Source: http://www.google.com/patents/US7698163?ie=ISO-8859-1&dq=5754119
Timestamp: 2015-08-02 12:30:13
Document Index: 482230451

Matched Legal Cases: ['Application No. 2003302353', 'Application No. 2003299439', 'Application No. 2003299439', 'Application No. 2003302353', 'Application No. 2003302762', 'Application No. 2003302762', 'Application No. 02784170', 'Application No. 03811852', 'Application No. 2612', 'Application No. 2590', 'Application No. 2612', 'Application No. 2590', 'Application No. 03']

Patent US7698163 - Multi-dimensional segmentation for use in a customer interaction - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsA system and method for segmenting customer data that represents a plurality of customers for use in a customer interaction. The segmentation process groups customers with similar characteristics into segments. The segments may be used to classify customers according to a likelihood of the customers...http://www.google.com/patents/US7698163?utm_source=gb-gplus-sharePatent US7698163 - Multi-dimensional segmentation for use in a customer interactionAdvanced Patent SearchPublication numberUS7698163 B2Publication typeGrantApplication numberUS 10/302,418Publication dateApr 13, 2010Filing dateNov 22, 2002Priority dateNov 22, 2002Fee statusPaidAlso published asCA2506800A1, EP1563437A2, US20040103051, WO2004049222A2, WO2004049222A8Publication number10302418, 302418, US 7698163 B2, US 7698163B2, US-B2-7698163, US7698163 B2, US7698163B2InventorsKenneth L. Reed, Hari S. Hariharan, Michiko SaitoOriginal AssigneeAccenture Global Services GmbhExport CitationBiBTeX, EndNote, RefManPatent Citations (50), Non-Patent Citations (59), Referenced by (38), Classifications (10), Legal Events (3) External Links: USPTO, USPTO Assignment, EspacenetMulti-dimensional segmentation for use in a customer interaction
US 7698163 B2Abstract
1. A computer-implemented method for segmenting data representing a plurality of customers, comprising the computer-implemented steps of:
choosing, using a data type choosing module, at least four different data types stored in a data warehouse on a computer platform,
wherein the at least four different data types comprise a first data type containing customer behavior data, a second data type containing customer value data, a third data type, and a fourth data type; and
wherein the third and fourth data types are chosen from a group comprising attitude, satisfaction, brand experience, brand attachment, brand utility, lifestyle, life-stage, and category involvement;
segmenting, using at least one software module, a subset of the plurality of customers by each of the at least four different data types to produce a corresponding plurality of segmentation results;
generating a multi-dimensional hypercube, for cross-segmenting the subset of the plurality of customers, and combining the at least four different data types by overlaying the plurality of segmentation results within the multi-dimensional hypercube;
profiling the plurality of segmentation results overlaid within the multi-dimensional hypercube, wherein profiling includes cross-segmenting the subset of the plurality of customers to produce a joint view of relationships among the at least four different data types, and to identify the relationships among the at least four data types;
associating the identified relationships to a superset of the plurality of customers; and
updating a customer record stored in the data warehouse on the computer platform for each of the superset of the plurality of customers to reflect the identified relationships.
2. The computer-implemented method of claim 1, wherein the computer-implemented step of segmenting, using the at least one software module, the plurality of customers comprises employing a cluster analysis algorithm to identify clusters in the data, and wherein distance metrics utilized within the cluster analysis algorithm to cluster the customers are calculated based on a business objective.
3. The computer-implemented method of claim 1, wherein the computer-implemented step of segmenting, using the at least one software module, the plurality of customers comprises excluding demographic variables from a segmentation decision.
4. The computer-implemented method of claim 1, wherein the customer behavior data comprises purchase pattern data.
5. The computer-implemented method of claim 1, wherein the customer behavior data comprises preferences data.
6. The computer-implemented method of claim 1, wherein the customer behavior data comprises support needs data.
7. The computer-implemented method of claim 1, wherein the customer value data comprises cost data.
8. The computer-implemented method of claim 1, wherein the customer value data comprises customer lifetime value data.
9. The computer-implemented method of claim 1, wherein the customer value data comprises revenue data.
10. The computer-implemented method of claim 1, wherein the computer-implemented method for segmenting is used in at least one of a customer interaction, customer portfolio management, marketing strategy, and product development.
11. A computer-implemented method for segmenting data representing a plurality of customers, the method comprising the computer-implemented steps of:
choosing, using a characteristic choosing module, at least four different characteristics stored in a data warehouse on a computer platform,
wherein the at least four different characteristics are chosen from a group comprising attitude, behavior, value, satisfaction, brand experience, brand attachment, brand utility, lifestyle, life-stage, and category involvement;
segmenting, using at least one software module, a subset of the plurality of customers by each of the at least four different characteristics to produce a corresponding plurality of segmentation results;
generating a matrix multi-dimensional hypercube for cross-segmenting the subset of the plurality of customers and combining the at least four different characteristics by overlaying the plurality of segmentation results within the multi-dimensional hypercube;
profiling the plurality of segmentation results overlaid within the multi-dimensional hypercube, wherein profiling includes cross-segmenting the subset of the plurality of customers to produce a joint view of relationships among the at least four different characteristics and to identify the relationships among the at least four different characteristics;
12. The computer-implemented method of claim 11, wherein identifying the relationships is performed according to defined characteristic relationships that comprise at least one of a group comprising:
the attitude driving the behavior;
the behavior driving the value;
a relationship experience felt by a customer impacting the satisfaction;
the satisfaction affecting the brand experience;
the brand experience affecting the brand attachment which is also being impacted by the advertising;
the life-stage and the lifestyle affecting the brand utility and the category involvement; and
the attitude being affected by the brand experience, the brand attachment, the brand utility, and the category involvement.
13. The computer-implemented method of claim 11, wherein the method for segmenting is used in at least one of a customer interaction, customer portfolio management, marketing strategy, and product development.
14. A system for segmenting data representing a plurality of customers, comprising:
a data type choosing module that chooses at least four different data types stored in a data warehouse on a computer platform,
wherein the at least four different data types comprise a first data type containing behavior data, a second data type containing value data, a third data type, and a fourth data type; and
a data type segmenting module that segments a subset of the plurality of customers by each of the at least four different data types to produce a corresponding plurality of segmentation results;
a multi-dimensional hypercube generating module that generates a multi-dimensional hypercube for cross-segmenting the subset of a plurality of customers by the at least four different data types and that combines the at least four different data types by overlaying the plurality of segmentation results within the multi-dimensional hypercube;
a profiling module that profiles the segmentation results overlaid within the multi-dimensional hypercube by cross-segmenting the subset of the plurality of customers to produce a joint view of relationships among the at least four different data types, and to identify the relationships among the at least four different data types;
an associating module that associates the identified relationships to a superset of the plurality of customers; and
an updating module that updates a customer record stored in the data warehouse on the computer platform for each of the superset of the plurality of customers to reflect the identified relationships.
15. The system of claim 14, wherein the data type segmenting module employs a cluster analysis algorithm to identify clusters in the data, and wherein distance metrics utilized within the cluster analysis algorithm to cluster the customers are calculated based on a business objective.
an excluding module that excludes demographic variables from a segmentation decision.
17. The system of claim 14, wherein the data type segmenting module employs distance metrics driven by a business objective.
18. The system of claim 14, wherein the behavior data comprises purchase pattern data.
19. The system of claim 14, wherein the behavior data comprises preferences data.
20. The system of claim 14, wherein the behavior data comprises support needs data.
21. The system of claim 14, wherein the value data comprises cost data.
22. The system of claim 14, wherein the value data comprises customer lifetime value data.
23. The system of claim 14, wherein the value data comprises revenue data.
24. The system of claim 14, wherein the system for segmenting data is used in at least one of a customer interaction, customer portfolio management, marketing strategy, and product development.
25. A system for segmenting data representing a plurality of customers, the system comprising:
a characteristic choosing module that chooses at least four different characteristics stored in a data warehouse on a computer platform,
a characteristic segmenting module that segments a subset of the plurality of customers by each of the at least four different characteristics to produce a corresponding plurality of segmentation results;
a multi-dimensional hypercube generating module that generates a multi-dimensional hypercube for cross-segmenting the subset of a plurality of customers and that combines the at least four different characteristics by overlaying the plurality of segmentation results within the multi-dimensional hypercube;
a profiling module that profiles the plurality of segmentation results overlaid within the multi-dimensional hypercube by cross-segmenting the subset of the plurality of customers to produce a joint view of relationships among the at least four different characteristics and to identify the relationships among the at least four different characteristics;
26. The system of claim 25, wherein identifying the relationships is performed according to defined characteristic relationships that comprise at least one of a group comprising:
27. The system of claim 25, wherein the system for segmenting data is used in at least one of a customer interaction, customer portfolio management, marketing strategy, and product development.
28. A computer program product comprising a computer-readable medium having computer-executable instructions that control a computer to perform a method for segmenting data representing a plurality of customers, the method comprising the computer-implemented steps of:
generating, using the at least one software module, a multi-dimensional hypercube for cross-segmenting the subset of the plurality of customers and combining the at least four different data types by overlaying the plurality of segmentation results within the multi-dimensional hypercube;
profiling the plurality of segmentation results within the multi-dimensional hypercube, wherein profiling includes cross-segmenting the subset of the plurality of customers to produce a joint view of relationships among the at least four different data types and to identify relationships among the at least four different data types;
29. A computer program product comprising a computer-readable medium having computer-executable instructions that control a computer to perform a method for segmenting data representing a plurality of customers, the method comprising:
segmenting, using a segmenting module, a subset of the plurality of customers by each of the at least four different characteristics to produce a corresponding plurality of segmentation results; and
generating, using a generating module, a multi-dimensional hypercube for cross-segmenting the subset of a plurality of customers and combining the at least four different characteristics by overlaying the plurality of segmentation results within the multi-dimensional hypercube;
profiling, using a profiling module, the plurality of segmentation results within the multi-dimensional hypercube by cross-segmenting the subset of the plurality of customers to produce a joint view of relationships among the at least four different characteristics and to identify the relationships among the at least four different characteristics;
associating, using an association module, the identified relationships to a superset of the plurality of customers; and
updating, using an updating module, a customer record stored in the data warehouse on the computer platform for each of the superset of the plurality of customers to reflect the identified relationships.
This application is related to commonly assigned co-pending patent applications “Adaptive Marketing Using Insight Driven Customer Interaction”, U.S. patent application Ser. No. 10/302,395 and “Standardized Customer Application and Record for Inputting Customer Data Into Analytic Models”, U.S. patent application Ser. No. 10/302,337, both filed Nov. 22, 2002, all of which is incorporated herein by reference. This application is also related to commonly assigned co-pending U.S. patent application Ser. No. 10/014,840, filed Oct. 22, 2001, and titled “Real-Time Collaboration and Workflow Management for a Marketing Campaign”, all of which is incorporated herein by reference.
Patent CitationsCited PatentFiling datePublication dateApplicantTitleUS5548506Mar 17, 1994Aug 20, 1996Srinivasan; Seshan R.Automated, electronic network based, project management server system, for managing multiple work-groupsUS5659724Nov 6, 1992Aug 19, 1997NcrInteractive data analysis apparatus employing a knowledge baseUS5712987Jun 3, 1994Jan 27, 1998Ncr CorporationInterface and associated bank customer databaseUS5806062Oct 17, 1995Sep 8, 1998Lucent Technologies Inc.Data analysis system using virtual databasesUS5930764Aug 23, 1996Jul 27, 1999Citibank, N.A.Sales and marketing support system using a customer information databaseUS5963910Sep 20, 1996Oct 5, 1999Ulwick; Anthony W.Computer based process for strategy evaluation and optimization based on customer desired outcomes and predictive metricsUS5987466Nov 25, 1997Nov 16, 1999International Business Machines CorporationPresenting web pages with discrete, browser-controlled complexity levelsUS6026397May 22, 1996Feb 15, 2000Electronic Data Systems CorporationData analysis system and methodUS6032125Oct 22, 1997Feb 29, 2000Fujitsu LimitedDemand forecasting method, demand forecasting system, and recording mediumUS6064977Jun 19, 1998May 16, 2000International Business Machine CorporationWeb server with integrated scheduling and calendaringUS6073112Jul 12, 1999Jun 6, 2000Geerlings; HuibComputer system for merchant communication to customersUS6219680Jun 19, 1998Apr 17, 2001International Business Machines CorporationSystem and method for building a web site for use in E-commerce with user specific pricingUS6223177Nov 19, 1998Apr 24, 2001Involv International CorporationNetwork based groupware systemUS6240441Mar 31, 1997May 29, 2001Sun Microsystems, Inc.Secure event-driven EDI transaction processing using the internetUS6240444Sep 27, 1996May 29, 2001International Business Machines CorporationInternet web page sharingUS6253234Oct 17, 1997Jun 26, 2001International Business Machines CorporationShared web page caching at browsers for an intranetUS6334110Mar 10, 1999Dec 25, 2001Ncr CorporationSystem and method for analyzing customer transactions and interactionsUS6430539May 6, 1999Aug 6, 2002Hnc SoftwarePredictive modeling of consumer financial behaviorUS6622125Nov 7, 2000Sep 16, 2003International Business Machines CorporationAutomatic sales promotion selection system and methodUS6636862 *Jul 5, 2001Oct 21, 2003Camo, Inc.Method and system for the dynamic analysis of dataUS6647269Jul 5, 2001Nov 11, 2003TelcontarMethod and system for analyzing advertisements delivered to a mobile unitUS6732096Jul 30, 2001May 4, 2004Ncr CorporationOptimizing an aggregate join queryUS6742003 *Apr 30, 2001May 25, 2004Microsoft CorporationApparatus and accompanying methods for visualizing clusters of data and hierarchical cluster classificationsUS6836773Sep 27, 2001Dec 28, 2004Oracle International CorporationEnterprise web mining system and methodUS6839682 *Oct 3, 2000Jan 4, 2005Fair Isaac CorporationPredictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matchingUS6862574Jul 27, 2000Mar 1, 2005Ncr CorporationMethod for customer segmentation with applications to electronic commerceUS6970882Apr 4, 2002Nov 29, 2005International Business Machines CorporationUnified relational database model for data mining selected model scoring results, model training results where selection is based on metadata included in mining model control tableUS7003476 *Dec 29, 1999Feb 21, 2006General Electric Capital CorporationMethods and systems for defining targeted marketing campaigns using embedded models and historical dataUS7016936May 15, 2001Mar 21, 2006Hewlett-Packard Development Company, L.P.Real time electronic service interaction management system and methodUS7047251 *Nov 22, 2002May 16, 2006Accenture Global Services, GmbhStandardized customer application and record for inputting customer data into analytic modelsUS7069197 *Oct 25, 2001Jun 27, 2006Ncr Corp.Factor analysis/retail data mining segmentation in a data mining systemUS7072841Apr 29, 1999Jul 4, 2006International Business Machines CorporationMethod for constructing segmentation-based predictive models from data that is particularly well-suited for insurance risk or profitability modeling purposesUS7072848 *Nov 15, 2001Jul 4, 2006Manugistics, Inc.Promotion pricing system and methodUS7155401Jan 7, 1998Dec 26, 2006International Business Machines CorporationAutomatic sales promotion selection system and methodUS7177851Nov 9, 2001Feb 13, 2007Affinnova, Inc.Method and apparatus for dynamic, real-time market segmentationUS20010037321Feb 28, 2001Nov 1, 2001Vladimir FishmanMethod of building predictive models on transactional dataUS20020038230 *Sep 24, 2001Mar 28, 2002Li-Wen ChenUser interface and method for analyzing customer behavior based upon event attributesUS20020147599Apr 5, 2001Oct 10, 2002International Business Machines CorporationMethod and system for simplifying the use of data mining in domain-specific analytic applications by packaging predefined data mining modelsUS20020169658Mar 6, 2002Nov 14, 2002Adler Richard M.System and method for modeling and analyzing strategic business decisionsUS20020194050Apr 6, 2001Dec 19, 2002Oumar NabeMethods and systems for supplying customer leads to dealersUS20030009536Jul 6, 2001Jan 9, 2003Portris, Inc.Method and system for collaborative knowledge managementUS20030033194Nov 9, 2001Feb 13, 2003Pavilion Technologies, Inc.System and method for on-line training of a non-linear model for use in electronic commerceUS20030130899Jan 8, 2002Jul 10, 2003Bruce FergusonSystem and method for historical database training of non-linear models for use in electronic commerceUS20030163343Feb 27, 2002Aug 28, 2003International Business Machines CorporationMethod and system for dynamically modifying an electronic campaign based on network activityUS20030195889Apr 4, 2002Oct 16, 2003International Business Machines CorporationUnified relational database model for data miningUS20040039679Aug 14, 2002Feb 26, 2004Norton David W.Generation and acceptance of tailored offersUS20040254860May 6, 2004Dec 16, 2004Accenture Global Services Gmbh, A Switzerland CorporationReal-time collaboration and workflow management for a marketing campaignUS20060090171Sep 30, 2004Apr 27, 2006Citrix Systems, Inc.Method and apparatus for virtualizing window informationJP2002140490A Title not availableWO1999054828A1Apr 20, 1999Oct 28, 1999Straight Up Software IncSystem and method for assessing effectiveness of internet marketing campaign* Cited by examinerNon-Patent CitationsReference1(no authors) E.piphany, Inc. Software for the Customer Economy, May 18, 2001, www.epiphany.com, pp. 1-7.2(no authors) Is your dimensional data warehousing expressive? Intelligient enterprise, www.intelligententerprise.com, May 15, 2000 vol. 3- number, pp. 1-4.3(no authors) Is your dimensional data warehousing expressive? Intelligient enterprise, www.intelligententerprise.com, May 15, 2000 vol. 3— number, pp. 1-4.4(no authors) MarketFirst to Integrate Leading Analytic Applications from Business Objects, May 15, 2002, www.businessobjects.com Partners Press Release, pp. 1.5(no authors) RightNow Technologies and MarketFirst Partnership Delivers Complete e-Marketing solutions, Aug. 14, 2001, www.rightnow.com, pp. 1-2.6AdAgent 2.0, Ad Sales Go Real-Time, Karpinski, Richard, InternetWeek, v692, p. 19(1), Dec. 1, 1997, 3 pages.7Andrew Eisenberg et al., SQL standardization: the next steps, Mar. 2000, vol. 29, Issue 1, pp. 63-67.8AU Examination Report issued on Apr. 20, 2007 for AU Application No. 2003302353.9AU Examination Report issued on Apr. 5, 2007 for AU Application No. 2003299439.10AU Examination Report issued on Apr. 8, 2008 for AU Application No. 2003299439.11AU Examination Report issued on Jun. 27, 2008 for AU Application No. 2003302353.12AU Examination Report issued on Mar. 15, 2007 for AU Application No. 2003302762.13AU Examination Report issued on Oct. 1, 2008 for AU Application No. 2003302762.14 *Becher, Tutorial on E-Commerce and Clickstream Mining, First SIAM International Conference on Data Mining, Apr. 5, 2001, p. 1-84.15Berson et al. "Building Data Mining Applications for CRM," December 22, 1999, McGraw-Hill Companies, Chapter 10, retrieved online from <http://thearling.com/text/chapter10.htm.16 *Berson et al., Customer Acquisition and Data Mining, Excerpted from the Book Building Data Mining Applications for CRM, 1999, online reprint comprises p. 1-6.17 *Cabena et al., Intelligent Miner for Data Applications Guide, IBM International Technical Support Organization, Mar. 1999, p. 1-159.18Cabena, Peter; Choi, Hyun Hee; Kim, II Soo; Otsuka, Shuichi; Reinschmidt, Joerg; Saarenvirta, Gary. "Intelligent Miner for Data Applications Guide". Mar. 1999. IBM Corporation.19 *CRM Best Practices, CRMTRends.com, Oct. 16, 2002, p. 1-11, retrieved from web.archive.org, http://web.archive.org/web/20021016232814/ http://crmtrends.com/analytics.html.20Dayco Statement Regarding Related Applications.21 *Eick, Visual Discovery and Analysis, IEEE Transactions on Visualization and Computer Graphics, vol. 6, No. 1, Jan.-Mar. 2000, p. 44-58.22EP Communication issued on Nov. 8, 2007 for EP Application No. 02784170.9.23EP Examination Report issued on Feb. 29, 2008 for EP Application No. 03811852.7.24Frawley, Andrew; Thearling, Kurt. "Increasing Customer Value by Integrating Data Mining and Campaign Management Software". Feb. 1999. Direct Marketing. vol. 61, Issue 10. pp. 49-53.25 *Gray et al., Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals, Data Mining and Knowledge Discovery 1, 1997, p. 29-53.26 *Hahn et al., Capturing Customer Heterogeneity Using a Finite Mixture PLS Approach, Schmalenbach Business Review, vol. 54, Jul. 2002, p. 243-269.27Hoekstra, Janny C.; Huizingh, Eelko K.R.E; "The Lifetime Value Concept in Customer-Based Marketing". 1999. Journal of Market Focused Management. vol. 3, pp. 257-274.28Hughest, Arthur Middleton. "Boosting Response with RFM". May 1996. American Demographics. pp. 4-10.29IN Examination Report issued on Dec. 27, 2006 for IN Application No. 2612/DELNP/2005.30IN Examination Report issued on Feb. 28, 2007 for IN Application No. 2590/DELNP/2005.31IN Examination Report issued on Jan. 2, 2006 for IN Application No. 2612/DELNP/2005.32IN Examination Report issued on Oct. 19, 2007 for IN Application No. 2590/DELNP/2005.33Information regarding Enterprise Insight downloaded from www.epihany.com, May 18, 2001, 7 pages.34Information regarding nQuire Software downloaded from www.nQuire.com, Jul. 23, 2001, 12 pages.35Kahan, Ron. "Using Database Marketing Techniques to Enhance Your One-to-One Marketing Initiatives". 1998. The Journal of Consumer Marketing. vol. 15, Issue 5. p. 491.36 *Kahan, Using database marketing techniques to enhance your one-to-one marketing initiatives, The Journal of Consumer Marketing, Santa Barbara,vol. 15, No. 5, 1998, p. 491 (reprint p. 1-3).37 *Kimball, Is Your Dimensional Data Warehouse Expressive?, Intelligent Enterprise Magazine, vol. 3, No. 8, May 15, 2000, p. 1- 4.38 *Machauer and Morgner, Segmentation of bank customers by expected benefits and attitudes, international journal of Bank Marketing, 19/1 (2001), p. 6-17.39MarketFirst Announces Initial Customer Availability of MarketFirst Release 2.0. PR Newswire, p. 2526, May 4, 1999, 4 pages.40Miglautsch, John R. "Thoughts on RFM Scoring". May 22, 2000. Journal of Database Marketing. vol. 8, Issue 1. pp. 67-72.41 *Morgan, The Boomer Attitude, American Demographics, Oct 1, 2002, p. 1-5.42Office Action issued on Apr. 15, 2003 by USPTO in U.S. Appl. No. 10/014,840.43Office Action issued on Jun. 23, 2009 by USPTO in U.S. Appl. No. 10/302,395.44Office Action issued on Mar. 8, 2005 by USPTO in U.S. Appl. No. 10/302,337.45Office Action issued on May 15, 2008 by US PTO on U.S. Appl. No. 10/302,395.46Office Action issued on May 2, 2006 by USPTO in U.S. Appl. No. 11/147,034.47Office Action issued on Nov. 12, 2003 by USPTO in U.S. Appl. No. 10/014,840.48Office Action issued on Oct. 19, 2006 by USPTO in U.S. Appl. No. 11/147,034.49Office Action issued on Oct. 19, 2007 by US PTO on U.S. Appl. No. 10/302,395.50Office Action issued on Oct. 7, 2008 by USPTO in U.S. Appl. No. 10/302,395.51Office Action/Examination Report issued by the EPO Nov. 6, 2007 on Application No. 03 812 082.0-2221.52Paragren Technologies and Rubric Announce Strategic Relationship: web execution and workflow enhances relationship marketing solutions. PR Newswire, p073DCTH030, Jul. 30, 1998, 3 pages.53 *Poulos, Choosing Customers Through Needs-Based Segmentation: Part I, destinationCRM.com, Nov. 8, 2000, p. 1-3.54Schmid, Jack; Boyle, Lois. "Catalog Creative; The RFMP Way". Jul. 1998. Target Marketing. vol. 21, Issue 7. pp. 38-41.55Thearling, Kurt, "Campaign Optimization: Maximizing the Value of Interacting with Your Customers," Aug. 2001, Relationship Marketing Report.56 *Vassiliadis, Modeling Multidimensional Databases, Cubes, and Cube Operations, IEEE 1998, p. 53-62.57Weber, Alan. "A Simple Way to Use RFM". Mar. 1997. Target Marketing. vol. 20, Issue 3, pp. 72-75.58 *Yankelovich-Solutions and Services, Yankelovich Monitor MindBase, 2001-2002, p. 1-22, retrieved from web.archive.org, see e.g., http://web.archive.org/web/ 20011024020908/secure.yankelovich.com/solutions/mindbase.asp.59 *Yankelovich—Solutions and Services, Yankelovich Monitor MindBase, 2001-2002, p. 1-22, retrieved from web.archive.org, see e.g., http://web.archive.org/web/ 20011024020908/secure.yankelovich.com/solutions/mindbase.asp.* Cited by examinerReferenced byCiting PatentFiling datePublication dateApplicantTitleUS7904327 *Apr 30, 2003Mar 8, 2011Sas Institute Inc.Marketing optimization systemUS8065262Oct 19, 2010Nov 22, 2011Sas Institute Inc.Computer-implemented multidimensional database processing method and systemUS8175989Jan 3, 2008May 8, 2012Choicestream, Inc.Music recommendation system using a personalized choice setUS8271313 *Sep 26, 2011Sep 18, 2012Experian Marketing Solutions, Inc.Systems and methods of enhancing leads by determining propensity scoresUS8364518Feb 14, 2011Jan 29, 2013Experian Ltd.Systems and methods for forecasting household economicsUS8442854 *Dec 6, 2012May 14, 2013Branch Banking And Trust CompanySystem and method for estimating residual lifetime value of a customer base utilizing survival analysisUS8494995 *Nov 17, 2010Jul 23, 2013International Business Machines CorporationSystems and methods for dynamically reconfiguring predictive analytics within workflows using selective user feedbackUS8504409 *Jul 21, 2011Aug 6, 2013Branch Banking & Trust CompanySystem and method for evaluating banking consumers as a function of aggregated residual lifetime values and potential lifetime valuesUS8510658Aug 11, 2010Aug 13, 2013Apple Inc.Population segmentationUS8583593Nov 18, 2011Nov 12, 2013Experian Information Solutions, Inc.Systems and methods for optimizing database queriesUS8630892Mar 30, 2012Jan 14, 2014Accenture Global Services LimitedChurn analysis systemUS8688503 *Jun 22, 2012Apr 1, 2014American Express Travel Related Services Company, Inc.System and method for targeting family members of transaction account product holders to receive supplementary transaction account productsUS8694357 *Jun 7, 2010Apr 8, 2014E-Rewards, Inc.Online marketing research utilizing sentiment analysis and tunable demographics analysisUS8712828 *Feb 3, 2006Apr 29, 2014Accenture Global Services LimitedChurn prediction and management systemUS8756096May 14, 2013Jun 17, 2014Branch Banking & Trust CompanySystem for evaluating banking consumers as a function of aggregated residual lifetime values and potential lifetime valuesUS8775299Jul 11, 2012Jul 8, 2014Experian Information Solutions, Inc.Systems and methods for large-scale credit data processingUS8805724 *Dec 18, 2007Aug 12, 2014Verizon Patent And Licensing Inc.Intelligent customer retention and offer/customer matchingUS8825514 *May 14, 2013Sep 2, 2014Branch Banking And Trust CompanySystem and method for estimating residual lifetime value of a customer base utilizing survival analysisUS8856071Aug 11, 2009Oct 7, 2014At&T Intellectual Property I, L.P.Minimizing staleness in real-time data warehousesUS8868580 *Sep 15, 2004Oct 21, 2014Ab Initio Technology LlcData profilingUS8930251Jan 6, 2012Jan 6, 2015Consumerinfo.Com, Inc.Debt trending systems and methodsUS8990103Aug 2, 2010Mar 24, 2015Apple Inc.Booking and management of inventory atoms in content delivery systemsUS9043220 *Oct 19, 2010May 26, 2015International Business Machines CorporationDefining marketing strategies through derived E-commerce patternsUS9047615 *Apr 30, 2012Jun 2, 2015International Business Machines CorporationDefining marketing strategies through derived E-commerce patternsUS9087335 *Sep 29, 2006Jul 21, 2015American Express Travel Related Services Company, Inc.Multidimensional personal behavioral tomographyUS20040093296 *Apr 30, 2003May 13, 2004Phelan William L.Marketing optimization systemUS20050114369 *Sep 15, 2004May 26, 2005Joel GouldData profilingUS20080065395 *Aug 25, 2006Mar 13, 2008Ferguson Eric JIntelligent marketing system and methodUS20090157449 *Dec 18, 2007Jun 18, 2009Verizon Data Services Inc.Intelligent customer retention and offer/customer matchingUS20110004483 *Jun 7, 2010Jan 6, 2011Conversition Strategies, Inc.Systems for applying quantitative marketing research principles to qualitative internet dataUS20120022917 *Jan 26, 2012Branch Banking & Trust CompanySystem and method for evaluating a client baseUS20120041817 *Aug 11, 2010Feb 16, 2012Apple Inc.Prioritizing population segment assignments to optimize campaign goalsUS20120095770 *Apr 19, 2012International Business Machines CorporationDefining Marketing Strategies Through Derived E-Commerce PatternsUS20120123987 *May 17, 2012International Business Machines CorporationSystems and methods for dynamically reconfiguring predictive analytics within workflows using selective user feedbackUS20120215590 *Apr 30, 2012Aug 23, 2012International Business Machines CorporationDefining Marketing Strategies Through Derived E-Commerce PatternsUS20120290357 *Nov 15, 2012American Express Travel Related Services Company, Inc.Identifying industry segments with highest potential for new customers or new spending for current customersUS20120323835 *Aug 28, 2012Dec 20, 2012International Business Machines CorporationDynamically reconfiguring predictive analytics within workflows using selective user feedbackUS20130253983 *May 14, 2013Sep 26, 2013Robert Russell LawtonSystem and method for estimating residual lifetime value of a customer base utilizing survival analysis* Cited by examinerClassifications U.S. Classification705/7.33, 703/2International ClassificationG06Q30/00, G06F17/30Cooperative ClassificationG06Q30/02, G06Q40/06, G06Q30/0204European ClassificationG06Q30/02, G06Q30/0204, G06Q40/06Legal EventsDateCodeEventDescriptionMar 4, 2003ASAssignmentOwner name: ACCENTURE GLOBAL SERVICES GMBH, SWITZERLANDFree format text: ASSIGNMENT OF ASSIGNORS 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