专利汇可以提供PORTFOLIO MODELING AND CAMPAIGN OPTIMIZATION专利检索，专利查询，专利分析的服务。并且In an embodiment of the invention, historical data related to multiple members of a customer loyalty program is gathered. A set of loyalty behavior models is developed for an individual member of the loyalty program is developed based on the historical data. For each campaign in a plurality of marketing campaigns, at least one combination of offers is inserted into each loyalty behavior model to output a plurality of net profit scores for the individual member, wherein each combination of offers outputs a separate net profit score. For each campaign in the plurality of campaigns, a combination of offers having the highest net profit score for the campaign is selected. The campaign having the highest net profit score of the plurality of campaigns is selected, and marketing materials for the selected campaign and combination of offers is transmitted to the individual member.，下面是PORTFOLIO MODELING AND CAMPAIGN OPTIMIZATION专利的具体信息内容。
1. Field of the Invention
The inventions relate in general to customer loyalty programs associated with or operated by financial companies. More specifically, the inventions relate to predicting behavior of members of a loyalty program and applying predictions to campaigns and offers directed to loyalty program members in order to maximize benefits to the financial company.
2. Background Art
Customer loyalty programs, also known as “rewards programs,” have become a widely used tool of financial companies to encourage loyalty and other behaviors typically having some financial impact. Although such programs have become a strategic lever, the use of such programs is expensive. It is becoming increasingly important that marketing and other campaigns directed to loyalty program members be as effective as possible. Loyalty program members are not all the same. They come from different backgrounds, earn their money in different ways, have different expense profiles, etc. Thus, they do not all respond in the same way to specific types of campaigns. What is needed is a way to target the right offers to the right customers at the right times to obtain the best economic leverage of the rewards program membership.
An embodiment of the invention relates to a method and system for managing a customer loyalty program for an individual member of the customer loyalty program. In an embodiment, historical data related to multiple members of a customer loyalty program is gathered. A set of loyalty behavior models may be developed for an individual member of the loyalty program based on the historical data. For each campaign in a plurality of marketing campaigns, at least one combination of offers may be inserted into each loyalty behavior model to output a plurality of net profit scores for the individual member, wherein each combination of offers outputs a separate net profit score. For each campaign in the plurality of campaigns, a combination of offers having the highest net profit score for the campaign may be selected, wherein the net profit score for the selected combination of offers becomes the campaign's net profit score. The campaign having the highest net profit score of the plurality of campaigns may be selected, and marketing materials for the selected campaign and combination of offers may be transmitted to the individual member.
Another embodiment of the invention relates to a method and system for targeting a customer loyalty program member for a marketing campaign. In an embodiment, historical data related to multiple customer loyalty program members is gathered. A set of loyalty behavior models for a given campaign may be developed based on the historical data. A set of baseline behavior models based on the historical data may also be developed. For each individual of a plurality of individuals, at least one attribute of the individual may be inserted into each loyalty behavior model for the given campaign, wherein a separate campaign net profit score is output for each individual. For each individual in the plurality of individuals, at least one attribute of the individual may be inserted into the baseline behavior model, wherein a separate baseline net profit score is output for each individual. The campaign net profit score for each individual may be compared to the baseline net profit score for the individual. From the plurality of individuals, at least one individual having a campaign net profit score that is higher than the baseline net profit score for the individual may be selected. Marketing materials for the given campaign may then be transmitted to the selected at least one individual.
Another embodiment of the invention relates to a method and system for targeting a customer loyalty program member for a new type of marketing campaign. In an embodiment, historical data related to multiple customer loyalty program members is gathered. A set of baseline behavior models based on the historical data may also be developed. For each individual of a plurality of individuals, at least one attribute of the individual may be inserted into the set of baseline behavior models, wherein a separate baseline net profit score is output for each individual. Each individual may then be ranked based on the baseline net profit score output for the individual. From the plurality of individuals, at least one individual may be selected based on the ranked baseline net profit scores. Marketing materials for the new type of campaign may then be transmitted to the selected at least one individual.
Further embodiments, features, and advantages of the present invention, as well as the structure and operation of the various embodiments of the present invention, are described in detail below with reference to the accompanying drawings.
The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate the present invention and, together with the description, further serve to explain the principles of the invention and to enable a person skilled in the pertinent art to make and use the invention.
The present invention will be described with reference to the accompanying drawings. The drawing in which an element first appears is typically indicated by the leftmost digit(s) in the corresponding reference number.
While specific configurations and arrangements are discussed, it should be understood that this is done for illustrative purposes only. A person skilled in the pertinent art will recognize that other configurations and arrangements can be used without departing from the spirit and scope of the present invention. It will be apparent to a person skilled in the pertinent art that this invention can also be employed in a variety of other applications.
The terms “user,” “end user,” “consumer,” “customer,” “participant,” “member,” and/or the plural form of these terms are used interchangeably throughout herein to refer to those persons or entities capable of accessing, using, being affected by and/or benefiting from the tool that the present invention provides for portfolio modeling and campaign selection.
Furthermore, the terms “business” or “merchant” may be used interchangeably with each other and shall mean any person, entity, distributor system, software and/or hardware that is a provider, broker and/or any other entity in the distribution chain of goods or services. For example, a merchant may be a grocery store, a retail store, a travel agency, a service provider, an on-line merchant or the like.
A “transaction account” as used herein refers to an account associated with an open account or a closed account system (as described below). The transaction account may exist in a physical or non-physical embodiment. For example, a transaction account may be distributed in non-physical embodiments such as an account number, frequent-flyer account, telephone calling account or the like. Furthermore, a physical embodiment of a transaction account may be distributed as a financial instrument.
A financial transaction instrument may be traditional plastic transaction cards, titanium-containing, or other metal-containing, transaction cards, clear and/or translucent transaction cards, foldable or otherwise unconventionally-sized transaction cards, radio-frequency enabled transaction cards, or other types of transaction cards, such as credit, charge, debit, pre-paid or stored-value cards, or any other like financial transaction instrument. A financial transaction instrument may also have electronic functionality provided by a network of electronic circuitry that is printed or otherwise incorporated onto or within the transaction instrument (and typically referred to as a “smart card”), or be a fob having a transponder and an RFID reader.
With regard to use of a transaction account, users may communicate with merchants in person (e.g., at the box office), telephonically, or electronically (e.g., from a user computer via the Internet). During the interaction, the merchant may offer goods and/or services to the user. The merchant may also offer the user the option of paying for the goods and/or services using any number of available transaction accounts. Furthermore, the transaction accounts may be used by the merchant as a form of identification of the user. The merchant may have a computing unit implemented in the form of a computer-server, although other implementations are possible.
In general, transaction accounts may be used for transactions between the user and merchant through any suitable communication means, such as, for example, a telephone network, intranet, the global, public Internet, a point of interaction device (e.g., a point of sale (POS) device, personal digital assistant (PDA), mobile telephone, kiosk, etc.), online communications, off-line communications, wireless communications, and/or the like. The transaction accounts may be associated with loyalty programs to encourage use of the transaction accounts by a transaction account holder.
Persons skilled in the relevant arts will understand the breadth of the terms used herein and that the exemplary descriptions provided are not intended to be limiting of the generally understood meanings attributed to the foregoing terms.
It is noted that references in the specification to “one embodiment”, “an embodiment”, “an example embodiment”, etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it would be within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
Customer behavior models are built in order to understand customer life cycle behavior on an individual customer level. Once the behavior models are built, they may be used to predict customer behavior for individual customers over a given period of time. Regarding consumer loyalty, behavior models can optimize marketing investment returns by determining whether a given marketing campaign will enhance loyalty engagement for the individual customer with a transaction account company, such as American Express Co. of New York, N.Y. The models can also be used to determine the best type of campaign to be used for the individual customer, as well as to determine the most profitable combination of offers within the campaign for the individual. Such offers may include, for example and without limitation, a campaign offer, a type of messaging, a channel, a duration of offer, and timing of the campaign.
A behavior model is a collection of one or more consumer attributes and correlated effects the attributes have on consumer behavior. Although the present description will be made with reference to modeling behavior regarding a loyalty or rewards program associated with a transaction card provider, one of skill in the art will recognize that consumer behavior can be modeled for various uses without departing from the spirit and scope of the present invention.
One of the specific types of models listed among the examples above is a redemption model. Such a model may suggest, for example, that a particular customer is likely to redeem points from a loyalty rewards program during a next six month period. Certain redemptions are more expensive (e.g., airline tickets) than others (e.g., retail merchandise). Thus, based on model redemption predictions, it may be advantageous to target such customers near the beginning of that six month period with a cross-redemption campaign encouraging the members to use their reward points to purchase less expensive rewards, such as retail merchandise.
Another of the specific types of models listed among the examples above is an attrition model. An attrition model may suggest that during a next six month period there is likely to be significant attrition of members (members leaving the rewards program). Such a model may be useful in strategy and planning to target loyalty program members likely to leave the loyalty program with a retention campaign. Such a campaign may provide an offer to a member likely to leave that would encourage such a member to stay by requiring the member to stay in the program to obtain some benefit. An attrition model may also do the reverse and predict how many new members are likely to join the loyalty program if offered membership.
Another of the specific types of models listed among the examples above is an overall spend model. Such a model may suggest a total amount of money that a customer is likely to spend that is subject to the loyalty program during a particular future time period without regard to how that amount will be apportioned to various types of services, industries, partners, etc.
Another of the specific types of models listed among the examples above is a spend persistency model. Such a model may predict how long a particular level of spending is likely to continue.
A partner spend model may suggest an amount of money a member will spend with a particular reward program partner. For example, the reward program may partner with a retail store, and target the member with offers to increase spend at the retail store.
An industry spend model relates to the amount to be spent in a particular type of endeavor, such as, for example, air, travel, lodging, and retail. For example, a model might predict that a customer may spend $3,000 in retail stores (not a particular partner store) during the next twelve months or spend $5000 in restaurants during the next six months, or $2000 in hotels during the next twelve months. The models can also predict the amount of spend in each such category.
For the model type selected, performance time periods are selected at step 304. In some cases, there are defined “pre-performance” and “performance” periods. In other cases, there are defined “pre-performance” and “post-performance” periods. “Pre-performance” refers to a time period before a customer participated in a campaign. “Performance” refers to a time period during which the customer participated in the campaign. “Post-performance” refers to a time period after the customer's participation in the campaign was complete.
For models in which performance and post-performance data exist, extracting data may also include gathering the associated performance and post-performance data.
At step 308 the defined model is developed using, for example, statistical regression analysis. Each model may include various consumer attributes and correlated effects the attributes have on consumer behavior. An example of behavior model development may be found in U.S. patent application Ser. No. 11/694,086, filed Mar. 30, 2007, which is incorporated by reference herein in its entirety. Models may be developed using an entire set or various subsets of members of the loyalty program. For example, the entire population of the loyalty program may be used to develop a set of baseline behavior models (as will be described below, baseline behavior models are behavior models not tied to a specific campaign which indicate how a customer will act if no campaign is targeted at that customer). However, for developing a campaign-specific model intended to predict customer response to a particular kind of marketing campaign, the eligible population might be a campaign-specific population of the loyalty program. For example, the model population may be consumers who have previously been targeted with the same or similar campaign.
After a particular model is developed at step 308, its performance is tested at step 310. Step 310 may include sub-steps of a) checking the accuracy of model performance (e.g., comparing how close a predicted value is versus an actual value of the dependent variable), and b) checking the discrimination power of the model (e.g., how much the volume of the actual “post spend” is captured by the top 30% of high predicted “post spenders”).
If the model developed at step 308 performs as desired at step 310, the model is tested for validity at step 312. Step 312 may include sub-steps as follows: a) applying the model results to a new post performance time period to validate whether the model works across time periods, and b) if the model performance is not satisfied, further developing the model at step 308.
If the model is valid, as tested on actual historical data, the model is coded at step 314. Model code is based on the finalized model equations, and may also be referred to herein as an algorithm. The algorithm may include, for example, weighted combinations of attributes resulting in a net profit expected from an individual consumer.
In the past, a single marketing campaign would be used to cover a wide variety of consumers. However, it is more profitable to a company trying to increase consumer spending behavior (hereinafter, “the provider”) for the campaigns to be customized such that a campaign sent to an individual consumer is optimized for that consumer. Once the behavior models have been developed, they may be used to develop various marketing campaigns and evoke particular responses from consumers.
As used herein, an “offer” is a feature of a campaign which can be changed depending on a customer's predicted response to that feature. An offer may also be referred to as a variable. Offers may include, for example and without limitation, a campaign fee offer, a duration offer, a response channel offer, a threshold offer, and a cap offer. A campaign fee offer refers to the charge to the consumer for accepting the campaign (e.g., a 4.5% APR on all purchases). A duration offer refers to the length of the campaign (e.g., 3 months, 6 months, etc.). A response channel offer refers to the manner in which the consumer should respond to accept the offer (e.g., email, telephone, etc.). A threshold offer refers to a spend amount involved with the offer (e.g., a minimum amount of spend needed to participate in the campaign). A cap offer refers to a spend level over which the campaign is no longer applicable.
In the example of
For a given consumer, a set of loyalty behavior models are developed based on, for example, historical data about the consumer. A set, as used herein, may include one or more models. Each campaign 502 and 504 includes various combinations of offers that may be used in that campaign. For example, for campaign 502, a first combination of offers 506 may include the following offers: a fee of $10, a duration of 3 months, and a cap of $3,000. A second combination of offers 508 may include the following offers: a fee of $0, a duration of 6 months, and a cap of $1,500. Campaign 502 having combination of offers 506 is referred to herein as campaign 502a; campaign 502 having combination of offers 508 is referred to herein as campaign 502b.
For campaign 504, a first combination of offers 510 may include the following offers: an incentive of double points and a fee of $20. A second combination of offers 512 may include the following offers: an incentive of increased point value and a fee of $10. Campaign 504 having combination of offers 510 is referred to herein as campaign 504a; campaign 504 having combination of offers 512 is referred to herein as campaign 504b.
In step 513, each combination of offers is processed by the set of loyalty behavior models 514. In step 515, for each combination of offers, a net profit score is output. A net profit score correlates to a value of a net profit estimated to be received by a provider from the given consumer. The net profit score may be approximately equal to the net profit value, or the net profit score may be some function of the net profit value. In the example of
In step 523, for each campaign, the combination of offers having the highest net profit score is selected. In the example of
Once a highest net profit score has been determined for each campaign, the campaign having the highest net profit score is selected in step 527. For purposes of this example, net profit score 524 for campaign 502 is lower than net profit score 526 for campaign 504. Campaign 504 is thus selected as the campaign to be used for targeting the given consumer. In step 529, the individual consumer is targeted with the combination of offers in the selected campaign that is expected to provide the highest net profit to the provider. In
In step 604, each customer in the plurality of customers is provided with both a baseline net profit score and a campaign net profit score. The baseline net profit score may be determined by inserting at least one attribute corresponding to the customer into the set of baseline behavior models. The campaign net profit score may be determined by inserting at least one combination of offers corresponding to the customer into the set of campaign behavior models.
In step 606, it is determined for each customer which of the baseline net profit score and the campaign net profit score has a higher value for that customer. For purposes of this example, in
In step 608, one or more of the customers whose campaign scores are higher than their baseline scores are selected for targeting. In the example of
To reduce the cost to the provider, only the most profitable customers may be targeted with the campaign. For example, if a particular provider budget is allocated to the campaign such that the campaign can only target 30% of consumers, the campaign may rank the consumers selected in step 608, and target the top 30% of the ranked consumers.
The present invention or any part(s) or function(s) thereof may be implemented using hardware, software or a combination thereof and may be implemented in one or more computer systems or other processing systems. However, the manipulations performed by the present invention were often referred to in terms, such as adding or comparing, which are commonly associated with mental operations performed by a human operator. No such capability of a human operator is necessary, or desirable in most cases, in any of the operations described herein which form part of the present invention. Rather, the operations are machine operations. Useful machines for performing the operation of the present invention include general purpose digital computers or similar devices.
In fact, in one embodiment, the invention is directed toward one or more computer systems capable of carrying out the functionality described herein. An example of a computer system 200 is shown in
The computer system 200 includes one or more processors, such as processor 204. The processor 204 is connected to a communication infrastructure 206 (e.g., a communications bus, cross-over bar, or network). Various software embodiments are described in terms of this exemplary computer system. After reading this description, it will become apparent to a person skilled in the relevant art(s) how to implement the invention using other computer systems and/or architectures.
Computer system 200 can include a display interface 202 that forwards graphics, text, and other data from the communication infrastructure 206 (or from a frame buffer not shown) for display on the display unit 230.
Computer system 200 also includes a main memory 208, preferably random access memory (RAM), and may also include a secondary memory 210. The secondary memory 210 may include, for example, a hard disk drive 212 and/or a removable storage drive 214, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, etc. The removable storage drive 214 reads from and/or writes to a removable storage unit 218 in a well known manner. Removable storage unit 218 represents a floppy disk, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive 214. As will be appreciated, the removable storage unit 218 includes a computer usable storage medium having stored therein computer software and/or data.
In alternative embodiments, secondary memory 210 may include other similar devices for allowing computer programs or other instructions to be loaded into computer system 200. Such devices may include, for example, a removable storage unit 222 and an interface 220. Examples of such may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an erasable programmable read only memory (EPROM), or programmable read only memory (PROM)) and associated socket, and other removable storage units 222 and interfaces 220, which allow software and data to be transferred from the removable storage unit 222 to computer system 200.
Computer system 200 may also include a communications interface 224. Communications interface 224 allows software and data to be transferred between computer system 200 and external devices. Examples of communications interface 224 may include a modem, a network interface (such as an Ethernet card), a communications port, a Personal Computer Memory Card International Association (PCMCIA) slot and card, etc. Software and data transferred via communications interface 224 are in the form of signals 228 which may be electronic, electromagnetic, optical or other signals capable of being received by communications interface 224. These signals 228 are provided to communications interface 224 via a communications path (e.g., channel) 226. This channel 226 carries signals 228 and may be implemented using wire or cable, fiber optics, a telephone line, a cellular link, a radio frequency (RF) link and other communications channels.
In this document, the terms “computer program medium” and “computer usable medium” are used to generally refer to media such as removable storage drive 214 and a hard disk installed in hard disk drive 212. These computer program products provide software to computer system 200. The invention is directed to such computer program products.
Computer programs (also referred to as computer control logic) are stored in main memory 208 and/or secondary memory 210. Computer programs may also be received via communications interface 224. Such computer programs, when executed, enable the computer system 200 to perform the features of the present invention, as discussed herein. In particular, the computer programs, when executed, enable the processor 204 to perform the features of the present invention. Accordingly, such computer programs represent controllers of the computer system 200.
In an embodiment where the invention is implemented using software, the software may be stored in a computer program product and loaded into computer system 200 using removable storage drive 214, hard drive 212 or communications interface 224. The control logic (software), when executed by the processor 204, causes the processor 204 to perform the functions of the invention as described herein.
In another embodiment, the invention is implemented primarily in hardware using, for example, hardware components such as application specific integrated circuits (ASICs). Implementation of the hardware state machine so as to perform the functions described herein will be apparent to persons skilled in the relevant art(s).
In yet another embodiment, the invention is implemented using a combination of both hardware and software.
While various embodiments of the present invention have been described above, it should be understood that they have been presented by way of example, and not limitation. It will be apparent to persons skilled in the relevant art(s) that various changes in form and detail can be made therein without departing from the spirit and scope of the present invention. Thus, the present invention should not be limited by any of the above described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.
In addition, it should be understood that the figures and screen shots illustrated in the attachments, which highlight the functionality and advantages of the present invention, are presented for example purposes only. The architecture of the present invention is sufficiently flexible and configurable, such that it may be utilized (and navigated) in ways other than that shown in the accompanying figures.
Further, the purpose of the foregoing Abstract is to enable the U.S. Patent and Trademark Office and the public generally, and especially the scientists, engineers and practitioners in the art who are not familiar with patent or legal terms or phraseology, to determine quickly from a cursory inspection the nature and essence of the technical disclosure of the application. The Abstract is not intended to be limiting as to the scope of the present invention in any way.
|INTEGRATED LOYALTY PROGRAM AND GAME MECHANIC
|MULTIPLE CRM LOYALTY INTERFACE FRAMEWORK
|ANONYMOUS PLAYER TRACKING WITH MOBILE DEVICES
|Kids' Loyalty and Rewards Program
|PARTNER PROGRAM PARTICIPATION SYSTEM AND METHOD
|COUPONLESS REWARDS IN RESPONSE TO A TRANSACTION
|LOCATION-BASED LOYALTY PROGRAM