AU2015203884A1 - Adaptive marketing using insight driven customer interaction - Google Patents
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Abstract
A computer-implemented method for insight-driven, adaptive marketing, including extracting customer data using an extraction module of a computer system for a plurality of customers from at least one database of customer data using a Customer Analytic Record (CAR) application, wherein the CAR application uses the capabilities of a database management system and a structured query language (SQL, defining a targeted marketing campaign by a marketing campaign defining module, the targeted marketing campaign including a selected targeted marketing offer, a selected targeted interaction channel, and a targeted set of customers most likely to accept the targeted marketing offer via the targeted interaction channel, wherein the targeted set of customers is determined by selecting, through the CAR application, a random selection of the plurality of customers, segmenting a sample set of the customers within the random selection into a plurality of customer segments, wherein the customer segments are created based on similar characteristics among multiple customers, describing each of the customer segments with lift values indicating a likelihood of positive response by a selected customer segment to a selected marketing offer via the targeted interaction channel, selecting, based on the lift values, a prospect set from the customer segments for potential inclusion in a selected marketing campaign, assessing with a predictive model a likelihood of acceptance of each customer in the selected prospect set of the selected marketing offer via the targeted interaction channel, scoring each customer in the selected prospect set with a customer score indicating the likelihood of acceptance, and selecting the targeted set of customers based on the scoring, and executing, by a processor, the targeted marketing campaign by offering the targeted marketing offer to the targeted set of customers via the targeted interaction channel, gathering, using a campaign tracking module, campaign results data from the executed targeted marketing, the campaign results data including a count of customers accepting the targeted marketing offer via the targeted interaction channel, and a length of time between the making of the targeted marketing offer and the accepting of the targeted marketing offer, training, the predictive model with the campaign results data, wherein the training is based on how the customers respond to various types of the marketing offers and to various types of the interaction channels used in the targeted marketing campaign, and updating, via a series of SQL update statements, the customer data shared in the database based on the campaign results data.
Description
1 ADAPTIVE MARKETING USING INSIGHT DRIVEN CUSTOMER INTERACTION CROSS REFERENCE TO RELATED APPLICATION 5 This application is related to commonly assigned co-pending patent applications "Standardized Customer Application And Record For Inputting Customer Data Into Analytic Models", Attorney Docket No. 60021-378101 and "Multi-Dimensional Segmentation For Use In A Customer Interaction", Attorney Docket No. 378001, both filed November 22, 2002, all of which is incorporated herein by reference. This 10 application is also related to commonly assigned co-pending U.S. Patent Application Serial No. 10/014,840, filed October 22, 2001, and titled "Real-Time Collaboration and Workflow Management for a Marketing Campaign", all of which is incorporated herein by reference. 15 FIELD OF THE INVENTION The invention relates to adaptive marketing, and more particularly to adaptive marketing using insight driven customer interaction. BACKGROUND OF THE INVENTION 20 Many businesses use a campaign process to deliver marketing offers to a variety of consumers. The campaign process may be, for example, by telephone or byimags 4 mailing. In order to define the campaigns to execute, the business may gather aiid aggregate information about their customers from a variety of data sources, both froi within their company as well as from third party data providers. After gathering the 25 consumer information, the businesses may decide to separate customers into groupings, customer segments, which have similar characteristics. The businesses may then create a specific list of consumers that the businesses hope will respond positively to. the campaign. Sometimes, these lists may be produced using generalized marketing response models - models developed on generalities about the firm's customers rather 30 than specifics about likely customer response to forthcoming campaign offers. These general models are sub-optimal. But more often, the lists are purchased from third party vendors, or extracted from internal databases using SQL-based rules. Not infrequently, telemarketing relies simply on lists of bare telephone numbers selected 2 from particular area codes and exchanges, with no information about the prospect until the contact is actually established. This process typically can be time consuming and deliver sub-optimal results. Businesses typically employ personnel to search for the consumer information. The 5 personnel may individually search a number of disparate databases attempting to gather the consumer information. This could include information that helps to identify the customer (e.g., name, address, phone, electronic mail address, etc.), information on products or services the customer has purchased in the past, and any additional contextual information captured during past contacts with the customer. Oftentimes, 10 this information is stored in disparate databases in inconsistent formats, making it very difficult to formulate a total, integrated view of a customer. The databases may also contain stale data that produces poor or even erroneous results. Businesses may attempt to purchase additional information about existing or prospective customers from third party data providers (e.g., Equifax, etc.). Types of 15 information purchased may include demographic data (e.g., income level, house size), lifestyle data (e.g., activities the customer participates in, etc.), and interests (e.g., information indicating the customer enjoys eating at restaurants, going to see movies, etc.). Oftentimes, businesses find it challenging to integrate externally purchased data with their own customer data. When data is merged from multiple data sources, 20 sophisticated programming skills are required to link records as well as to aggregate information and calculate values that could be useful to predict customer behavior. Further, the extraction of data from multiple sources to drive analytical modeling can be a very laborious, time consuming process given the number ofjoins that have to be written. Oftentimes, businesses do not have common extract procedures meaning that 25 new extract routines have to be written each time a new form of data analysis needs to be performed. More advanced database marketers make heavy use of analytics and modeling. Customer segmentations based on commercially available demographics, lifestyle, and life-stage data are often used to help define campaigns. These data are also used to 30 target individuals. Unfortunately, because these data are usually compiled at the zip code or census-tract level, application to individuals for targeting is subject to a great deal of error. Propensity models (models comparing attributes of prospect lists to attributes of existing customers) are often developed by businesses and used to develop 3 targeting lists of persons who look like existing customers, hence may have a greater propensity to respond to the business' marketing campaigns. Some more sophisticated businesses are able to develop response models (models based on respondents to actual campaigns); these models tend to outperform the other list generating methods. 5 However, these more sophisticated models require more sophisticated methods and better data. The cost of developing these models can be high. For example, a typical model development process may require two or three people and four to twelve weeks (i.e., 12 - 36 people-weeks) to extract the required customer data and build an analytic model. Then developing a scoring algorithm may 10 take a person four additional weeks. Thus, targeting models are costly. The cost and time required for model development encourages the development of generalized marketing models that are often used for a year or more. Generalized models are commonly outperformed by as much as one hundred percent (100%) by models developed specifically for a particular campaign or offer. Over time, models degrade in 15 performance, but are often used long after their performance peak. This results in diminished marketing returns and often results in abandonment of the use of models for targeting. A second problem is that the data used to create the predictive models and ultimately define and execute the marketing campaigns is old by the time the models are run, leading to out of date model results and poor offer acceptance rates for the 20 resulting marketing campaigns. The time-consuming conventional modeling and marketing processes cannot support rapid test and learn iterations that could ultimately improve offer acceptance rates. After completing a marketing campaign, the personnel may gather the results of the campaign to determine a success rate for the campaign. The results, however, are 25 typically not effectively fed back into the customer information database and used to re-analyze predictive customer behavior. Without an effective closed-loop, businesses lose the ability to retrain their analytical models and improve their campaigns by defining campaigns that have a greater return. The effect of the previously described issues extend beyond marketing 30 campaigns to all forms of interaction. A business' inability to execute an effective, closed loop process to tailor their marketing campaigns affects all forms of customer interaction. Ideally, a business should strive to deliver the right message to the right customer through the best channel. Customers who are the target of an outbound 4 marketing campaign should be able to receive the same offer should they interact with the business through any interaction channel (e.g., web, phone, retail branch, etc.) to perform a service transaction, sales transaction, etc. However, since traditional methods prevent the business from quickly 5 generating reliable, targeted offers for customers based upon predictive analytical models and refined through rapid test and learn iterations, they are unable to deliver optimized marketing offers tailored to their customers and prospects across all forms of customer interaction; best offer to the right customer through the best channel. 10 SUMMARY OF THE INVENTION In one aspect, the present invention provides a computer implemented method for insight-driven, adaptive marketing, including extracting customer data using an extraction module of a computer system 15 for a plurality of customers from at least one database of customer data using a Customer Analytic Record (CAR) application, wherein the CAR application uses the capabilities of a database management system and a structured query language (SQL, defining a targeted marketing campaign by a marketing campaign defining module, the targeted marketing campaign 20 including a selected targeted marketing offer, a selected targeted interaction channel, and a targeted set of customers most likely to accept the targeted marketing offer via the targeted interaction channel, wherein the targeted set of customers is determined by selecting, through the CAR application, a random selection of the plurality of customers, segmenting a sample set of 25 the customers within the random selection into a plurality of customer segments, describing each of the customer segments, wherein the customer segments are created based on similar characteristics among multiple customers with lift values indicating a likelihood of positive response by a selected customer segment to a selected marketing offer via the targeted 30 interaction channel, selecting, based on the lift values, a prospect set from the customer segments for potential inclusion in a selected marketing campaign, assessing with a predictive model a likelihood of acceptance of 5 each customer in the selected prospect set of the selected marketing offer via the targeted interaction channel, scoring each customer in the selected prospect set with a customer score indicating the likelihood of acceptance, and selecting the targeted set of customers based on the scoring, and 5 executing, by a processor, the targeted marketing campaign by offering the targeted marketing offer to the targeted set of customers via the targeted interaction channel, gathering, using a campaign tracking module, campaign results data from the executed targeted marketing, the campaign results data including a count of customers accepting the targeted marketing offer via the 10 targeted interaction channel, and a length of time between the making of the targeted marketing offer and the accepting of the targeted marketing offer, training, the predictive model with the campaign results data, wherein the training is based on how the customers respond to various types of the marketing offers and to various types of the interaction channels used in the 15 targeted marketing campaign, and updating, via a series of SQL update statement, the customer data shared in the database based on the campaign results data. In another aspect, the present invention provides a computer implemented system for insight-driven, adaptive marketing, including a 20 processor, a memory storing instructions operable with the processor for performing marketing activities, the instructions structured throughout a plurality of modules said modules including a customer data extracting module configured to extract customer data for a plurality of customers from at least one database of customer data using a Customer Analytic Record 25 (CAR) application, wherein the CAR application uses the capabilities of a database management system and a structured query language (SQL), a marketing campaign defining module configured to define a targeted marketing campaign including a selected targeted marketing offer, a selected targeted interaction channel, and a targeted set of customers most likely to 30 respond to the targeted marketing offer via the targeted interaction channel wherein the targeted set of customers is determined by a customer data selecting module configured to select, through the CAR application, a 5a random selection of the plurality of customers, a segmenting module configured to segment a sample set of the customers within the random selection into a plurality of customer segments, wherein the customer segments are created based on similar characteristics among multiple 5 customers, a segment description module configured to describe each of the plurality of customer segments with lift values indicating a likelihood of positive response by a selected customer segment to a selected marketing offer via the targeted interaction channel, a prospect set selecting module configured to select, based on the lift values, a prospect set from the plurality 10 of customer segments for potential inclusion in a selected marketing campaign, a predictive model module configured to assess, a likelihood of acceptance of each customer in the selected prospect set of the selected marketing offer via the targeted interaction channel, a prospect set scoring module configured to score each customer in the selected prospect set with 15 a customer score indicating the likelihood of acceptance, a customer selection module configured to select the targeted set of customers based on the customer score, an execution module configured to execute the targeted marketing campaign by offering the targeted marketing offer to the targeted set of the customers via the targeted interaction channel, a campaign 20 tracking module configured to gather campaign results data from the executed targeted marketing campaign, the campaign results data including a count of customers accepting the targeted marketing offer via the targeted interaction channel, and a length of time between making of the targeted marketing offer and the accepting of the targeted market offer, a training 25 module configured to train the predictive model with the campaign results data, wherein the training is based on how the customers respond to the various types of marketing offers and to various types of the interaction channels used in the targeted marketing campaign, and an updating module for updating, via a series of SQL update statements, the customer data 30 stored in the database based on the campaign results data. In another aspect, the present invention provides a computer program for program embodied on a non-transitory computer readable medium for 5b performing marketing activities, wherein the computer program instructs a processor to perform a method including accessing instructions for a plurality of software modules stored in a memory, wherein the plurality of software modules include a customer data extracting software module and a 5 marketing campaign defining module, accessing customer data stored in a database for a plurality of customers, and executing, by the processor, the instructions to perform the steps of extracting the customer data from the database by the customer data extracting software module using a Customer Analytic Record (CAR) application, wherein the CAR application uses the 10 capabilities of a database management system and a structured query language (SQL), defining a targeted marketing campaign by the marketing campaign defining module, the targeted marketing campaign including a selected targeted marketing offer, a selected targeted interaction channel, and a targeted set of customers most likely to accept the targeted marketing 15 offer via the targeted interaction channel, wherein the targeted set of customers is determined by selecting, through the CAR application, a random selection of the plurality of customers, segmenting a sample set of the customers within the random selection into a plurality of customer segments, wherein the customer segments are created based on similar 20 characteristics among multiple customers, describing each of the plurality of customer segments with lift values indicating a likelihood of positive response by a selected customer segment to a selected marketing offer via the targeted interaction channel, selecting, based on the lift values, a prospect set from the customer segments for potential inclusion in a selected 25 marketing campaign, assessing with a predictive model a likelihood of acceptance of each customer in the selected prospect set of the selected marketing offer via the targeted interaction channel, scoring each customer in the selected prospect set with a customer score indicating the likelihood of acceptance, selecting the targeted set of customers based on the scoring, 30 and executing the targeted marketing campaign by offering the targeted marketing offer to the targeted set of the customers via the targeted interaction channel, gathering campaign results data from the targeted 5c marketing campaign by a campaign tracking module, the campaign results data including a count of customers accepting the targeted marketing offer via the targeted interaction channel, and a length of time between making of the targeted marketing offer and the accepting of the targeted marketing 5 offer, and training the predictive model with the campaign results data, wherein the training is based on how the customers respond to the various types of marketing offers and to various types of the interaction channels used in the targeted marketing campaign, and updating, via a series of SQL update statements the customer data stored in the database based on the 10 campaign results data.
6 5 BRIEF DESCRIPTION OF THE DRAWINGS Fig. IA is a block diagram of a method for adaptive marketing using insight driven customer interaction according to one embodiment of the invention. Fig. IB is a table illustrating segmented customer data according to one 10 embodiment of the invention. Fig. 1 C is a table illustrating the definition of a marketing campaign according to one embodiment of the invention. Fig. 2 is a block diagram of a system for adaptive marketing using insight driven customer interaction according to one embodiment of the invention. 15 Fig. 3 is a block diagram of a method for segmentation and predictive modeling for adaptive marketing using insight driven customer interaction according to one embodiment of the invention. Fig. 4 is a block diagram of a system for segmentation and predictive modeling for adaptive marketing using insight driven customer interaction according to one 20 embodiment ofthe invention. Fig. 5 is a block diagram of a method for creating a standardized input for analytic models for adaptive marketing using insight driven customer interaction according to one embodiment of the invention. Fig. 6 is a block diagram of a system for creating a standardized input for 25 analytic models for adaptive marketing using insight driven customer interaction according to one embodiment of the invention. Fig. 7A is a method for developing multi-dimensional segmentation according to one embodiment of the invention. Fig. 7B is an illustration of relationships among characteristics that may drive 30 customer behavior. Fig. 8 is a system for developing multi-dimensional segmentation according to one embodiment of the invention.
7 Fig. 9 is a method for adaptive marketing using insight driven customer interaction according to one embodiment of the invention. Fig. 10 is a system for adaptive marketing using insight driven customer interaction according to one embodiment of the invention. 5 DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS The invention relates to adaptive marketing using insight driven customer interaction. The invention can be extended to other customer interactions in which insight improves the interaction between an enterprise and its customers. Fig. IA 10 illustrates a method for adaptive marketing using insight driven customer interaction according to one embodiment of the invention. Initially, a database may be created to store customer data, step 10. The customer data may be collected through internal, external, and/or business partner data sources. The database used for storing customer data may be any known data storage mechanism, generally a relational database often 15. referred to as a data warehouse. According to one embodiment of the invention, the data warehouse platform used for storing the customer data is powered by NCR's Teradata system. The data may be extracted from the database, step 12. In one embodiment, a CAR/PAR application may be used to extract data from the database and then 20 transform, aggregate, and combine the data into standardized virtual flat file records for each customer, such as a customer analytic record (CAR) for existing customers and a Prospect Analytic Record (PAR) when the targeted consumers are not current customers of the company. The step of transforming the data may include custom transformations to fill the calculated CAR fields. The CAR may be used as input to 25 descriptive and predictive models to determine how consumers are likely to respond to marketing offers. The models may also be used to predict a likelihood of attrition or other behaviors. According to one embodiment of the invention, the CAR may be produced via a view. A database view is a virtual query. The CAR is usually written as a set of views 30 that do all the "flattening out" of the data and also computes the ratios, etc. that may be used in modeling. Consider the following SQL statement: select custid, acct-balanceRTM, 8 max( accountbalance) from txntable where behavior-segment = I and acct_balanceRTM < 1 5 order by custid; This statement may be executed by a database system to return a sorted list of customer ids, account balance ratio-to-mean and their maximum account balances. The result may be a table if left in the database or a flat file if exported. The query may be changed to generate a view using, for example, the following: 10 create view CARrtmmaxbalance as select custid, acctbalanceRTM, max( accountbalance) from txntable 15 This creates a virtual table or "view" in the database. The CAR prefix indicates that the view CARrtmmaxbalance is a component of the overall CAR application. A user may now query CARrtmmaxbalance as though this component was a table in the database: select * from CARrtmmaxbalance 20 where behaviorsegment = I and acctbalanceRTM < I The view looks just like a table to the user. Because CARrtmmaxbalance is a view, a query automatically returns the latest values that have been loaded into the database. If the information in the view was stored in a permanent database table (of 25 the same or different name), a special update process would be necessary in order to capture changes made to a base table, txn table. The optimal configuration for the CAR development is to define is a set of dynamic views of the customer data within the data warehouse. This enhances data integrity in the resulting analytic data set. The CAR/PAR data may include identification and behavior fields. The 30 identification fields may be for household information such as a household identifier, address, and phone number and household individual information such as name and electronic mail address. The behavior fields summarize transaction information and contain statistical transformation of this data for analytical use. Examples include 9 account summary data, ratio to mean and z-score calculations, moving average and moving difference calculations over a specified period of time, log transformations and slope calculations. The CAR may also include demographic fields The demographic fields may 5 include, for example, income level and house size. The demographic fields also include fields pertaining to lifestyle and interest. The lifestyle fields may include, for example, whether the individual is a domestic, enjoys the outdoors such as hiking, biking, camping, walking, running, etc., and whether the individual is athletic or enjoys sports. The interest fields may indicate, for example, whether the individual likes to 10 travel, play video games, drink wine, play sports, watch sports, read, etc. Preferably, each of-the fields and data included in the CAR may be cross-referenced to an individual's household. This may be performed by linking a household identifier to an individual's identifier. The CAR may also include a contact history. The contact history may include 15 information related to promotions offered to a customer, promotions redeemed by the customer, elapsed time for the offer to be redeemed, and telephone calls made or emails sent to the customer by a contact center or received from the customer by a contact center. The contact center may be, for example, an on-line support system, a sales representative center, etc. 20 The CAR also preferably includes model scores. The model score fields may include an individual identifier such as a primary key, a model primary segment or decile such as segment number or predictive model score decile, and a model score such as a predictive model score or a response model score. The model score may also include a household identifier that may be used to cross-reference a customer to a 25 household. In addition to identification fields, behavior fields, demographic fields, contact history fields, and model score fields, the CAR also preferably includes fields representing product ownership information. Product ownership includes a complete listing of all of the products and services that a customer has previously purchased from 30 the business. Such product ownership information in the CAR record provides a more complete picture of each customer and may be used in determining which offers to extend to certain customer segments.
10 Segmentation may be performed based on the data extracted, step 14. The segmentation process may establish customer segments, for example, 6-9 groups of customers that are used to drive the campaign strategy and design. The customer segments may be created based on similar characteristics among a plurality of 5 customers. Segmentation is usually based on a random sample set of customer records extracted through the CAR views. For example, the data extracted from the database may be for approximately thirty (30) million customers. Segmentation, however, may be performed only on a percentage, for example, ten (10) percent, of the customer records. Therefore, segmentation may be performed for three (3) million customer 10 records instead of thirty (30) million. Preferably, the three (3) million customer records are randomly selected, however, any manner of selecting the customer records may be used. Although a fewer number of customer records may be used for segmentation, by using a random sample set of a percentage of customer records, a fairly accurate depiction of the customers may still be obtained. Some technologies enable 15 segmentation of the complete customer record. This may be advantageous for segmentation, however, a good random sample will usually be much easier to handle and can produce statistically valid results. Once the customer segments have been defined, they are profiled in terms of behavior, value, and possibly demographic, lifestyle and life-stage data. This allows the 20 business users to understand and "name" the segments. If the business user cannot name the segments, then the process is repeated until he can. This ensures that the segments are statistically valid and have meaningful business value. At this time, all of the customer records in the database are updated to reflect the segmentation results. Fig. 1B demonstrates a sample segmentation profile. Segments have been defined as 25 customers having mortgages only, big savers, small savers, normal savers, new customers, and entrenched customers. Each segment is described through a description, percentage of sample population falling within the segment, a lift value, asset accounts, loan accounts, tenure, transaction activity, demographics, etc. After segmenting the customer records, a marketing campaign may be defined 30 for one or more customer segments based upon what's known about the customers in the segment, step 16. For example, referring to Fig. iB, the lift value may indicate a likelihood for a customer segment to redeem an offer. The lift value may be calculated by dividing a number of accounts held by a predetermined number of customers 11 divided by the number of customers. The lift value provides a factor that may be used to target specific segments and reduce the total number of customers to whom an offer is to be communicated. A reduction in costs is achieved because fewer telephone calls or mailings are necessary to achieve substantially the same or higher response. For 5 example, if a marketing campaign results in 100 new accounts for a bank out of the 1,000,000 customers contacted with the marketing offer, 0.0001 is the calculated lift. By using adaptive marketing through the present invention, 100 new accounts may be opened by contacting just the 400,000 customers most likely to respond (as determined by the model). In Fig. I B, because segments 2, and 6 have a high lift value, these 10 segments may be targeted for a marketing campaign. By using adaptive marketing, a 0.0025 lift results, which means that for the same number of sales, 600,000 fewer customers needed to be contacted. Because, each contact has a cost, a reduction of 600,000 contacts in an outbound telemarketing context may save millions of dollars in marketing costs. 15 Next, a predictive model may be created/trained to determine the specific offers to provide to customers based on the data extracted, step 18. Predictive models may be created when a first marketing campaign is defined. Predictive models are often developed using statistical methods like logistic regression, but data mining technologies like neural nets, decision trees may also be used. Fig. IC is a table that 20 provides a description, hypotheses, and potential offers for segments 2 and 6. Prescriptive models may be defined and executed to determine which of these offers to provide and which specific customers in each segment should be targeted. After the first campaign is executed, the predictive model may be trained using insight obtained from the first marketing campaign. Such training of analytic models is well known in 25 the art, as are the tools to accomplish the modeling. For example, software developed and sold by KXEN, Inc. (Knowledge Extraction Engines) of San Francisco, CA may be used. After training a predictive analytic model, a marketing campaign for one or more customer segments may be executed, step 20. The marketing campaign may be 30 run by communicating offers to the customers through a customer interaction. The customer interaction may be, for example, a telephone call to the home of a customer or a mailing of an offer to the customer's home.
12 As the campaign is executed, the results of the campaign may be captured, step 22. It should be noted that the invention reduces marketing campaign cycle times and provides adjustments for competitive positioning in a changing market. The invention achieves this by not requiring an arduous data extraction, transformation, modeling, and 5 scoring process to have to be repeated each time a marketing campaign is desired as discussed above. Marketing cycle times are reduced by simplifying the extraction and transformation of all the customer data elements needed for analytic modeling. The CAR application includes all the necessary logic to extract the data from a database and all of the transformations needed to create additional customer data elements for 10 segmentation and predictive modeling. Pre-built data models can also be re-used or leveraged since the models all rely on the same standard data inputs. The invention uses insight developed from prior campaigns to update a customer database. The results may include, for example, the number of offers redeemed, which customers redeemed the offer, the time elapsed between the offer presentation and 15 redeeming of the offer, and other information. The database may then be updated automatically with the results of the marketing campaign, step 24. This update may be done via a series of SQL update statements, for example. The marketing campaign results provide insight regarding a customer's behavior toward redeeming offers. The insight may be, for example, what types of offers a customer is likely to accept, which 20 customers are more inclined to accept an offer, how quickly a customer redeems an offer, etc. This information may be used to refine further customer interactions to increase the number of offers accepted. Thus, through the interaction with the customer, insight (knowledge) is gained that is used to improve future interactions, such as marketing campaigns. This may be performed by repeating the steps of the 25 method for adaptive marketing using insight driven customer interaction. Based on new customer data extracted (including part or all of the updated data that is the insight gained from the prior interaction), the predictive model may be trained resulting in a more accurate picture of anticipated customer responses to marketing offers. The adaptive model is usually developed to support each new campaign. Due to the 30 rapidity of model development, enabled by this process, models can be developed to support each new campaign, then re-trained (adapted) to provide a mid-campaign correction if necessary. This process may be repeated for any desired number of customer interactions.
13 Fig. 2 illustrates a system 50 for adaptive marketing using insight driven customer interaction according to one embodiment of the invention. The system 50 may include a database creating module 52 that provides a database for storing customer data. A customer data extracting module 54 may be used to extract, 5 transform, and format the customer data from the database for segmentation and training a predictive model. A customer segmenting module 56 may be used to segment customer records that provide a profile of a customer into segments of customers that have similar characteristics. A campaign defining module 58 may be used to define a campaign for one or more customer segments identified by customer 10 segmenting module 56. A predictive model creating/training module 60 may be used to create/train a predictive model for determining how a customer may react to a marketing offer. The predictive model may be created when a first marketing campaign is defined and trained when subsequent marketing campaigns are defined using insight obtained from the first marketing campaign. The predictive model may 15 be used to predict customer behavior regarding one ormore offers communicated to the customer. The predictive model may indicate, for example, that the customer is highly likely, likely, unlikely, or very unlikely to accept the offer. This assists in defining targeted treatments, offers, and marketing campaigns based upon an integrated view of the customer resulting in improved marketing campaigns. 20 Based on the predictive model, a campaign executing module 62 may be used to define and execute one or more campaigns to be communicated to the customer. The campaigns may include, for example, telephoning or mailing offers to the customers that are likely to accept the offer. As a campaign is executed, the results of the campaign may be gathered. The 25 results may include, for example, the number of offers accepted, the identification information for the customers who accepted the offers, the length of time between initiating the customer interaction and acceptance of the offer, and other information. The campaign results may be gathered using campaign results gathering module 64. The database storing the customer data may then be updated with the campaign results 30 using database updating module 66. The campaign results may be used to refine additional customer interactions with the customers. For example, the customer results may show that a particular offer was not well received by the customers or that a particular segment of customers redeemed a large percentage of a particular offer.
14 Therefore, based on this information, additional customer interactions may be refined to target the customer segment that accepted a large percentage of the offers. The offer not well received by the customers may be altered to induce future acceptance of the same or a similar offer or possibly canceled. The offers may be, for example, coupons 5 to be used at restaurants, movie theaters, amusement parks, etc. or for services rendered. Fig. 3 illustrates a method for training a predictive analytic model according to one embodiment of the invention. A predictive analytic model may be created/trained by choosing a customer data sample set, step 100. The sample set may be, for example, 10 a percentage of customer records from a total number of customer records such as ten (10) percent. The sample set may be segmented to divide customers into segments having similar characteristics, step 102. The customers associated with each segment may be given a score to identify to which customer segment each customer belongs, step 104. A marketing campaign for one or more of the customer segments may be 15 defined, step 106. The marketing campaign may include one or more offers to be delivered to the customers. A predictive marketing model is constructed to assess each customer's propensity to respond to an offer in the targeted customer segments, step 108. The results from running the marketing model are used to score prospective 20 customers, step 110. The prospect set may be used for detennining which offers are to be communicated to which customers. According to one embodiment of the invention, KXEN technology may be used to build the marketing model and score the prospect set. According to one embodiment, the customers receiving the highest score may be deemed most likely to accept a particular offer. A lower score may indicate a lower 25 acceptance response to the offer. Therefore, offers are preferably communicated to customers receiving high scores. Fig. 4 illustrates a system 150 for training a predictive analytic model according to one embodiment of the invention. The system 150 may include a customer data sample set choosing module 152. The customer data sample set choosing module 152 30 may choose a sample set of customer data to be used to group customers into segments. A customer segmenting module 154 may be used to segment customer records according to similar characteristics. A customer segment scoring module 156 may be used to update customer records to reflect their assigned segment created by 15 customer segmenting module 154. Based on the profiles of each of the defined customer segments, a marketing campaign may be defined using campaign defining module 158. The marketing campaign may propose which offers are to be targeted to which customer segments. 5 Next, a marketing model is built to assess customers' propensity to respond to proposed offers using the marketing model building module 160. The marketing model may then be used to score a prospect set of customers using prospect set scoring module 162. Based on the results generated by the prospect set scoring module 162, the customers that receive a high score may be communicated an offer. 10 Fig. 5 illustrates a method for creating a standardized input for analytical models according to one embodiment of the invention. A database for storing customer data may be initially created, step 200. A CAR may be created to extract, transform, and format the customer data to be used as input for an analytic model, step 202. The CAR provides a database object that may include one or more database views and 15 dynamic and temporary tables. Dynamic tables are automatically developed at the beginning of a query and destroyed when completed whereas temporary tables are usually pre-loaded with data and persist after the query has finished. Dynamic and temporary tables are typically used for performance reasons or to store data in a certain manner. 20 The customer data may be extracted from the database by running one or more queries on the CAR, step 204. The SQL queries against the CAR may themselves create additional variables by operating on the data returned by the CAR queries, step 206. Some examples of these transformations are slopes and ratio calculations. Slopes may be calculated when a customer record contains time series transaction data (e.g., 25 number of transactions per week, account balance per month, etc). Plotting these trends on a time graph allows a straight line to be fitted through the points. The slope of the line is an indicator of whether the rate of transactions is increasing or decreasing over time. Ratios provide another good way to analyze data (e.g., the ratio of one customer's account balance compared to the average balance of all customers in the 30 segment). These variables provide additional predictive power to models. For example, ratios-to-means and slopes are very important inputs to retention models. When the slope of a customer's account balance is decreasing at a high rate, determined by the ratio of decline compared to the mean, it's a good predictor that the customer is 16 planning to take his or her business to another establishment. If the business recognizes this trend in advance and identifies that this is a high value customer, it can take measures to attempt to retain the customer. The. CAR is a method of setting up virtual stored queries that include table 5 fields as well as calculated fields created using capabilities of a Data Base Management System (DBMS) and a structured query language (SQL) such that the stored queries present to the user a virtual flat file that may be used as input to an analytic engine. The capabilities of the DBMS and SQL may include, for example, (1) functional objects available within SQL to perform certain statistical and mathematical operations 10 on data retrieved from the database: e.g., average, standard deviation, ranking, moving averages, regression, logarithmic transformation, sequence analysis, etc.; (2) ability to process and complete queries that may contain one or more mathematical or statistical operations against a 3rd Normal Form database, which implies the ability to join many tables to produce the query answer sets; (3) the capability to express these complex 15 queries as an object in the database, either as a view or as a pre-defined function usable in a SQL query; (4) the capability to nest these views and otherwise combine them into other higher-level views or in ad hoc SQL queries; (5) the ability to create dynamic or temporary relational tables on the fly (during the course of execution of queries); (6) the ability to define and query these views and other data objects within a very large 20 relational database that may be dozens of terabytes in size, that may contain tables with billions of rows of data. If additional variables are created, from CAR data or database data during modeling or analysis processes, the CAR may be modified to include the additional variables, step 208. In this manner, the additional variables become part of the CAR 25 and are available for future modeling and analytic requirements. Fig. 6 illustrates a system 250 that may be used for creating a standardized input for analytical models according to one embodiment of the invention. The system 250 may include a database creating module 252. The database creating module 252 may provide a database for storing customer data. A CAR may be created for the purpose of 30 extracting, transforming, and formatting the customer data to be used as input for an analytic model using CAR creating module 254. The CAR preferably provides a database object that may include one or more views. A customer data extracting module 256 may be used to extract the customer data from the database using the CAR.
17 The customer data may be extracted by running one or more queries against the view(s). Based on the data queried, additional variables may be created by the CAR view(s) using additional variable creating module 258. If additional variables are created, the CAR may be modified to include the additional variables using CAR 5 modifying module 260. The CAR may then be used to provide standardized input for analytical models. The CAR preferably includes all of the information necessary to predict customer behavior and define targeted customer interactions with a customer. Fig. 7A illustrates a method for segmenting data representing a plurality of customers for use in a customer interaction according to one embodiment of the 10 invention. Customer data may be segmented according to a first characteristic, step 300. The first characteristic may include, for example, behavior data. To execute behavior segmentation, preferably, only the variables of the CAR, both direct and derived, that reflect a customer's behavior are used. Examples of such variables that reflect behavior are the number of transactions, rate of increase of the number of 15. transactions, average value per transaction, etc. Preferably, demographic variables are not used for generating behavioral segmentation. After the segments have been identified, the segments may be profiled with all of the variables including any demographic variables. The customer data may then be segmented according to a second characteristic, 20 step 302. The second characteristic may be, for example, value data. For the value segmentation, the variables that are used are preferably indicative of customer value. In one embodiment, the customer lifetime value may be used as the driving variable for this segmentation. Other value indicators like profitability, etc. may be used. A determination may then be made regarding whether the customer data is to be 25 segmented according to a third characteristic, step 304. If the customer data is not to be segmented according to the third characteristic, a two-dimensional matrix for cross segmenting the customers by both behavior data and value data may be generated, step 306. To do this, the value segments and the behavior segments may be "overlayed" and the behavior-value crossed segments may be profiled to get a joint view. If, 30 however, a determination is made that the customer data is to be segmented according to a third characteristic, the customer data may segmented by a third characteristic, step 308.
18 After segmenting the customer data according to a third characteristic, a three dimensional matrix for cross-segmenting a plurality of customers by the first, second, and third characteristics may be generated, step 310. The segmentation may be expanded to many data types generating a multi-dimensional hypercube that more 5 completely characterizes the customers. In the behavior-value segmentation embodiment of the invention, the segmentation may be performed by using a cluster analysis algorithm to identify latent clusters in the data. Most algorithms typically identify clusters that have a low ratio of within cluster variability to across cluster variability using some standard distance 10 metrics. According to one embodiment of the invention, the algorithm used is driven by a business objective. This in turn permits the distance metrics that are used in the cluster analysis to be calibrated in the context of the stated business objective. In other words, the invention generates clusters that are more closely aligned with the business case and is therefore a semi-supervised segmentation as opposed to a completely 15 unsupervised segmentation. The approach to two dimensional modeling described above regarding behavior and value data may be applied to other characteristics that may influence customer behavior, such as, for example, attitude, satisfaction, brand experience, brand attachment, brand utility, and category involvement. Attitude may reflect a holistic 20 view of a firm held by a customer. Satisfaction may be a day-to-day satisfaction resulting from current transactions between the customer and a firm. The brand experience may be the cumulative effect of day-to-day satisfaction. Brand attachment may be an attitude or feeling toward a brand by a customer. For example, brand attachment may be strongly influenced by advertising. Non-customers such as 25 HARLEY DAVIDSONTM brand motorcycle aficionados may have a strong brand attachment. Attachment may be reinforced positively or negatively by brand experience. Brand utility may be a need for goods or services provided by a firm. The brand utility may be affected by lifestyle and life-stage factors. Category involvement may be a need for specific products and/or services. Category involvement may also be 30 affected by lifestyle and life-stage factors. Applicants of this invention have found that there may be relationships between these characteristics that may ultimately affect customer behavior. Fig. 7B illustrates that: attitude drives behavior; behavior drives value; the relationship experience felt by 19 the customer impacts customer satisfaction; satisfaction affects brand experience; brand experience affects brand attachment which is also impacted by advertising; life-stage and lifestyle affect brand utility and involvement by category; attitude is affected by brand experience, brand attachment, brand utility, and involvement by category; etc. 5 Fig. 8 illustrates a system 350 for segmenting data representing a plurality of customers for use in a customer interaction according to one embodiment of the invention. The system 350 may include a first characteristic segmenting module 352. The first characteristic segmenting module 350 may segment the customer data according to a first characteristic. The first characteristic may be, for example, 10 behavior, attitude, value, satisfaction, brand experience, brand attachment, brand utility, or category involvement. A second characteristic segmenting module 354 may be used to segment the customer data according to a second characteristic. The second characteristic may be, for example, any of the first characteristics not segmented. A third characteristic determining module 356 may be used to determine whether the 15 customer data is to be segmented according to a third characteristic. If a determination is made that the customer data is not to be segmented according to a third characteristic, a two-dimensional matrix for cross-segmenting a plurality of customers by the first and second characteristics may be generated using two-dimensional matrix generating module 358. The segmentation may be expanded to many data types generating a 20 multi-dimensional hypercube that more completely characterizes the customers. If third characteristic determining module 356 determines that the customer data is to be segmented according to a third characteristic, a third characteristic segmenting module 360 may be used to segment the customer data according to the third characteristic. The third characteristic may be any of the characteristics not segmented 25 by the first characteristic segmenting module 352 and the second characteristic segmenting module 354. After segmenting the customer data according to the third characteristic, a three-dimensional matrix generating module 362 may be used to generate a three-dimensional matrix for cross-segmenting the plurality of customers by the first, second, and third characteristics. 30 Fig. 9 illustrates a method for adaptive marketing using insight driven customer interaction according to one embodiment of the invention. A database may be created, step 402. The database may be used to store customer data. The data may be cleansed (such as by removing duplicate records), step 404, and individualized, step 406.
20 Individualizing the data may include providing an identifier to customer data that indicates the particular customer for whom that data was gathered. This enables the data to be cross-referenced easily according to a customer identifier. The customer data may be retrieved from plurality of databases, step 408. The customer data may then be 5 loaded into, for example, a data warehouse, step 410. The customer data may be appended with demographic data for each customer gathered, step 412. The demographic data may be gathered from external sources. When a business wants to optimize the value of its customer relationships, it must learn to tailor customer interactions to the needs of its customers. To do this, a company needs to obtain an 10 integrated view of the customer, segment its customers into groups, and assess how customers behave and will respond to various offers. Selecting data using the CAR may be the first step in the process, step 414. The data selected may be used to create a sample set of customer records for segmentation as well as drive predictive modeling. A sample set of customer records may be created to reduce a number of 15, customers for which customer segmenting may be performed, step 416. Preferably, the sample set is chosen at random, however, other methods may also be used. By using a random sample set, fewer customer records are used for segmentation while generating a substantially accurate depiction of customers. The customer records may be divided into segments, step,418. The customers within the segments may have one or more 20 similar characteristics. The customers within the segments may le given a score, step 420. The score may be based on the segmentation results and quantitatively represent a customer.. The marketing offer(s) to be delivered to the customers may be determined, a delivery channel determined, and the segments to be targeted identified, step 422. A campaign may be defined comprising of the offers proposed to targeted segments, step 25 424. The campaign may include marketing offers such as coupons or other incentives for purchasing a particular product or service. A predictive model may be built to predict how customers may react to the marketing offers and which customers in a particular segment should receive the offers, step 426. The predictive model may provide propensity scores for the customers. The 30 propensity scores may indicate which customers are more likely to accept a marketing offer. The propensity scores may then be applied to the customer data, step 428. A customer list may be created to identify which customers should be given the offer, step 430. The campaign may then be executed, step 432. This may include communicating 21 the marketing offers to the customers via a customer interaction. The customer interaction may be a telephone call with a telemarketer, an electronic mail message, an offer received via regular mail, etc. Depending on how a customer reacts to the marketing offer, the marketing campaign may be adjusted accordingly. For example, if 5 the customer provides a telemarketer with information that the telemarketer believes will induce the customer to accept a marketing offer, the telemarketer may customize the marketing offer for that customer. The campaign results may be tracked, step 434. Tracking the campaign may include determining which customers have redeemed a marketing offer, which 10 marketing offer was redeemed, and the time elapsed between communicating the marketing offer to the customer and when the offer was redeemed. Campaign tracking may also include updating the database with the campaign results. The campaign results may then be used to further refine existing campaigns or to define additional campaigns. After refining existing campaigns and defining one or more additional 15 campaigns, the campaigns may be iterated, step 436. By iterating through campaigns, insight may be developed regarding how a customer may respond to marketing offers. This insight may then be used to-generate more predictive models regarding a customer's behavior toward marketing offers in general or to specific types of marketing offers. Subsequent campaigns may then launched, step 438, using the 20 insight developed such that the subsequent campaigns may produce a higher result of redeemed offers. The method for adaptive marketing using insight driven customer interaction may be repeated as desired to generate additional refined marketing campaigns. Fig. 10 is a block diagram of a system 500 for adaptive marketing using insight 25 driven customer interaction. The system 500 may include a database creating module 502. The database creating module 502 may be used to create a database for storing customer data that may be used for defining a marketing campaign. A data cleansing module 504 may be used to cleanse the data, such as by performing de-duplication. A data individualizing module 506 may be used to individualize the data for each 30 customer that the data refers. The customer data may be retrieved from a plurality of databases using customer data retrieving module 508. A data loading module 510 may be used to load the cleansed and individualized data into, for example, a data warehouse. The data may then be appended with demographic data obtained related to 22 each customer using external demographic data appending module 512. The demographic data may be, for example, obtained from an external data source. A customer data selecting module 514 may be used to select customer data using the CAR. The CAR preferably includes all of the information needed about a 5 customer that may be needed to group customers into segments and prepare predictive models. A sample set of customer records may be created to reduce a number of customers for which customer segmenting may be performed using sample set creating module 516. Preferably, the sample set is chosen at random, however, any method may be used. The customer records may be divided into segments using customer 10 segmenting module 518. The customers within the segments may have one or more similar characteristics. The customers within the segments may be given a score using customer segment scoring module 520. The score may be based on the segmentation results and be used to quantitatively represent the customer. The marketing offer(s) that are to be delivered to customers may be determined along with a marketing 15 channel and target segment(s) using offer, channel, and target segment determining module 522. The marketing channel may be, for example, electronic mail, regular mail, facsimile, telephone call, etc. Based on the offer(s), channel(s), and segment(s) determined, a marketing campaign may be defined using marketing campaign defining module 524. 20 A predictive model may be built using predictive model building module 526. The CAR prepared using CAR preparing module 514 may be used to select the customer data needed as input to the predictive model. The predictive model may be used to predict customer behavior regarding how a customer may respond to particular marketing offers. The predictive model may generate a propensity score for the 25 customers. The propensity score may indicate how likely a customer is to accept a marketing offer. A higher score may indicate that customers within that segment are more likely to accept a particular marketing offer. The propensity score may then be applied to the customer data, step 528. A customer list may be created to identify which customers should receive the 30 offer using customer list creating module 530. The campaign may then be executed using campaign executing module 532. The campaign may be executed, for example, by mailing the offers via regular mail or electronic mail, telephoning the customers, or initiating some other kind of customer interaction.
23 The results of the campaign may be tracked using campaign tracking module 534. The results may include, for example, which marketing offers were accepted, which customers accepted the offers, and the time elapsed between offer and acceptance. This information may be used to iterate additional campaigns using 5 campaign iterating module 536. The results may provide insight regarding customer behavior that may be used to refine additional marketing campaigns to increase the likelihood that a customer will accept the marketing offer. The iterated campaigns may be launched using iterated campaign launching module 538. While the specification describes particular embodiments of the present 10 invention, those of ordinary skill can devise variations of the present invention without departing from the inventive concept. For example, although the invention has been described in terms of a marketing campaign, the invention may be used with any type of customer interaction. For instance, customers who are the target of a marketing campaign may be given an offer if the customers call a particular business with a 15 service request. Similarly, targeted customers may be given an offer when meeting in person with a sales representative of a business to conduct a sales transaction. The marketing promotion may be offered as follows. Assume that a bank has executed the adaptive marketing steps described above to the point of defining a marketing campaign whereby on-line banking customers with a combined family 20 income exceeding $100,000 per year will be offered a Platinum MastercardTM. The marketing offer may be defined in an offer database when the campaign is to be executed. The customers who are the target of the offer may be flagged. This information may be accessed and used when a targeted customer engages with the bank for any sort of transaction. 25 If the customer calls one of the bank's call centers to make a service request (e.g., validate the balance in an account, make an inquiry about a bank statement, etc), the call center agent may be given information that this customer is the target for the Platinum MastercardTM promotion which could be offered after the service request is fulfilled. Similarly, if the customer is servicing an account using an on-line banking 30 application, a web-based application may determine that the customer is the target of the marketing promotion and deliver the offer to the customer. If the customer visits a bank branch to open an account or buy a Certificate of Deposit, a sales agent may 24 determine that the customer is the target of the promotion and offer the promotion to the customer. Therefore, the adaptive marketing flow could affect all forms of customer interaction across multiple customer interaction channels. Note that the result of any 5 interaction may be loaded into the customer data warehouse and later extracted to retrain the analytical models and either define new, improved marketing campaigns or to better target existing campaigns. All forms of interaction may benefit from and contribute to the iterative nature of the adaptive marketing process. While the specification describes particular embodiments of the present 10 invention, those of ordinary skill can devise variations of the present invention without departing from the inventive concept.
Claims (19)
1. A computer-implemented method for insight-driven, adaptive marketing, including: extracting customer data using an extraction module of a computer system for a plurality of customers from at least one database of customer data using a Customer Analytic Record (CAR) application, wherein the CAR application uses the capabilities of a database management system and a structured query language (SQL; defining a targeted marketing campaign by a marketing campaign defining module, the targeted marketing campaign including a selected targeted marketing offer, a selected targeted interaction channel, and a targeted set of customers most likely to accept the targeted marketing offer via the targeted interaction channel, wherein the targeted set of customers is determined by: selecting, through the CAR application, a random selection of the plurality of customers; segmenting a sample set of the customers within the random selection into a plurality of customer segments, wherein the customer segments are created based on similar characteristics among multiple customers; describing each of the customer segments with lift values indicating a likelihood of positive response by a selected customer segment to a selected marketing offer via the targeted interaction channel; selecting, based on the lift values, a prospect set from the customer segments for potential inclusion in a selected marketing campaign; assessing with a predictive model a likelihood of acceptance of each customer in the selected prospect set of the selected marketing offer via the targeted interaction channel; scoring each customer in the selected prospect set with a customer score indicating the likelihood of acceptance; and selecting the targeted set of customers based on the scoring; and executing, by a processor, the targeted marketing campaign by offering the targeted marketing offer to the targeted set of customers via the targeted interaction channel; gathering, using a campaign tracking module, campaign results data from the executed targeted marketing, the campaign results data including a count of customers 26 accepting the targeted marketing offer via the targeted interaction channel, and a length of time between the making of the targeted marketing offer and the accepting of the targeted marketing offer; training, the predictive model with the campaign results data, wherein the training is based on how the customers respond to various types of the marketing offers and to various types of the interaction channels used in the targeted marketing campaign; and updating, via a series of SQL update statements, the customer data shared in the database based on the campaign results data.
2. A computer-implemented method according to claim 1, further including, in a closed loop process, obtaining insight from campaign results data from the targeted marketing campaign, wherein the insight is used to refine and simplify subsequent customer data extraction and customer selection.
3. A computer-implemented method according to either claim 1 or claim 2, further including: obtaining insight from the campaign results data with an insight module by comparing the results of the predictive model to the campaign results data; and developing an additional targeted marketing campaign with the insight to deliver an additional targeted marketing offer to an additional targeted set of customers via an additional targeted interaction channel.
4. A computer-implemented method according to any one of the preceding claims, further including creating the predictive model.
5. A computer-implemented method according to any one of the preceding claims, further including determining a segment score for a sampled customer in the sample set and associating the sampled customer with one of the customer segments based on the segment score. 27
6. A computer-implemented method according to any one of the preceding claims, wherein the step of segmenting the customers includes overlaying value segments and behavior segments.
7. A computer-implemented system for insight-driven, adaptive marketing, including: a processor; a memory storing instructions operable with the processor for performing marketing activities, the instructions structured throughout a plurality of modules said modules including: a customer data extracting module configured to extract customer data for a plurality of customers from at least one database of customer data using a Customer Analytic Record (CAR) application, wherein the CAR application uses the capabilities of a database management system and a structured query language (SQL); a marketing campaign defining module configured to define a targeted marketing campaign including a selected targeted marketing offer, a selected targeted interaction channel, and a targeted set of customers most likely to respond to the targeted marketing offer via the targeted interaction channel wherein the targeted set of customers is determined by: a customer data selecting module configured to select, through the CAR application, a random selection of the plurality of customers; a segmenting module configured to segment a sample set of the customers within the random selection into a plurality of customer segments, wherein the customer segments are created based on similar characteristics among multiple customers; a segment description module configured to describe each of the plurality of customer segments with lift values indicating a likelihood of positive response by a selected customer segment to a selected marketing offer via the targeted interaction channel; 28 a prospect set selecting module configured to select, based on the lift values, a prospect set from the plurality of customer segments for potential inclusion in a selected marketing campaign; a predictive model module configured to assess, a likelihood of acceptance of each customer in the selected prospect set of the selected marketing offer via the targeted interaction channel; a prospect set scoring module configured to score each customer in the selected prospect set with a customer score indicating the likelihood of acceptance; a customer selection module configured to select the targeted set of customers based on the customer score; an execution module configured to execute the targeted marketing campaign by offering the targeted marketing offer to the targeted set of the customers via the targeted interaction channel; a campaign tracking module configured to gather campaign results data from the executed targeted marketing campaign, the campaign results data including a count of customers accepting the targeted marketing offer via the targeted interaction channel, and a length of time between making of the targeted marketing offer and the accepting of the targeted market offer; a training module configured to train the predictive model with the campaign results data, wherein the training is based on how the customers respond to the various types of marketing offers and to various types of the interaction channels used in the targeted marketing campaign; and an updating module for updating, via a series of SQL update statements, the customer data stored in the database based on the campaign results data.
8. A computer-implemented system according to claim 7, further including an analysis module configured to analyze insight obtained from campaign results data from the targeted marketing campaign to refine and simplify subsequent customer data extraction and customer selection. 29
9. A computer-implemented system according to either claim 7 or claim 8, further including: an insight module configured to obtain insight from the campaign results data by comparing the results of the predictive mode to the campaign results data; and an additional targeted marketing campaign development module configured to develop an additional targeted marketing campaign to deliver an additional targeted marketing offer to an additional targeted set of customers via an additional targeted interaction channel.
10. A computer-implemented system according to any one of claims 7 to 9, further including a predictive model creating module configured to create the predictive model.
11. A computer-implemented system according to any one of claims 7 to 10, wherein the segmenting module is configured to use variables that indicate a customer value.
12. A computer-implemented system according to any one of claims 7 to 11, wherein the segmenting module is configured to use a customer lifetime value as a driving variable.
13. A computer-implemented system according to any one of claims 7 to 12, further including: an overlaying module configured to overlay value segments and behavior segments.
14. A computer program embodied on a non-transitory computer readable medium for performing marketing activities, wherein the computer program instructs a processor to perform a method including: accessing instructions for a plurality of software modules stored in a memory, wherein the plurality of software modules include a customer data extracting software module and a marketing campaign defining module; accessing customer data stored in a database for a plurality of customers; and executing, by the processor, the instructions to perform the steps of: 30 extracting the customer data from the database by the customer data extracting software module using a Customer Analytic Record (CAR) application, wherein the CAR application uses the capabilities of a database management system and a structured query language (SQL); defining a targeted marketing campaign by the marketing campaign defining module, the targeted marketing campaign including a selected targeted marketing offer, a selected targeted interaction channel, and a targeted set of customers most likely to accept the targeted marketing offer via the targeted interaction channel, wherein the targeted set of customers is determined by: selecting, through the CAR application, a random selection of the plurality of customers; segmenting a sample set of the customers within the random selection into a plurality of customer segments, wherein the customer segments are created based on similar characteristics among multiple customers; describing each of the plurality of customer segments with lift values indicating a likelihood of positive response by a selected customer segment to a selected marketing offer via the targeted interaction channel; selecting, based on the lift values, a prospect set from the customer segments for potential inclusion in a selected marketing campaign; assessing with a predictive model a likelihood of acceptance of each customer in the selected prospect set of the selected marketing offer via the targeted interaction channel; scoring each customer in the selected prospect set with a customer score indicating the likelihood of acceptance; selecting the targeted set of customers based on the scoring; and executing the targeted marketing campaign by offering the targeted marketing offer to the targeted set of the customers via the targeted interaction channel; gathering campaign results data from the targeted marketing campaign by a campaign tracking module, the campaign results data including a count of customers accepting the targeted marketing offer via the 31 targeted interaction channel, and a length of time between making of the targeted marketing offer and the accepting of the targeted marketing offer; and training the predictive model with the campaign results data, wherein the training is based on how the customers respond to the various types of marketing offers and to various types of the interaction channels used in the targeted marketing campaign; and updating, via a series of SQL update statements, the customer data stored in the database based on the campaign results data.
15. A computer program according to claim 14, wherein the method further includes executing the instructions to obtain, in a closed-loop process, insight from campaign results data from the targeted marketing campaign, wherein the insight is used to refine and simplify subsequent customer data extraction and customer selection.
16. A computer program according to either claim 14 or claim 15, wherein the method further includes executing the instructions to perform: obtaining insight from the campaign results data with an insight module by comparing the results of the predictive model to the campaign results data; and developing an additional targeted marketing campaign with the insight to deliver an additional targeted marketing offer to an additional targeted set of customers via an additional targeted interaction channel.
17. A computer program according to any one of claims 14 to 16, wherein extracting the customer data includes selecting data from a database view that includes at least a portion of data from the database and a transform that has been developed from the data from the database.
18. A computer program according to any one of claims 14 to 17, wherein a database object includes the database view. 32
19. A computer-implemented method for insight-driven, adaptive marketing according to claim 1, or a computer-implemented system for insight-driven adaptive marketing according to claim 7, or a computer program according to claim 14, substantially as herein described with reference to the accompanying drawings. 1 a. A method for insight-driven, adaptive marketing, comprising: extracting customer data for a plurality of customers from at least one database; training an analytic model to predict customer behaviour; selecting a first customer set of customers from the at least one database for use in a first marketing campaign; gathering campaign results from the first marketing campaign; updating the at least one database based on the campaign results; and repeating the steps of extracting, training, selecting, gathering and updating for at least one additional marketing campaign. 2a. A method for insight driven interaction between an enterprise and a consumer, the method comprising: extracting customer data for at least one of a plurality of customers from at least one database; training a predictive analytic model to predict customer behavior; selecting the at least one of a plurality of customers from the at least one database for use in delivering differentiated customer treatment during a first customer interaction; gathering results from the first customer interaction; updating the at least one database based on the results of the first customer interaction; and repeating the steps of extracting, training, selecting, gathering and updating for at least one additional customer interaction. 33 3a. A system for insight-driven, adaptive marketing, comprising: a customer data extracting module that extracts customer data for a plurality of customers from at least one database; a training module that trains an analytic model to predict customer behavior; a customer selecting module that selects a first customer set of customers from the at least one database for use in a first marketing campaign; a gathering module that gathers campaign results from the first marketing campaign; and an updating module that updates the at least one database based on the campaign results. 4a. A system for insight driven interaction between an enterprise and a consumer comprising: an extracting module that extracts customer data for at least one of a plurality of customers from at least one database; a training module that trains a predictive analytic model to predict customer behavior; a selecting module that selects the at least one of a plurality of customers from the at least one database for use in delivering differentiated customer treatment during a first customer interaction; a gathering module that gathers results from the first customer interaction; and an updating module that updates the at least one database based on the results of the first customer interaction.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2015203884A AU2015203884A1 (en) | 2002-11-22 | 2015-07-10 | Adaptive marketing using insight driven customer interaction |
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| Application Number | Priority Date | Filing Date | Title |
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| US10/302,395 | 2002-11-22 | ||
| AU2012200695A AU2012200695A1 (en) | 2002-11-22 | 2012-02-06 | Adaptive marketing using insight driven customer interaction |
| AU2015203884A AU2015203884A1 (en) | 2002-11-22 | 2015-07-10 | Adaptive marketing using insight driven customer interaction |
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| AU2012200695A Division AU2012200695A1 (en) | 2002-11-22 | 2012-02-06 | Adaptive marketing using insight driven customer interaction |
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| AU2015203884A1 true AU2015203884A1 (en) | 2015-07-30 |
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