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

  • US 6,993,493 B1
  • Filed: 08/05/2000
  • Issued: 01/31/2006
  • Est. Priority Date: 08/06/1999
  • Status: Expired due to Term
First Claim
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1. A computer-implemented method for optimizing a cross-selling marketing campaign that includes j=1 to M promotions, targeting i=1 to N customers, said method calculating an N×

  • M solicitation matrix A=(aij), where each aij is to be set to a first value when said specific promotion j is to be offered to said specific customer i, or otherwise is to be set to a second value, and comprising the steps of;

    (a) accessing a customer database and randomly selecting a statistically significant sample of n customer records from N customer records;

    (b) calculating an n×

    M response matrix R=(rij), where each rij is the probability that a specific customer i within said n customers will respond to a specific promotion j;

    (c) calculating an n×

    M profitability matrix P=(pij), where each pij is the profitability of said specific customer i when they positively respond to said specific promotion j;

    (d) selecting a utility function that is a function of at least said response, profitability and solicitation matrices, said utility function being linear with respect to aij;

    (e) defining a set of N×

    K customer constraint inequalities, Cik(A)≦

    0, wherein K is the total number of customer constraints;

    Cik are linear functions with respect to aij; and

    each of k=1 to K constraints is reflective of a constraint selected from the group consisting of;

    an eligibility condition constraint, a peer group logic constraint and a maximum number of offers constraint;

    (f) defining a set of Q economic constraint inequalities, Gq(A, R, P)≦

    0, wherein Q is the total number of economic constraints;

    Gq are linear functions with respect to aij; and

    each of q=1 to Q constraints is reflective of an economic goal of the cross-selling marketing campaign, and thus formulating an integer optimization problem with n×

    M variables;

    (g) deriving a non-linear problem that is mathematically equivalent to said integer optimization problem having Q dimensions;

    (h) iteratively solving said non-linear problem on said sample of n customer records within a pre-defined tolerance; and

    (i) accessing said customer database and using the solution of the said non-linear problem to calculate each aij of said N×

    M solicitation matrix A, wherein the values set for each aij within said solicitation matrix A is a solution to said integer optimization problem;

    wherein at least one of steps (a) through (i) is performed by a computer.

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