WO2001011522A2 - Method for optimizing net present value of a cross-selling marketing campaign - Google Patents

Method for optimizing net present value of a cross-selling marketing campaign Download PDF

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Publication number
WO2001011522A2
WO2001011522A2 PCT/US2000/021453 US0021453W WO0111522A2 WO 2001011522 A2 WO2001011522 A2 WO 2001011522A2 US 0021453 W US0021453 W US 0021453W WO 0111522 A2 WO0111522 A2 WO 0111522A2
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WO
WIPO (PCT)
Prior art keywords
customer
optimizing
cross
linear
marketing campaign
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2000/021453
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English (en)
French (fr)
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WO2001011522A8 (en
Inventor
Yuri Galperin
Vladimir Fishman
Leonid Gibiansky
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Marketswitch Corp
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Marketswitch Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Marketswitch Corp filed Critical Marketswitch Corp
Priority to AU64009/00A priority Critical patent/AU769761B2/en
Priority to CA002381349A priority patent/CA2381349A1/en
Priority to EP00950995A priority patent/EP1212717A2/en
Priority to JP2001516103A priority patent/JP2003526139A/ja
Publication of WO2001011522A2 publication Critical patent/WO2001011522A2/en
Publication of WO2001011522A8 publication Critical patent/WO2001011522A8/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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

Definitions

  • TITLE Method for Optimizing Net Present Value of a Cross- Selling Marketing
  • This invention relates generally to the development of a method to optimize the effects of cross-selling marketing campaigns. More specifically, this invention is an improvement on the application of classical methods of discrete linear programming to the problem of multidimensional optimization.
  • Businesses typically have a number of promotions to offer to a large list of prospective customers. Each promotion may have an eligibility condition, a response model, and a profitability model associated with it.
  • Peer Groups i.e., groups of mutually exclusive offers, such as a credit card with different interest rates.
  • a constraint may be placed on the maximum number of offers that goes to any customer; in addition, there may be business requirements such as minimal number of sales, minimal NPV (Net Present Value) per customer, maximal budget, etc. These requirements may apply to any individual promotion, a peer group, or a campaign as a whole.
  • the goal of cross-selling marketing optimization is to determine what offers to send to which customers to maximize a utility function of the campaign (total NPV, total number of sales etc.), while satisfying all the business requirements and constraints.
  • the present state of the art lets marketers process one offer at a time.
  • a response and/or profitability model is applied and customers are rank-ordered based on propensity to respond to the offer. After this ordering, a certain percentage from the top of the list is selected to receive the offer. The same process is applied to all available offers separately.
  • the present invention represents the application of a novel iterative algorithm to the problem of multidimensional optimization.
  • the present invention supplies a strict, nonlinear mathematical solution to what has traditionally been treated as a linear multidimensional problem.
  • the process of the present invention consists of randomly selecting a statistically significant sample of a prospect list, calculating the value of the utility function for each pair of an offer and selected prospects, reducing the original linear multidimensional problem to a non-linear problem with a feasible number of dimensions, solving the nonlinear problem for the selected sample numerically with the desired tolerance using an iterative algorithm, and using the results to calculate an optimal set of offers in one pass for the full prospect list.
  • Figure 1 is a flow chart of the basic process of the present invention.
  • Figure 2 is a more detailed data flow of a marketing optimization process of the present invention.
  • Figure 3 is a flow chart of the single pass process of the present invention.
  • Figure 4 is a flow chart of the novel iterative algorithm of the present invention.
  • the present invention represents the application of a novel iterative algorithm to the problem of multidimensional optimization of cross-selling campaigns by supplying a strict, nonlinear mathematical solution to the traditional linear multidimensional problem desired to be solved when offering a large number of promotions M to a very large set of prospective customers N.
  • the process of the present invention consists of randomly selecting a statistically significant sample 10 of a prospect list, calculating the value of the utility function 20 for each pair of an offer 30 and selected prospects 10, reducing the original linear multidimensional problem to a non-linear problem 40 with a feasible number of dimensions, solving the non-linear problem 50 for the selected sample numerically with the desired tolerance using an iterative algorithm, and using the results to calculate an optimal set of offers 60 in one pass for the full prospect list.
  • NPV NPV( A, R, P)
  • NPV NPV( A, R, P)
  • Eligibility conditions, peer group logic, and maximal number of offers per customer constraint can be expressed by a set of inequalities C lk
  • G are linear functions
  • M is of the order of number of promotions in the campaign
  • L is total number of restrictions. These main restrictions are applied for a promotion or the campaign, and G is a sum over all eligible customers.
  • a first step is to create a campaign or project by selecting a set 202 of targeting optimizer (TO) projects from a modeling database 200.
  • TO targeting optimizer
  • Each TO project contains promotion and offer economics, and eligibility information for a selected pool of prospects.
  • Each TO project includes substitute offer groups 206, model calibration 204, and eligibility information that is combined with the prospect input to create an eligibility matrix 214.
  • DCP derived customer pool
  • Matrices P and R are then calculated for selected prospects at 224.
  • the next steps, to reduce the original linear multidimensional problem to a non-linear problem with a feasible number of dimensions and solve the non-linear problem for the selected sample numerically with the desired tolerance using a novel iterative algorithm (described below) is done by the optimization engine 240.
  • Input data reports 230 record the matrices and offers used. Using this input data, campaign level constraints 242, and offer level constraints 244, the optimization engine 240 produces a solicitation matrix 250. This is used to calculate report data 252 for optimization reports 254 that are tested at 260 to see if the selected constraints 242 and 244 satisfied the desired offer solicitation schema 256. If satisfied, a final report 260 is generated. If the offer solicitation schema 256 are not satisfied, campaign level constraints 242 and offer level constraints 244 are adjusted to produce another iteration.
  • the optimization engine 240 calculates the vector of parameters L of the ANPV (adjusted NP V) functions
  • Pi (Pi j ) _ vector of profitability of a customer i for promotions 1, 2, ...
  • the present invention needs to solve the high dimensional conditional extremum problem with a large number of restrictions.
  • the present invention uses the Lagrange multiplier technique to take into account only the main restrictions. They can be of an equality or inequality type. This low-dimensional nonlinear problem is solved by a gradient type iterative process.
  • the algorithm as shown in figure 4, consists of following steps:
  • a novel feature of the algorithm used by the present invention enables rollout scoring of a 100M record database overnight.
  • the present invention operates on a computer system and is used for targeted marketing purposes. Using the present invention in conjunction with a neural network, the present invention provides a user with data indicating the individuals or classes or individuals who are most likely to respond to direct marketing.

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  • Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Engineering & Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Finance (AREA)
  • Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Complex Calculations (AREA)
PCT/US2000/021453 1999-08-06 2000-08-05 Method for optimizing net present value of a cross-selling marketing campaign Ceased WO2001011522A2 (en)

Priority Applications (4)

Application Number Priority Date Filing Date Title
AU64009/00A AU769761B2 (en) 1999-08-06 2000-08-05 Method for optimizing net present value of a cross-selling marketing campaign
CA002381349A CA2381349A1 (en) 1999-08-06 2000-08-05 Method for optimizing net present value of a cross-selling marketing campaign
EP00950995A EP1212717A2 (en) 1999-08-06 2000-08-05 Method for optimizing net present value of a cross-selling marketing campaign
JP2001516103A JP2003526139A (ja) 1999-08-06 2000-08-05 抱き合わせ販売マーケティング・キャンペーンの正味現在価値を最適化するための方法

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US14745699P 1999-08-06 1999-08-06
US60/147,456 1999-08-06

Publications (2)

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WO2001011522A2 true WO2001011522A2 (en) 2001-02-15
WO2001011522A8 WO2001011522A8 (en) 2001-12-27

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EP (1) EP1212717A2 (enExample)
JP (1) JP2003526139A (enExample)
AU (1) AU769761B2 (enExample)
CA (1) CA2381349A1 (enExample)
WO (1) WO2001011522A2 (enExample)

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US7624054B2 (en) 2005-08-25 2009-11-24 Sas Institute Inc. Financial risk mitigation optimization systems and methods
US7634431B2 (en) 2006-03-08 2009-12-15 Sas Institute Inc. Systems and methods for costing reciprocal relationships
US7689528B2 (en) 2004-07-09 2010-03-30 Fair Isaac Corporation Method and apparatus for a scalable algorithm for decision optimization
US7813948B2 (en) 2006-08-25 2010-10-12 Sas Institute Inc. Computer-implemented systems and methods for reducing cost flow models
US7904327B2 (en) 2002-04-30 2011-03-08 Sas Institute Inc. Marketing optimization system
US7930200B1 (en) 2007-11-02 2011-04-19 Sas Institute Inc. Computer-implemented systems and methods for cross-price analysis
US7996331B1 (en) 2007-08-31 2011-08-09 Sas Institute Inc. Computer-implemented systems and methods for performing pricing analysis
US8024241B2 (en) 2007-07-13 2011-09-20 Sas Institute Inc. Computer-implemented systems and methods for cost flow analysis
US8027871B2 (en) 2006-11-03 2011-09-27 Experian Marketing Solutions, Inc. Systems and methods for scoring sales leads
US8050959B1 (en) 2007-10-09 2011-11-01 Sas Institute Inc. System and method for modeling consortium data
US8065262B2 (en) 2003-10-17 2011-11-22 Sas Institute Inc. Computer-implemented multidimensional database processing method and system
US8200518B2 (en) 2008-02-25 2012-06-12 Sas Institute Inc. Computer-implemented systems and methods for partial contribution computation in ABC/M models
US8296182B2 (en) 2008-08-20 2012-10-23 Sas Institute Inc. Computer-implemented marketing optimization systems and methods
US8533322B2 (en) 2007-11-19 2013-09-10 Experian Marketing Solutions, Inc. Service for associating network users with profiles
US8732004B1 (en) 2004-09-22 2014-05-20 Experian Information Solutions, Inc. Automated analysis of data to generate prospect notifications based on trigger events
US9152727B1 (en) 2010-08-23 2015-10-06 Experian Marketing Solutions, Inc. Systems and methods for processing consumer information for targeted marketing applications
US9508092B1 (en) 2007-01-31 2016-11-29 Experian Information Solutions, Inc. Systems and methods for providing a direct marketing campaign planning environment
US9767309B1 (en) 2015-11-23 2017-09-19 Experian Information Solutions, Inc. Access control system for implementing access restrictions of regulated database records while identifying and providing indicators of regulated database records matching validation criteria
US10019508B1 (en) 2014-05-07 2018-07-10 Consumerinfo.Com, Inc. Keeping up with the joneses
US10078868B1 (en) 2007-01-31 2018-09-18 Experian Information Solutions, Inc. System and method for providing an aggregation tool
US10102536B1 (en) 2013-11-15 2018-10-16 Experian Information Solutions, Inc. Micro-geographic aggregation system
US10121194B1 (en) 2006-10-05 2018-11-06 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US10242019B1 (en) 2014-12-19 2019-03-26 Experian Information Solutions, Inc. User behavior segmentation using latent topic detection
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US10262362B1 (en) 2014-02-14 2019-04-16 Experian Information Solutions, Inc. Automatic generation of code for attributes
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US7499868B2 (en) 1999-08-06 2009-03-03 Marketswitch Corporation Method for optimizing net present value of a cross-selling marketing campaign
US6993493B1 (en) * 1999-08-06 2006-01-31 Marketswitch Corporation Method for optimizing net present value of a cross-selling marketing campaign
US8285577B1 (en) 1999-08-06 2012-10-09 Experian Information Solutions, Inc. Method for optimizing net present value of a cross-selling marketing campaign
US8015045B2 (en) 1999-08-06 2011-09-06 Experian Information Solutions, Inc. Method for optimizing net present value of a cross-selling marketing campaign
US7904327B2 (en) 2002-04-30 2011-03-08 Sas Institute Inc. Marketing optimization system
US8065262B2 (en) 2003-10-17 2011-11-22 Sas Institute Inc. Computer-implemented multidimensional database processing method and system
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Publication number Publication date
AU769761B2 (en) 2004-02-05
AU6400900A (en) 2001-03-05
WO2001011522A8 (en) 2001-12-27
CA2381349A1 (en) 2001-02-15
JP2003526139A (ja) 2003-09-02
EP1212717A2 (en) 2002-06-12

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