis means, coupled to the specification means, for defining market segments and vaUdating defined goals and constraints; computer-implemented design means, coupled to the analysis means, for designing the marketing campaign responsive to results of the specification means and the analysis means; and computer-implemented execution means, coupled to the design means, for executing the designed marketing campaign.
29. The computer-implemented customer relationship management system of claim 28, further comprising: computer-implemented tracking means, coupled to the execution means, for capturing responses and analyzing campaign results.
30. The computer-implemented customer relationship management system of claim 28, wherein the specification means comprises: computer-implemented goal definition means for defining an overall goal for the marketing campaign; computer-implemented constraint definition means for defining at least one constraint for the marketing campaign; and computer-implemented customer definition means for defining a set of customers for the marketing campaign.
31. The method of claim 28, wherein the analysis means comprises: computer-implemented exploratory analysis means for performing an exploratory analysis; computer-implemented market segment definition means, for defining a pluraUty of market segments to be targeted by the marketing campaign, each segment having characteristics; computer-implemented scoring means, coupled to the market segment definition means, for scoring the defined market segments according to a scoring metric; and computer-implemented vaUdation means, for validating the defined constraints.
32. The method of claim 28, wherein the design means comprises: computer-implemented segment selection means, for selecting one of the defined market segments; computer-implemented cell division means, coupled to the segment selection means, for dividing the selected market segment into a plurality of cells; and computer-implemented promotion definition means, coupled to the cell division means, for, for each of at least a subset of the ceUs, defining a marketing promotion and associating the defined marketing promotion with the ceU.
33. The method of claim 28, wherein the execution means comprises: computer-implemented output specification means, for specifying an output format for the marketing campaign; computer-implemented scheduling means, for developing a campaign schedule; and computer-implemented output means, coupled to the output specification means and to the scheduling means, for outputting data to at least one marketing channel according to the designed marketing campaign, using the specified output format.
34. A computer program product comprising a computer-usable medium having computer-readable code embodied therein for customer relationship management, comprising: computer-readable program code devices configured to cause a computer to define goals and constraints for a marketing campaign; computer-readable program code devices configured to cause a computer to define market segments and validate defined goals and constraints; computer-readable program code devices configured to cause a computer to design the marketing campaign responsive to results of the computer- readable program code devices configured to cause a computer to define goals and constraints for a marketing campaign and the computer-readable program code devices configured to cause a com- puter to define market segments and validate defined goals and constraints; and computer-readable program code devices configured to cause a computer to execute the designed marketing campaign.
35. The computer program product of claim 34, further comprising: computer-readable program code devices configured to cause a computer to capture responses and analyze campaign results.
36. The computer program product of claim 34, wherein the computer- readable program code devices configured to cause a computer to define goals and constraints for a marketing campaign comprise: computer-readable program code devices configured to cause a computer to define an overaU goal for the marketing campaign; computer-readable program code devices configured to cause a computer to define at least one constraint for the marketing campaign; and computer-readable program code devices configured to cause a computer to define a set of customers for the marketing campaign.
37. The computer program product of claim 34, wherein the computer- readable program code devices configured to cause a computer to define market segments and validate defined goals and constraints comprise: computer-readable program code devices configured to cause a computer to perform an exploratory analysis; computer-readable program code devices configured to cause a computer to define a plurality of market segments to be targeted by the marketing campaign, each segment having characteristics; computer-readable program code devices configured to cause a computer to score the defined market segments according to a scoring metric; and computer-readable program code devices configured to cause a computer to validate the defined constraints.
38. The computer program product of claim 34, wherein the computer- readable program code devices configured to cause a computer to design the marketing campaign comprise: computer-readable program code devices configured to cause a computer to select one of the defined market segments; computer-readable program code devices configured to cause a computer to divide the selected market segment into a plurality of cells; and computer-readable program code devices configured to cause a computer to, for each of at least a subset of the ceUs, define a marketing promotion and associate the defined marketing promotion with the ceU.
39. The computer program product of claim 34, wherein the computer- readable program code devices configured to cause a computer to execute the designed marketing campaign comprise: computer-readable program code devices configured to cause a computer to specify an output format for the marketing campaign; computer-readable program code devices configured to cause a computer to develop a campaign schedule; and computer-readable program code devices configured to cause a computer to output data to at least one marketing channel according to the designed marketing campaign, using the specified output format.
PCT/US1999/029247 1998-12-11 1999-12-09 Customer relationship management system and method WO2000034910A2 (en)

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Cited By (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2819325A1 (en) * 2001-01-09 2002-07-12 France Telecom System for supporting commercial actions such as establishing list of prospective customers has device that may establish list of prospective customers as function at least of trading results information
US6546545B1 (en) 1998-03-05 2003-04-08 American Management Systems, Inc. Versioning in a rules based decision management system
US6601034B1 (en) 1998-03-05 2003-07-29 American Management Systems, Inc. Decision management system which is cross-function, cross-industry and cross-platform
US6609120B1 (en) 1998-03-05 2003-08-19 American Management Systems, Inc. Decision management system which automatically searches for strategy components in a strategy
US6708155B1 (en) 1999-07-07 2004-03-16 American Management Systems, Inc. Decision management system with automated strategy optimization
US20100100420A1 (en) * 2008-10-21 2010-04-22 Anderson James R Target marketing method and system
US7769626B2 (en) 2003-08-25 2010-08-03 Tom Reynolds Determining strategies for increasing loyalty of a population to an entity
US7917383B2 (en) 2005-11-11 2011-03-29 Accenture Global Services Limited Method and system for boosting the average revenue per user of products or services
US7953695B2 (en) * 2000-06-06 2011-05-31 Fair Isaac Corporation System and method for integrated data mart defining start schema model processing business contact data from multiple contact channels
US8041619B1 (en) * 2008-10-24 2011-10-18 Bank Of America Corporation Hybrid model for new account acquisition
US8306908B1 (en) 2002-12-31 2012-11-06 West Corporation Methods and apparatus for intelligent selection of goods and services in telephonic and electronic commerce
US8364578B1 (en) 1998-03-05 2013-01-29 Cgi Technologies And Solutions Inc. Simultaneous customer/account strategy execution in a decision management system
US8417560B2 (en) 2008-04-18 2013-04-09 Steven Woods Systems, methods, and apparatus for analyzing the influence of marketing assets
US8428997B2 (en) 2005-11-21 2013-04-23 Accenture Global Services Limited Customer profitability and value analysis system
US8655746B1 (en) 1998-03-11 2014-02-18 Tuxis Technologies Llc Methods and system for providing real time offers to a user based on obsolescence of possessed items
US8762193B2 (en) 2005-11-11 2014-06-24 Accenture Global Services Limited Identifying target customers for campaigns to increase average revenue per user
US8769567B1 (en) 2004-09-30 2014-07-01 Tuxis Technologies Llc Methods, media, and apparatus for intelligent selection of items encoded onto portable machine-readable entertainment media
US8793165B1 (en) 1998-03-11 2014-07-29 Tuxis Technologies Llc Method, program storage device, and apparatus for offering a user a plurality of scenarios under which to conduct a primary transaction
WO2016013972A1 (en) * 2014-07-23 2016-01-28 Communikate Digital Channel Group Pte Ltd Computer-implemented method of and system for running a marketing campaign
US9355376B2 (en) 2012-05-11 2016-05-31 Qvidian, Inc. Rules library for sales playbooks

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7874841B1 (en) 2001-08-08 2011-01-25 Lycas Geoffrey S Method and apparatus for personal awareness and growth

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
No Search *

Cited By (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6546545B1 (en) 1998-03-05 2003-04-08 American Management Systems, Inc. Versioning in a rules based decision management system
US6601034B1 (en) 1998-03-05 2003-07-29 American Management Systems, Inc. Decision management system which is cross-function, cross-industry and cross-platform
US6609120B1 (en) 1998-03-05 2003-08-19 American Management Systems, Inc. Decision management system which automatically searches for strategy components in a strategy
US7062757B2 (en) 1998-03-05 2006-06-13 American Management Systems, Inc. Decision management system which is cross-function, cross-industry and cross-platform
US7318224B2 (en) 1998-03-05 2008-01-08 American Management Systems, Inc. Versioning in a rules based decision management system
US8364578B1 (en) 1998-03-05 2013-01-29 Cgi Technologies And Solutions Inc. Simultaneous customer/account strategy execution in a decision management system
US8793165B1 (en) 1998-03-11 2014-07-29 Tuxis Technologies Llc Method, program storage device, and apparatus for offering a user a plurality of scenarios under which to conduct a primary transaction
US8655746B1 (en) 1998-03-11 2014-02-18 Tuxis Technologies Llc Methods and system for providing real time offers to a user based on obsolescence of possessed items
US6708155B1 (en) 1999-07-07 2004-03-16 American Management Systems, Inc. Decision management system with automated strategy optimization
US7953695B2 (en) * 2000-06-06 2011-05-31 Fair Isaac Corporation System and method for integrated data mart defining start schema model processing business contact data from multiple contact channels
FR2819325A1 (en) * 2001-01-09 2002-07-12 France Telecom System for supporting commercial actions such as establishing list of prospective customers has device that may establish list of prospective customers as function at least of trading results information
US8306908B1 (en) 2002-12-31 2012-11-06 West Corporation Methods and apparatus for intelligent selection of goods and services in telephonic and electronic commerce
US7769626B2 (en) 2003-08-25 2010-08-03 Tom Reynolds Determining strategies for increasing loyalty of a population to an entity
US8769567B1 (en) 2004-09-30 2014-07-01 Tuxis Technologies Llc Methods, media, and apparatus for intelligent selection of items encoded onto portable machine-readable entertainment media
US7917383B2 (en) 2005-11-11 2011-03-29 Accenture Global Services Limited Method and system for boosting the average revenue per user of products or services
US8762193B2 (en) 2005-11-11 2014-06-24 Accenture Global Services Limited Identifying target customers for campaigns to increase average revenue per user
US8428997B2 (en) 2005-11-21 2013-04-23 Accenture Global Services Limited Customer profitability and value analysis system
US8417560B2 (en) 2008-04-18 2013-04-09 Steven Woods Systems, methods, and apparatus for analyzing the influence of marketing assets
US8473327B2 (en) * 2008-10-21 2013-06-25 International Business Machines Corporation Target marketing method and system
US8768746B2 (en) 2008-10-21 2014-07-01 International Business Machines Corporation Target marketing
US20100100420A1 (en) * 2008-10-21 2010-04-22 Anderson James R Target marketing method and system
US8041619B1 (en) * 2008-10-24 2011-10-18 Bank Of America Corporation Hybrid model for new account acquisition
US9355376B2 (en) 2012-05-11 2016-05-31 Qvidian, Inc. Rules library for sales playbooks
WO2016013972A1 (en) * 2014-07-23 2016-01-28 Communikate Digital Channel Group Pte Ltd Computer-implemented method of and system for running a marketing campaign

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