AU769761B2 - 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

Info

Publication number
AU769761B2
AU769761B2 AU64009/00A AU6400900A AU769761B2 AU 769761 B2 AU769761 B2 AU 769761B2 AU 64009/00 A AU64009/00 A AU 64009/00A AU 6400900 A AU6400900 A AU 6400900A AU 769761 B2 AU769761 B2 AU 769761B2
Authority
AU
Australia
Prior art keywords
customer
aij
constraints
value
calculating
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.)
Expired
Application number
AU64009/00A
Other versions
AU6400900A (en
Inventor
Vladimir Fishman
Yuri Galperin
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 AU6400900A publication Critical patent/AU6400900A/en
Application granted granted Critical
Publication of AU769761B2 publication Critical patent/AU769761B2/en
Anticipated expiration legal-status Critical
Expired legal-status Critical Current

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q30/00Commerce
    • G06Q30/02Marketing; Price estimation or determination; Fundraising

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)

Description

WO 01111522 wool/ 1522PCTIUSOO/21453 TITLE: Method for Optimizing Net Present Value of a Cross-Selling Marketing Campaign FIELD OF THE INVENTION 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.
BACKGROUND OF THE INVENTION 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.
Some promotions may be combined into Peer Groups groups of mutually exclusive offers, such as a credit card with differenrt 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.
As a result, the best, most responsive and valuable customers are saturated with offers and the middle segment of the customer list is ignored. The overall efficiency of the P:\WPDOCS\RET 7672160CLAIMS.doc-21/10/03 -2campaign therefore degrades.
Another significant drawback of this approach is the inability to satisfy various real-life constraints and business goals.
Most sophisticated marketers have tried to consolidate models built for different offers. However, these attempts have not been based on any solid scientific method, but rather have utilised an ad hoc approach. Because of this, only the most-simple constraints have been able to be satisfied and the solutions have been sub-optimal with respect to a utility function. In fact, these marketers haven't even been able to estimate how far off they are from the true optimum.
What would therefore be useful is a process that provides a mathematically optimal offer allocation, i. one that selects an optimal set of offers for each customer that maximises the utility function and satisfies all business goals and constraints.
SUMMARY OF THE INVENTION The present invention represents the application of a novel iterative algorithm to the problem of multidimensional optimisation. The present invention supplies a strict, nonlinear mathematical solution to what has traditionally been treated as a linear S•multidimensional problem.
The problem in its original form is a problem of discrete linear programming.
However, due to a huge number of dimensions (in a typical business case N 0(108), M 0(102)), the application of classical methods of discrete linear programming is not feasible.
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 S"iterative algorithm, and using the results to calculate an optimal set of offers in one pass for the full prospect list.
oee The present invention seeks to increase the efficiency of a cross-selling marketing 30 campaign.
~1I?~111 Lllii ~ii. )llr.-il 1-till 11 11*.lil(i_ 111 111 I iii_-iii P:\WPDOCS\RET/7672160CLAIMS.doc-2 1/10/03 -3- The present invention also seeks to increase the efficiency of cross-selling campaigns that include a large number of offers.
The present invention also seeks to provide optimisation of cross-selling campaigns wherein groups of offers can be mutually exclusive.
The present invention also seeks to increase the efficiency of cross-selling campaigns that are targeted to large number of prospective customers.
The present invention also seeks to increase the efficiency of cross-selling campaigns by selecting an individual, optimal set of offers for each customer.
The present invention also seeks to constrain of maximum number of offers sent to a customer within cross-selling campaigns.
The present invention also seeks to satisfy business goals, like minimum number of sales and budget constraints, while optimising cross-selling campaigns as applied to individual offers, groups of offers or the entire campaign.
The present invention also seeks to maximise a user-chosen utility function, like total NPV or number of sales, within a cross-selling campaign.
The present invention also seeks to mathematically maximise the utility function and satisfy all constraints within a cross-selling campaign.
The present invention also seeks to allow interactive changes in goals or constraints 0o of cross-selling campaigns and quickly view the results.
20 The present invention also seeks to provide final scoring for cross-selling campaigns in a single pass so as to scalable and efficient enough to process a list of 100 million customers overnight.
The present invention also seeks to provide true "one-to-one" marketing in crossselling campaigns.
BRIEF DESCRIPTION OF THE DRAWINGS SFigure 1 is a flow chart of the basic process of the present invention.
S:-i Figure 2 is a more detailed data flow of a marketing optimisation process of the present •invention.
o• WO 01/11522 PCT/US00/21453 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.
DETAILED DESCRIPTION OF THE 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, as shown in figure 1, 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.
Let be a solicitation matrix, where aj= 1, if offer j goes to a customer i 0, otherwise; R (ri) be a response matrix, where rij is a probability for a customer i respond to a promotion j; P be a profitability matrix, where pj is a profitability of a customer i, if he/she responds to a promotion j.
WO 01/11522 PCT/US00/21453 Total NPV of the campaign, NPV NPV( A, R, is a linear function ofa., ri, p.
and other economic parameters of the campaign.
Eligibility conditions, peer group logic, and maximal number of offers per customer constraint can be expressed by a set of inequalities C a Cik(A)<=0, i k= 1,2, ,K where C i are linear functions, and N is of the order of number of customers in the prospect list, K is number of restrictions. These customer-level restrictions are applied for each individual. Economic goals are expressed by a set of inequalities G for each promotion and the whole campaign: j= 1,2, 1= Go( A, R, P) 0, 1, 2, Lo where Gj are linear functions, and M is of the order of number of promotions in the campaign, Lj 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.
It is desired to then find a solicitation matrix A that maximizes NPV(A,*) under the condition that all constraints C and G are satisfied.
The solution presented by the inventors uses the following steps, as shown in figure 2. 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. 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.
For prospect input, one selects, randomly, a statistically significant sample or testing DCP (derived customer pool) 212 of a prospect list from a customer database 210.
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 ^L ~1~YtUf~.
WO 01/11522 PCT/US00/21453 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 NPV)functions ANPVj(L, where j 1, 2, of promotions; ri (ri) vector of propensities to respond of a customer i to promotions 1, 2, Pi (Pj) vector of profitability of a customer i for promotions 1, It then calculates the optimal solicitation matrix 250 in a single pass through the full prospect list. To accomplish that, as shown in figure 3: 1. Read the next customer record 31; 2. Calculate vectors ri and p, 32; 3. Calculate anpv i (ANPVj(L, ri, Pi), j 1, 2, of promotions) 33; 4. Based on the values ofanpv, and eligibility conditions, calculate solicitation vector a, aj, j= 1, of promotions which defines the optimal set of promotions that goes to a customer i at 34; and Repeat the previous four steps until the end of the customer list at 6 WO 01/11522 PCTfUSOO/21453 To calculate matrices P and R for selected prospects at 224 and reduce the original linear multidimensional problem to a non-linear problem with a feasible number of dimensions described above, 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.
At each iterative step, the optimization ofANPVj(L, ri, pi) under customer-level restrictions (high dimensional linear problem) is made directly, record by record. It is equivalent to the following min-max problem: Min,Lb>O,Lc) Maxc<-.o ANPV(L, ri, Pi), where ANPV(L, pi) ANPV(L, ri, pi)o Lb Gb( A, R, P) L, A, R, P) Here, summation over all the inequalities is assumed.
The algorithm, as shown in figure 4, consists of following steps: 1. Prepare data 41.
2. Calculate initial value of the functional and gradients 42.
3. Set a value for initial algorithm steps 43; for each Lagrange multiplier, the step should be set equal to the initial value of the functional divided by the square of the gradient.
4. Make a step along the gradient 44.
Update the step 45, if needed.
6. Calculate new value of the functional 46, taking customer level restrictions into account.
7. Check convergence 47.
8. If not converged at 48, go to step 4.
9. Output the results 49 upon adequate convergence.
It is important to underscore that the above algorithm is not a heuristic, but delivers a strict mathematical solution for the multidimensional optimization problem formulated above.
P:\WPDOCSRET\7672160CLAIMS.doc-21/10/03 -8- Tests performed by inventors on a variety of real business cases show that the iterative procedure in Step 4 above typically converges with the tolerance of 0.1 in less then 30 iterations. That allows a user to work with the cross-selling optimiser of the present invention interactively and perform real-time analysis of the financial outcome of marketing activities.
A novel feature of the algorithm used by the present invention, the one-pass scoring, 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.
Throughout this specification and the claims which follow, unless the context requires otherwise, the word "comprise", and variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.
The reference to any prior art in this specification is not, and should not be taken as, an acknowledgement or any form of suggestion that that prior art forms part of the common general knowledge in Australia.
0 -1 "I n

Claims (9)

1. A computer-implemented method for optimizing a cross-selling marketing campaign that includesj 1 to M promotions, targeting i 1 to N customers, said method calculating an NxM solicitation matrix A (aij), where each aij is to be set to a first value when said specific promotionj is to be offered to said specific customer i, or otherwise is to be set to a second value, and comprising the steps of: accessing a customer database and randomly selecting a statistically significant sample of n customer records from N customer records; calculating an nxM response matrix R (rij), where each rij is the probability that a specific customer i within said n customers will respond to a specific promotionj; calculating an nxM profitability matrix P (pij), where each Pij is the profitability of said specific customer i when they positively respond to said specific promotionj; 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; defining a set of NxK 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 I to K constraints is reflective of a constraint selected from the group :o consisting of: an eligibility condition constraint, a peer group logic constraint and a maximum number of offers constraint; 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 nxM variables; deriving a non-linear problem that is mathematically equivalent to said 0•06% .integer optimization problem having Q dimensions; 30 iteratively solving said non-linear problem on said sample of n customer records within a pre-defined tolerance; and A4~;t44M.,44~MtM.-~/< P:\WPDOCS\RET17672160CLAIMS.doc-21/10/03 accessing said customer database and using the solution of the said non- linear problem to calculate each aij of said NxM solicitation matrix A, wherein the values set for each aij within said solicitation matrix A is a solution to said integer optimization problem.
2. The method of Claim 1, wherein M is in the order of magnitude of 10 3
3. The method of Claim 1, wherein Nis in the order of magnitude of 108.
4. The method of Claim 1, wherein said first value is 1 and said second value is 0. The method of Claim 1, wherein said pre-defined tolerance is about 0.1%.
6. The method of Claim 1, wherein said utility function selected in step is a net present value (NPV) function expressed as: NPV(A, R, P).
7. The method of Claim 6, wherein step is accomplished by formulating a min- 0* max problem using a LaGrange multiplier technique.
8. The method of Claim 7, wherein said min-max problem is expressed as: Minj{Lb>o,Lc} Maxj ANPVj(L, ri, pi); wherein ANPV(L, ri, Pi) NPVj(L, ri, Pi) LbGb( A, R, P) Lc Gc( A, R, and wherein each of Lb is the inequality component of a LaGrange multiplier, and each of Lc is the equality component of said LaGrange multiplier.
9. The method of Claim 8, wherein step is accomplished by iteratively solving said min-max problem by performing at least the following steps: calculating an initial value of said ANPV function and a gradient vector; setting an initial value; setting each of Q number of LaGrange multipliers to said initial value of said ANPV function divided by the square of said gradient vector; ~ii~ i -I P:\WPDOCSNRET7672160CLAIMS,do-21/10/03 11 making a step along said gradient vector; updating said step size when needed; calculating a new value of said ANPV function taking said set of K constraints into account; and determining whether there is convergence within said pre-defined tolerance. The method of Claim 8, wherein step is accomplished in a single pass through said N customer records by performing the following steps for each of said N customers: reading a customer record i; calculating vectors ri and pi; calculating anpvi ANPVj(L, ri, pi); and using said calculated value of anpvi and said k 1 to K constraints to calculate said solicitation matrix vector ai (aij).
11. A method for optimizing a cross-selling marketing campaign, substantially as herein disclosed with reference to the figures. DATED this 2 0 th day of October, 2003 MARKETSWITCH COPORATION 20 By Their Patent Attorneys DAVIES COLLISON CAVE ao... o
AU64009/00A 1999-08-06 2000-08-05 Method for optimizing net present value of a cross-selling marketing campaign Expired AU769761B2 (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US14745699P 1999-08-06 1999-08-06
US60/147456 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 (2)

Publication Number Publication Date
AU6400900A AU6400900A (en) 2001-03-05
AU769761B2 true AU769761B2 (en) 2004-02-05

Family

ID=22521639

Family Applications (1)

Application Number Title Priority Date Filing Date
AU64009/00A Expired AU769761B2 (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 (en)
JP (1) JP2003526139A (en)
AU (1) AU769761B2 (en)
CA (1) CA2381349A1 (en)
WO (1) WO2001011522A2 (en)

Families Citing this family (43)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6993493B1 (en) 1999-08-06 2006-01-31 Marketswitch Corporation Method for optimizing net present value of a cross-selling marketing campaign
AU2003237135A1 (en) 2002-04-30 2003-11-17 Veridiem Inc. Marketing optimization system
US7831615B2 (en) 2003-10-17 2010-11-09 Sas Institute Inc. Computer-implemented multidimensional database processing method and system
US8346593B2 (en) 2004-06-30 2013-01-01 Experian Marketing Solutions, Inc. System, method, and software for prediction of attitudinal and message responsiveness
US7689528B2 (en) 2004-07-09 2010-03-30 Fair Isaac Corporation Method and apparatus for a scalable algorithm for decision optimization
US8732004B1 (en) 2004-09-22 2014-05-20 Experian Information Solutions, Inc. Automated analysis of data to generate prospect notifications based on trigger events
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
US7711636B2 (en) 2006-03-10 2010-05-04 Experian Information Solutions, Inc. Systems and methods for analyzing data
US7813948B2 (en) 2006-08-25 2010-10-12 Sas Institute Inc. Computer-implemented systems and methods for reducing cost flow models
US8036979B1 (en) 2006-10-05 2011-10-11 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US8027871B2 (en) 2006-11-03 2011-09-27 Experian Marketing Solutions, Inc. Systems and methods for scoring sales leads
US8606626B1 (en) 2007-01-31 2013-12-10 Experian Information Solutions, Inc. Systems and methods for providing a direct marketing campaign planning environment
US8606666B1 (en) 2007-01-31 2013-12-10 Experian Information Solutions, Inc. System and method for providing an aggregation tool
WO2008147918A2 (en) 2007-05-25 2008-12-04 Experian Information Solutions, Inc. System and method for automated detection of never-pay data sets
US8024241B2 (en) 2007-07-13 2011-09-20 Sas Institute Inc. Computer-implemented systems and methods for cost flow analysis
US7996331B1 (en) 2007-08-31 2011-08-09 Sas Institute Inc. Computer-implemented systems and methods for performing pricing analysis
US8050959B1 (en) 2007-10-09 2011-11-01 Sas Institute Inc. System and method for modeling consortium data
US7930200B1 (en) 2007-11-02 2011-04-19 Sas Institute Inc. Computer-implemented systems and methods for cross-price analysis
US7996521B2 (en) 2007-11-19 2011-08-09 Experian Marketing Solutions, Inc. Service for mapping IP addresses to user segments
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
US20100174638A1 (en) 2009-01-06 2010-07-08 ConsumerInfo.com Report existence monitoring
US8639920B2 (en) 2009-05-11 2014-01-28 Experian Marketing Solutions, Inc. Systems and methods for providing anonymized user profile data
US9652802B1 (en) 2010-03-24 2017-05-16 Consumerinfo.Com, Inc. Indirect monitoring and reporting of a user's credit data
US9152727B1 (en) 2010-08-23 2015-10-06 Experian Marketing Solutions, Inc. Systems and methods for processing consumer information for targeted marketing applications
US8930262B1 (en) 2010-11-02 2015-01-06 Experian Technology Ltd. Systems and methods of assisted strategy design
WO2012112781A1 (en) 2011-02-18 2012-08-23 Csidentity Corporation System and methods for identifying compromised personally identifiable information on the internet
US11030562B1 (en) 2011-10-31 2021-06-08 Consumerinfo.Com, Inc. Pre-data breach monitoring
US10255598B1 (en) 2012-12-06 2019-04-09 Consumerinfo.Com, Inc. Credit card account data extraction
US8812387B1 (en) 2013-03-14 2014-08-19 Csidentity Corporation System and method for identifying related credit inquiries
US10102536B1 (en) 2013-11-15 2018-10-16 Experian Information Solutions, Inc. Micro-geographic aggregation system
US10262362B1 (en) 2014-02-14 2019-04-16 Experian Information Solutions, Inc. Automatic generation of code for attributes
US9576030B1 (en) 2014-05-07 2017-02-21 Consumerinfo.Com, Inc. Keeping up with the joneses
US11257117B1 (en) 2014-06-25 2022-02-22 Experian Information Solutions, Inc. Mobile device sighting location analytics and profiling system
US10339527B1 (en) 2014-10-31 2019-07-02 Experian Information Solutions, Inc. System and architecture for electronic fraud detection
US10445152B1 (en) 2014-12-19 2019-10-15 Experian Information Solutions, Inc. Systems and methods for dynamic report generation based on automatic modeling of complex data structures
US11151468B1 (en) 2015-07-02 2021-10-19 Experian Information Solutions, Inc. Behavior analysis using distributed representations of event data
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
WO2018039377A1 (en) 2016-08-24 2018-03-01 Experian Information Solutions, Inc. Disambiguation and authentication of device users
US10699028B1 (en) 2017-09-28 2020-06-30 Csidentity Corporation Identity security architecture systems and methods
US10896472B1 (en) 2017-11-14 2021-01-19 Csidentity Corporation Security and identity verification system and architecture
US11682041B1 (en) 2020-01-13 2023-06-20 Experian Marketing Solutions, Llc Systems and methods of a tracking analytics platform

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6064973A (en) * 1998-04-17 2000-05-16 Andersen Consulting Llp Context manager and method for a virtual sales and service center
US6070142A (en) * 1998-04-17 2000-05-30 Andersen Consulting Llp Virtual customer sales and service center and method
US6115693A (en) * 1998-04-17 2000-09-05 Andersen Consulting Llp Quality center and method for a virtual sales and service center

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6064973A (en) * 1998-04-17 2000-05-16 Andersen Consulting Llp Context manager and method for a virtual sales and service center
US6070142A (en) * 1998-04-17 2000-05-30 Andersen Consulting Llp Virtual customer sales and service center and method
US6115693A (en) * 1998-04-17 2000-09-05 Andersen Consulting Llp Quality center and method for a virtual sales and service center

Also Published As

Publication number Publication date
WO2001011522A2 (en) 2001-02-15
JP2003526139A (en) 2003-09-02
WO2001011522A8 (en) 2001-12-27
CA2381349A1 (en) 2001-02-15
AU6400900A (en) 2001-03-05
EP1212717A2 (en) 2002-06-12

Similar Documents

Publication Publication Date Title
AU769761B2 (en) Method for optimizing net present value of a cross-selling marketing campaign
US6993493B1 (en) Method for optimizing net present value of a cross-selling marketing campaign
US6901406B2 (en) Methods and systems for accessing multi-dimensional customer data
McPherson Growth of micro and small enterprises in southern Africa
Mahajan et al. Diffusion of new products: Empirical generalizations and managerial uses
CN109272356A (en) Optimal Bidding Strategies method based on AdWords
Cabena et al. Intelligent miner for data applications guide
WO1994016394A2 (en) System and method for the advanced prediction of weather impact on managerial planning applications
WO2012037578A2 (en) Sales prediction and recommendation system
Mandapuram et al. Application of artificial intelligence (AI) technologies to accelerate market segmentation
Batt Modelling buyer-seller relationships in agribusiness in South East Asia
Dordunoo The structure and policy implications of a macroeconomic model of Ghana
Furness Techniques for customer modelling in CRM
Villena et al. Global and local advertising strategies: A dynamic multi-market optimal control model.
CN110930237B (en) SAGDO algorithm-based small and medium-sized enterprise credit prediction classification method
Xu et al. Forecasting for products with short life cycle based on improved BASS model
Chaudhry CRM: Making it simple for the Banking Industry
Chavas et al. Bargaining in the Family
Robalino et al. Poverty reduction through fiscal restructuring: An application to Thailand
Scandizzo et al. Beyond Cost Benefit Analysis: A SAM-CGE Model for Project-Program Evaluation
Grunt et al. Modeling of Marketing Processes Using Markov Decision Process Approach
Jamalpur et al. Applications of Deep Learning in Marketing Analytics: Predictive Modeling and Segmenting Customers
Zhang et al. Precise Issuance of Meituan Merchants’ Coupons with Machine Learning
Mittal Using Predictive Analytics to Measure the Effectiveness of Select Direct Marketing Attributes in the Banking Sector
Bhattacharjee et al. NiReMS: a regional model at household level combining spatial econometrics with dynamic microsimulation

Legal Events

Date Code Title Description
FGA Letters patent sealed or granted (standard patent)
MK14 Patent ceased section 143(a) (annual fees not paid) or expired