EP1212717A2 - 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

Info

Publication number
EP1212717A2
EP1212717A2 EP00950995A EP00950995A EP1212717A2 EP 1212717 A2 EP1212717 A2 EP 1212717A2 EP 00950995 A EP00950995 A EP 00950995A EP 00950995 A EP00950995 A EP 00950995A EP 1212717 A2 EP1212717 A2 EP 1212717A2
Authority
EP
European Patent Office
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.)
Withdrawn
Application number
EP00950995A
Other languages
German (de)
English (en)
French (fr)
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.)
Experian Information Solutions LLC
Original Assignee
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
Publication of EP1212717A2 publication Critical patent/EP1212717A2/en
Withdrawn legal-status Critical Current

Links

Classifications

    • 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.

Landscapes

  • 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)
EP00950995A 1999-08-06 2000-08-05 Method for optimizing net present value of a cross-selling marketing campaign Withdrawn EP1212717A2 (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US14745699P 1999-08-06 1999-08-06
US147456P 1999-08-06
PCT/US2000/021453 WO2001011522A2 (en) 1999-08-06 2000-08-05 Method for optimizing net present value of a cross-selling marketing campaign

Publications (1)

Publication Number Publication Date
EP1212717A2 true EP1212717A2 (en) 2002-06-12

Family

ID=22521639

Family Applications (1)

Application Number Title Priority Date Filing Date
EP00950995A Withdrawn EP1212717A2 (en) 1999-08-06 2000-08-05 Method for optimizing net present value of a cross-selling marketing campaign

Country Status (5)

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

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US8024241B2 (en) 2007-07-13 2011-09-20 Sas Institute Inc. Computer-implemented systems and methods for cost flow analysis
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Also Published As

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
WO2001011522A2 (en) 2001-02-15

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