The present invention relates generally to the management of marketing offers and, more particularly, to system and method for identifying optimal marketing offers.
A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner of this material has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the U.S. Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.
As with many other businesses, those within the financial services industry have endeavored to identity and focus their marketing practices as efficiently as possible so as to maximize their return on investment. Unfortunately, existing financial institution marketing practices have heretofore failed to sufficiently identify marketing opportunities with a significant likelihood of success. Rather, typical financial services marketing campaigns revolve primarily upon indiscriminate mass mailings, premised on the hope that a statistical percentage of receiving prospects may be open to the offers included within the mailing, failing to take any additional factors into account, such as same or similar offer proximity, time degradation, etc.
Accordingly, there is a need in the art of financial services marketing for a system and method for identifying an optimal product to offer a prospect at the individual level. In addition, there is a need for an optimal marketing offer determination system incorporating time degradation of financial characteristics, and to introduce prospect-level, household-level, and offer-level constraints.
The present invention overcomes or mitigates the problems noted above, and provides additional advantages, by providing a system and method for analyzing potential offers in a mass mailing marketing campaign, and to determine optimal offer(s) for mailing to a particular prospect at a particular time in a multi-wave campaign. In accordance with one embodiment of the present invention, offer information is initially collected for each of the various offers tagged for potential inclusion into a marketing campaign. Once the offer information has been collected, each offer is analyzed to determine whether it is eligible for inclusion in a mailing to a particular household or individual prospect within a household. In particular, when analyzing each mailing of the campaign, the suitability for mailing to each prospect and household is determined in a systematic manner, based upon a plurality of predetermined eligibility criteria. In this manner, offers failing such criteria can be easily removed from inclusion in the mailing.
Once an offer is determined to be eligible for inclusion in a mailing, an estimated net present value (NPV) for the offer given a predetermined degradation factor may be determined. NPV may be calculated based on 36 months of discounted net cash flows. NPV is intended to measure the overall profitability of an offer in a pre-specified period of time. Other essential financial figures may be derived along with NPV, such as CPA (average cost per acquisition), payback period (time to break even), loss estimates (NCL) in each of year 1, 2 and 3, and yearly return on outstanding. Additionally, a net response rate (NRR) for the offer may be determined. Next, a listing of offers in order of either estimated NPV or NRR is output to enable selection of the best offer combination to be included in a particular mailing to a particular prospect.
The system and method of the present invention is designed to take the output from a prospect-level profit and loss application and determine the optimal marketing offer(s) to make to each prospect in a target population. Optimization can be based on any pre-specified criteria that can be calculated at the prospect level. The system incorporates prospect-level, household-level, and offer-level constraints and can be applied in a multi-wave marketing campaign to determine the optimal offer(s) to make at each point in time. Additionally, the present invention computes time-degradation factors for financial inputs that go into the optimality decision.
The system of the present invention incorporates input degradation across time, by dynamically updating the input parameter sets. Consequently, the system and method of the present invention result in increased efficiency in acquisitions campaigns, that is, fewer mail pieces are needed to acquire the same number of customer responses or new accounts as in the past. Similarly, the present invention also improves overall net present value by mailing offers that generate higher revenues.
To achieve these advantages and in accordance with the purpose of the invention, as embodied and broadly described herein, a method for identifying optimal marketing offers comprises: collecting and analyzing information associated with a plurality of potential marketing offers; identifying a plurality of marketing offers, from the plurality of potential marketing offers, that are eligible for inclusion in a mailing campaign, based on a plurality of predetermined criteria and the collected information, where the plurality of potential marketing offers are evaluated for eligibility on a household level, a prospect level and an offer level; calculating a measure of profitability or response rate for each of the identified eligible marketing offers; and identifying at least one optimal marketing offer from the eligible marketing offers based at least in part on the measure of profitability or response rate for each of the eligible marketing offers.
In a further aspect, a computer readable medium having code for causing a processor to identify optimal marketing offers comprises: code adapted to collect and analyze information associated with a plurality of potential marketing offers; code adapted to identify a plurality of marketing offers, from the plurality of potential marketing offers, that are eligible for inclusion in a mailing campaign, based on a plurality of predetermined criteria and the collected information, where the plurality of potential marketing offers are evaluated for eligibility on a household level, a prospect level and an offer level; code adapted to calculate a measure of profitability or response rate for each of the identified eligible marketing offers; and code adapted to identify at least one optimal marketing offer from the eligible marketing offers based at least in part on the measure of profitability or response rate for each of the eligible marketing offers.
In another aspect, a system for identifying optimal marketing offers comprises: a data analysis module for collecting and analyzing information associated with a plurality of potential marketing offers; an identification module for identifying a plurality of marketing offers, from the plurality of potential marketing offers, that are eligible for inclusion in a mailing campaign, based on a plurality of predetermined criteria and the collected information, where the plurality of potential marketing offers are evaluated for eligibility on a household level, a prospect level and an offer level; a calculation module for calculating a measure of profitability or response rate for each of the identified eligible marketing offers; and an optimization module for identifying at least one optimal marketing offer from the eligible marketing offers based at least in part on the measure of profitability or response rate for each of the eligible marketing offers.
In yet another aspect, a system for identifying optimal marketing offers comprises: means for collecting and analyzing information associated with a plurality of potential marketing offers; means for identifying a plurality of marketing offers, from the plurality of potential marketing offers, that are eligible for inclusion in a mailing campaign, based on a plurality of predetermined criteria and the collected information, where the plurality of potential marketing offers are evaluated for eligibility on a household level, a prospect level and an offer level; means for calculating a measure of profitability or response rate for each of the identified eligible marketing offers; and means for identifying at least one optimal marketing offer from the eligible marketing offers based at least in part on the measure of profitability or response rate for each of the eligible marketing offers.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed.
The present invention can be understood more completely through the following detailed description of the invention, in conjunction with the accompanying drawings.
Referring to the figures and, more particularly, to
Once the offer information has been collected, each offer may be analyzed at step 102 to determine whether it is eligible for inclusion in a mailing to a particular household or individual prospect within a household. In particular, when analyzing each mailing of the campaign, the suitability for mailing to each prospect and household may be determined in a systematic manner, based upon predetermined eligibility criteria. In this manner, offers failing such criteria may be easily removed from inclusion in the mailing being analyzed, thereby saving unnecessary costs. Additional specifics regarding the eligibility determination mechanism will be set forth in detail below. Once an offer is determined to be eligible for inclusion in a mailing, the estimated NPV for the offer given a predetermined degradation factor may be calculated at step 104. Additionally, the net response rate for the offer may be calculated at step 106. Additional details regarding NPV and NRR calculation will also be set forth below. Next, at step 108, a listing of offers in order of either estimated NPV or NRR may be output to enable selection of the best offer combination to be included in a given mailing.
The extract of computer code in Table 1 exemplifies the above-described collection and set-up of offer parameter information and has been written in the SAS Version 8 computer language. It should be understood that the techniques applied herein are merely exemplary and may be embodied in different manners utilizing any desired computer programming language (e.g., Visual Basic, C++, etc.). Comments included within the code (demarcated by each pair of /* */ characters) are provided to assist in the interpretation and understanding of the programming.
Referring now to
At step 206, mailing information may be initialized, signifying that no offers have currently been assigned to the prospect. In one embodiment, the initialization of the mailing information includes setting an array of mailing counters related to mailing number, price point (NS_ID), and partner (NS_NAME) equal to zero. Next, at step 208, for each prospect, optimal offer information may be initialized to clear out any previously determined optimal offer information.
The extract of computer code in Table 2 exemplifies the above-described initialization process and has been written in the SAS Version 8 computer language. As above, it should be understood that the techniques applied herein are merely exemplary and may be embodied in different manners utilizing any desired computer programming language.
Referring now to
Referring now to
Referring now to
If the payback_month value is outside of the identified range, the offer may be deemed ineligible for inclusion at step 510. Next, at step 512, it may be determined whether the Y3NCL value for the offer is less than a predetermined maximum value. An offer's Y3NCL value relates to the perceived loss rate expected for the offer. In one embodiment, an eligible Y3NCL rate must be less than 6.5%. If the Y3NCL rate is 6.5% or greater of a given offer, the offer may be deemed ineligible for inclusion at step 514. However, if the offer's Y3NCL rate is less than the predetermined maximum, it may be next determined at step 516 whether the offer's expected return on outstanding (ROO) is greater than a predetermined value. In one embodiment the ROO constraints minimum limit may be zero. Additionally, an offer's ROO Sensitivity constraint may be calculated as: Y3ROO−ROOfactor−Y3NCL*NCLfactor>0. This ROO Sensitivity constraint requires that the third year return on outstanding of an offer must be above the sum of two quantities: (1) a minimum threshold specified by the value ROOfactor, and (2) a certain fraction of the perceived losses Y3NCL, specified by the value NCLfactor. If the offer's ROO Sensitivity constraint is violated, then the offer cannot generate sufficient return and the offer may be deemed ineligible for inclusion at step 518. Next, at step 520, it may be determined whether any other offers from the same partner have been repeated in the same mailing at a different price point. If so, the offer may be deemed ineligible at step 522. However, if it is determined that no other offers from the same partner have been repeated in the same mailing at a different price point, the offer may be deemed eligible for inclusion at the offer-level at step 524.
The extract of computer code in Table 3 exemplifies the above-described eligibility determinations and has been written in the SAS Version 8 computer language. As above, it should be understood that the techniques applied herein are merely exemplary and may be embodied in different manners utilizing any desired computer programming language.
For offers meeting the eligibility requirements at each of the household, prospect, and offer-levels, expected NPV's and NRR's may be calculated as set forth at steps 104 and 106, above. Referring now to
Next, at step 606, a NewNPV value may be calculated representing the offer's degraded net present value. In one embodiment, an offer's NewNPV may be calculated according to the following expression: NPV+(1−(1/DeGraFactor))×(1−TaxRate)×(CPA), where TaxRate represents a predetermined tax rate to be applied to each offer calculation and DeGraFactor represents a time-degradation factor. Once the NewNPV and NewNRR values have been calculated, an ENPV or expected NPV value may be calculated as (NewNPV×NewNRR) at step 608.
Once the degraded and estimated values for an offer have been determined, the position of the offer relative to other offers may be determined at step 610. More particularly, the expected NPV (or NRR, if selected for sorting) is compared against the existing maximum value. If the expected NPV (or NRR) is greater than the existing maximum expected NPV (or NRR) and the offer exceeds a predetermined hurdle rate, the current offer may be identified as the offer having the highest expected of profitability (or responsiveness). According to one embodiment of the present invention, the marketing offers may be sorted or ranked based on the calculated values of their NPVs and NRRs, as discussed previously in connection with step 108 of
The extract of computer code in Table 4 exemplifies the above-described ENPV and NewNRR determinations and has been written in the SAS Version 8 computer language. As above, it should be understood that the techniques applied herein are merely exemplary and may be embodied in different manners utilizing any desired computer programming language.
Once the optimal offer for a given prospect and mailing is identified, the various eligibility criteria may be updated to reflect this offer and its impact on the content of potential offers included in subsequent mailings. Referring now to
The extract of computer code in Table 5 exemplifies the above-described eligibility criteria updating process and has been written in the SAS Version 8 computer language. As above, it should be understood that the techniques applied below are merely exemplary and may be embodied in different manners utilizing any desired computer programming language.
By providing a dynamically adaptive system for determining optimal offers for mass mailing campaigns, based upon time-degraded data and eligibility requirements, the present invention substantially increases the efficiency and effectiveness of such campaigns. By systematically analyzing each prospect and household included within a campaign for each available offer, the best available offer is identified. In addition, by including degradation factors into the analysis, the accuracy of the identification is also improved.
By way of example, the operation of System 800 for identifying optimal marketing offers will now be described, according to one embodiment of the present invention. A user of the system may enter a request for offer optimization through User Interface 86. Upon receiving the request, Processor 80 may collect offer-related information by taking input data from the user, by communicating with Financial Database 82, or by taking the output of Profit & Loss Application 84. Then Processor 80 may analyze the information and apply a plurality of predetermined criteria to evaluate eligibility of each marketing offer for inclusion in a mailing campaign. The evaluation may be done on a household level, a prospect level and an offer level for each potential marketing offer, as discussed previously. For every eligible marketing offer identified, a net present value (NPV) and a net response rate (NRR) may be calculated. The eligible marketing offers may be sorted or ranked based on the calculated values of their NPVs and NRRs. Subsequently one or more optimal marketing offers may be identified and reported to the user through User Interface 86. Or Marketing Module 88 may send out marketing offers based on the optimization results. Next the offer-related information in Financial Database 82 may be updated based on the optimization results, mailed marketing offers and subsequent responses. According to embodiments of the invention, instead of being triggered by user inputs, System 800 may be programmed to operate automatically and/or on a regular basis. Marketing offers may be sent and offer-related information may be updated without much intervention from a user.
At this point, it should be appreciated that the system and method for identifying optimal marketing offers, as described herein, is not limited to making offers by mail. Optimal marketing offers may also be communicated to potential customers, i.e. prospects or households, via other media. For example, the offers may be made through electronic mails, facsimile transmission, telephone solicitation, inbound calls or targeted advertisements. The offers may also be made in person by sales representatives. Other ways for communicating the optimal marketing offers also exist.
While the foregoing description includes many details and specificities, it is to be understood that these have been included for purposes of explanation only, and are not to be interpreted as limitations of the present invention. Many modifications to the embodiments described above can be made without departing from the spirit and scope of the invention, as is intended to be encompassed by the following claims and their legal equivalents.
Number | Name | Date | Kind |
---|---|---|---|
3634669 | Soumas et al. | Jan 1972 | A |
3946206 | Darjany | Mar 1976 | A |
4047033 | Malmberg | Sep 1977 | A |
4545838 | Minkus | Oct 1985 | A |
4582985 | Lofberg | Apr 1986 | A |
4594663 | Nagata et al. | Jun 1986 | A |
4634845 | Riley | Jan 1987 | A |
4642768 | Roberts | Feb 1987 | A |
4700055 | Kashkashian | Oct 1987 | A |
4750119 | Cohen | Jun 1988 | A |
4766293 | Boston | Aug 1988 | A |
4831242 | Englehardt | May 1989 | A |
4882675 | Nichtberger | Nov 1989 | A |
4897533 | Lyszczarz | Jan 1990 | A |
4906826 | Spencer | Mar 1990 | A |
4953085 | Atkins | Aug 1990 | A |
4978401 | Bonomi | Dec 1990 | A |
5025372 | Burton | Jun 1991 | A |
5080748 | Bonomi | Jan 1992 | A |
5095194 | Barbanell | Mar 1992 | A |
5117355 | McCarthy | May 1992 | A |
5175416 | Mansvelt | Dec 1992 | A |
5180901 | Hiramatsu | Jan 1993 | A |
5192947 | Neustein | Mar 1993 | A |
5202826 | McCarthy | Apr 1993 | A |
5218631 | Katz | Jun 1993 | A |
5276311 | Hennige | Jan 1994 | A |
5287268 | McCarthy | Feb 1994 | A |
5287269 | Dorrough | Feb 1994 | A |
5297026 | Hoffman | Mar 1994 | A |
5311594 | Penzias | May 1994 | A |
5326960 | Tannenbaum | Jul 1994 | A |
5339239 | Manabe et al. | Aug 1994 | A |
5349633 | Katz | Sep 1994 | A |
5350906 | Brody et al. | Sep 1994 | A |
5365575 | Katz | Nov 1994 | A |
5397881 | Mannik | Mar 1995 | A |
5424524 | Ruppert | Jun 1995 | A |
5450477 | Amarant | Sep 1995 | A |
5459306 | Stein | Oct 1995 | A |
5465206 | Hilt | Nov 1995 | A |
5466919 | Hovakimian | Nov 1995 | A |
5471669 | Lidman | Nov 1995 | A |
5477038 | Levine | Dec 1995 | A |
5479494 | Clitherow | Dec 1995 | A |
5482139 | Rivalto | Jan 1996 | A |
5483444 | Heintzman | Jan 1996 | A |
5500514 | Veeneman | Mar 1996 | A |
5511114 | Stimson | Apr 1996 | A |
5521363 | Tannenbaum | May 1996 | A |
5530232 | Taylor | Jun 1996 | A |
5530235 | Stekfik | Jun 1996 | A |
5537314 | Kanter | Jul 1996 | A |
5544086 | Davis | Aug 1996 | A |
5544246 | Mandelbaum | Aug 1996 | A |
5553120 | Katz | Sep 1996 | A |
5578808 | Taylor | Nov 1996 | A |
5585787 | Wallerstein | Dec 1996 | A |
5637845 | Kolls | Jun 1997 | A |
5644727 | Atkins | Jul 1997 | A |
5689650 | McClelland | Nov 1997 | A |
5703344 | Bezy | Dec 1997 | A |
5710886 | Christensen | Jan 1998 | A |
5721768 | Stimson | Feb 1998 | A |
5729693 | Holda-Fleck | Mar 1998 | A |
5745706 | Wolfberg et al. | Apr 1998 | A |
5765141 | Spector | Jun 1998 | A |
5774870 | Storey | Jun 1998 | A |
5777305 | Smith | Jul 1998 | A |
5787156 | Katz | Jul 1998 | A |
5787404 | Fernndez-Holmann | Jul 1998 | A |
5806042 | Kelly | Sep 1998 | A |
5835576 | Katz | Nov 1998 | A |
5852811 | Atkins | Dec 1998 | A |
5857079 | Claus | Jan 1999 | A |
5857709 | Chock | Jan 1999 | A |
5864828 | Atkins | Jan 1999 | A |
5864830 | Armetta | Jan 1999 | A |
5870718 | Spector | Feb 1999 | A |
5875437 | Atkins | Feb 1999 | A |
5884285 | Atkins | Mar 1999 | A |
5911135 | Atkins | Jun 1999 | A |
5911136 | Atkins | Jun 1999 | A |
5915243 | Smolen | Jun 1999 | A |
5926800 | Baronowski | Jul 1999 | A |
5955961 | Wallerstein | Sep 1999 | A |
5970480 | Kalina | Oct 1999 | A |
5974399 | Giuliani et al. | Oct 1999 | A |
5991750 | Watson | Nov 1999 | A |
6000608 | Dorf | Dec 1999 | A |
6000832 | Franklin et al. | Dec 1999 | A |
6009415 | Shurling et al. | Dec 1999 | A |
6016954 | Abe et al. | Jan 2000 | A |
6032136 | Brake, Jr. et al. | Feb 2000 | A |
6036099 | Leighton | Mar 2000 | A |
6038552 | Fleischl et al. | Mar 2000 | A |
6070153 | Simpson | May 2000 | A |
6105865 | Hardesty | Aug 2000 | A |
6128598 | Walker et al. | Oct 2000 | A |
6128599 | Walker et al. | Oct 2000 | A |
6164533 | Barton | Dec 2000 | A |
6189787 | Dorf | Feb 2001 | B1 |
6227447 | Campisano | May 2001 | B1 |
6236977 | Verba et al. | May 2001 | B1 |
6243688 | Kalina | Jun 2001 | B1 |
6341724 | Campisano | Jan 2002 | B2 |
6345261 | Feidelson et al. | Feb 2002 | B1 |
6615189 | Phillips et al. | Sep 2003 | B1 |
6925441 | Jones et al. | Aug 2005 | B1 |
6970830 | Samra et al. | Nov 2005 | B1 |
20020095365 | Slavin | Jul 2002 | A1 |
20030154129 | Goff | Aug 2003 | A1 |
20030187717 | Crites et al. | Oct 2003 | A1 |
20030187767 | Crites et al. | Oct 2003 | A1 |
20040039679 | Norton et al. | Feb 2004 | A1 |
20040044615 | Xue et al. | Mar 2004 | A1 |
20040093296 | Phelan et al. | May 2004 | A1 |
20040128194 | Mase et al. | Jul 2004 | A1 |
Number | Date | Country |
---|---|---|
2293321 | Dec 1998 | CA |