US20130024274A1

US20130024274A1 - Method and system for measuring advertising effectiveness using microsegments

Info

Publication number: US20130024274A1
Application number: US13/438,346
Authority: US
Inventors: Curtis VILLARS
Original assignee: Mastercard International Inc
Current assignee: Mastercard International Inc
Priority date: 2011-07-19
Filing date: 2012-04-03
Publication date: 2013-01-24
Also published as: WO2013012863A2; US20130024242A1; US20170061450A1; US10339545B2; WO2013012863A3; US9524504B2; WO2013012861A2; WO2013012861A3

Abstract

A method for analyzing advertising effectiveness includes storing entity information including activity and characteristic information associated with a plurality of entities; generating a plurality of microsegments, each microsegment including a subset of the plurality of entities based on the associated characteristic information; generating a test audience including a plurality of first microsegments including entities exposed to an advertisement for a period of time and a control audience including a plurality of second microsegments including entities not deliberately exposed to the advertisement; analyzing the activity information for the test audience and the control audience to determine spending behaviors for the associated entities during the period of time; comparing the spending behaviors determined for the test and control audiences to determine the effectiveness of the advertisement; and reporting the effectiveness of the advertisement.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the priority benefit of commonly assigned U.S. Provisional Application 61/509,386, “Protecting Privacy in Audience Targeting,” by Curtis Villars, filed Jul. 19, 2011. The subject matter of the foregoing is herein incorporated by reference in its entirety.

FIELD

The present disclosure relates to methods for measuring the effectiveness of advertisements, specifically measuring advertisement effectiveness by using microsegments as applied to exposed and unexposed consumers.

BACKGROUND

In the ever expanding information age, merchants and advertisers have a desire to develop more effective and efficient advertising. Traditionally, methods and systems for measuring effectiveness of advertising have lacked in detail and efficiency. Analysis of overall revenue and consumer activity for a particular merchant may indicate that an advertisement campaign is effective, but the merchant is unable to deduce if the increased activity is from consumers exposed to the advertisement. In addition, this type of high level analysis is unable to provide specific information regarding advertising effectiveness, such as its effectiveness on particular demographic groups and the strength of the response, information which could be beneficial to not only the merchant, but to the end consumer as well.
Some traditional methods for detailed measuring advertising effectiveness include surveying and polling consumers. This type of analysis has several shortcomings. Surveys and polls require consumers to volunteer information, which may be inaccurate or fabricated, especially if the survey or poll is anonymous. The results of the analysis may be full of uncertainty at whether or not each consumer was in fact exposed to the advertisement, and whether or not the consumer's spending behavior was affected. In addition, surveys or polls take time and require consumer participation, which may result in a small and/or non-representative sample of all consumers. Furthermore, increased consumer concerns for privacy and security of personal information may result in even less participation and/or more unreliable information.
Thus, there is a perceived opportunity to provide a technical solution for improving measurement of advertising effectiveness by analyzing actual financial transaction information for exposed and unexposed consumers, while still maintaining the privacy and security of consumer information.

SUMMARY

The present disclosure provides for a system and method for analyzing advertising effectiveness.
A method for analyzing advertising effectiveness includes storing, by a database in a processing system, entity information associated with a plurality of entities, the entity information including activity information and characteristic information associated with the corresponding entity; generating a plurality of microsegments, each microsegment including a subset of the plurality of entities based on the associated characteristic information, wherein no two subsets of the plurality of entities contains a common entity; and generating a test audience including a plurality of first microsegments and a control audience including a plurality of second microsegments, wherein each entity in the plurality of first microsegments is exposed to an advertisement associated with a merchant during a predetermined period of time and wherein each entity in the plurality of second microsegments is not exposed to the advertisement during the predetermined period of time. The method also includes analyzing, by a processor in the processing system, the activity information for the entities in the plurality of first microsegments and the entities in the plurality of second microsegments to determine spending behaviors for the associated entity during the predetermined period of time. The method further includes comparing the spending behaviors determined for the entities in the plurality of first microsegments with the spending behaviors determined for the entities in the plurality of second microsegments to determine the effectiveness of the advertisement and reporting, by a communication component in the processing system, the effectiveness of the advertisement.
A system for analyzing advertising effectiveness includes a database component configured to store entity information associated with a plurality of entities, the entity information including activity information and characteristic information, a processor, and a communication component. The processor is configured to: generate a plurality of microsegments, each microsegment including a subset of the plurality of entities based on the associated characteristic information, wherein no two subsets of the plurality of entities contains a common entity; generate a test audience including a plurality of first microsegments and a control audience including a plurality of second microsegments, wherein each entity in the plurality of first microsegments is exposed to an advertisement associated with a merchant during a predetermined period of time and wherein each entity in the plurality of second microsegments is not exposed to the advertisement during the predetermined period of time; analyze the activity information for the entities in the plurality of first microsegments and the entities in the plurality of second microsegments to determine spending behaviors for the associated entity during the predetermined period of time; and compare the spending behaviors determined for the entities in the plurality of first microsegments with the spending behaviors determined for the entities in the plurality of second microsegments to determine the effectiveness of the advertisement. The communication component is configured to report the effectiveness of the advertisement

BRIEF DESCRIPTION OF THE DRAWING FIGURES

Exemplary embodiments are best understood from the following detailed description when read in conjunction with the accompanying drawings. Included in the drawings are the following figures:

FIG. 1 is a block diagram illustrating a high-level view of system architecture of a financial transaction processing system in accordance with exemplary embodiments.

FIG. 2 is a flow chart illustrating a method for generating microsegments without the use of personally identifiable information in accordance with exemplary embodiments.

FIG. 3 is a data set illustrating useable consumer data without including personally identifiable information in accordance with exemplary embodiments.

FIGS. 4A and 4B are data sets illustrating microsegments created from the data set of FIG. 3 in accordance with exemplary embodiments.

FIG. 5 is a block diagram illustrating a data set for use with the disclosed methods in accordance with exemplary embodiments.

FIG. 6 is a block diagram illustrating a system for analyzing the effectiveness of advertisements in accordance with exemplary embodiments.

FIGS. 7 and 8 are flowcharts illustrating methods for measuring advertisement effectiveness in accordance with exemplary embodiments.

FIG. 9 is a flowchart illustrating an exemplary method for analyzing the effectiveness of an advertisement in accordance with exemplary embodiments.

Further areas of applicability of the present disclosure will become apparent from the detailed description provided hereinafter. It should be understood that the detailed description of exemplary embodiments are intended for illustration purposes only and are, therefore, not intended to necessarily limit the scope of the disclosure.

DETAILED DESCRIPTION

Financial Transaction Processing System

FIG. 1 illustrates a financial transaction processing system 100 including a customer (e.g., a consumer) 102, a merchant 104, an issuer 106, a financial transaction processing agency 108, and a demographic tracking agency 110.
The customer 102 may use a payment card at the merchant 104 for payment of a financial transaction. The payment card may be any type of transaction card used for making payments in a financial transaction, such as a debit card, credit card, charge card, ATM card, etc. Each payment card may be assigned a unique identifier (e.g., an account number) that links the payment card to a cardholder (e.g., the customer 102).
The merchant 104 may forward the payment card information (e.g., the account number) as well as transaction information (e.g., the amount, merchant information, time and date information, etc.) to the financial transaction processing agency 108 for processing. The financial transaction processing agency 108 may be any service provider for merchants, acquirers, issuers, consumers, etc. for the processing of transactions involving payment cards, such as MasterCard or VISA. The financial transaction processing agency 108 may issue an authorization request from the issuer 106. The issuer 106 may be an entity (e.g., a bank or the merchant 104) that issued the payment card used in the transaction, a stand-in processor configured to act on behalf of the issuer of the payment card, a credit bureau that has card or consumer related information, or any other suitable entity.
The issuer 106 may approve or deny the transaction. If the issuer 106 approves the transaction, the issuer 106 notifies the financial transaction processing agency 108 of the approval. The financial transaction processing agency 108 may then notify the merchant 104 of the approval of the transaction, who may then finalize the transaction with the customer 102. The issuer 106 may then bill the customer 102 for payment of the transaction and report any payments, or lack thereof, to the demographic tracking agency 110 (e.g., a credit report agency, a marketing and research firm such as Nielsen, etc.). The demographic tracking agency 110, therefore, may possess personally identifiable information (PII) of the customer 102, which may be stored in the external database 114, though the financial transaction processing agency 108 would not be in possession of the PII or have access to it.

Personally Identifiable Information

Personally identifiable information (PII) may be information that may be used, alone or in conjunction with other sources, to uniquely identify a single individual (e.g., the customer 102). As such, there is a benefit to prevent the use and dissemination of PII in an effort to protect consumer privacy and to prevent against crimes, such as identity theft. The present disclosure provides for methods where the financial transaction processing agency 108 (e.g., MasterCard) does not possess any data containing personally identifiable information in processes that help accurately identify groups of individuals or businesses having particular interests or desires across a broad and diverse population of cardholders.
This is done, viewed at a high level, by enriched data associated with individuals or businesses (entities), to include transaction history and demographics, but not PII, as associated by a unique identifier, and placing like entities, filtered by some criteria, into small groups. Therefore, third parties that have contact information for entities can group them and match them to the enriched data groups. Whether or not the groups from the combined/enriched data sets and from the data sets have parity, common members, or no overlap, statistically the matched groups have predictable behavior, particularly in small groups or microsegments (as defined below). Having grouped the third party's data set members into small groups based on selected activities and/or characteristics (e.g., demographic and geographic information), the third party can effectively direct communications of interest to these small groups or microsegments. The third party may possess contact information, which may include PII, such as e-mail addresses, phone numbers, etc. In an exemplary embodiment, the contact information that may include PII may be removed from the third party data set or made otherwise unavailable to the financial transaction processing agency 108.
In some embodiments, bucketing may be used in order to render potentially identifiable information anonymous. such as by aggregating information that may otherwise be personally identifiable (e.g., age, income, etc.) into a bucket (e.g., grouping) in order to render the information not personally identifiable. For example, a consumer of age 26 with an income of $65,000, which may otherwise be unique in a particular circumstance to that consumer, may be represented by an age bucket for ages 21-30 and an income bucket for incomes $50,000 to $74,999, which may represent a large portion of additional consumers and thus no longer be personally identifiable to that consumer. In other embodiments, encryption may be used. For example, personally identifiable information (e.g., an account number) may be encrypted (e.g., using a one-way encryption) such that the financial transaction processing agency 108 may not possess the PII or be able to decrypt the encrypted PII.
Information that may be considered personally identifiable may be defined by a third party, such as a governmental agency (e.g., the U.S. Federal Trade Commission, the European Commission, etc.), a non-governmental organization (e.g., the Electronic Frontier Foundation), industry custom, consumers (e.g., through consumer surveys, contracts, etc.), codified laws, regulations, or statutes, etc.

Protection of PII in a Financial Transaction Processing System

As illustrated in FIG. 1, the financial transaction processing agency 108 may include a database without PII 112 and an enriched database 116, which also does not include PII. The demographic tracking agency 110 may include the external database 114, which may include PII not accessible by the financial transaction processing agency 108.
The database without PII 112 may store information on a plurality of consumers (e.g., the customer 102) that is not personally identifiable. For example, the financial transaction processing agency 108 may store information relating to financial transactions processed by the agency as it performs in the system 100, such as transaction amount, transaction time, transaction location, merchant identification, etc. and do so without the use of any PII relating to the customer 102 participating in the transactions. In some embodiments, the database without PII 112 may store an encrypted unique identifier associated with a consumer, which may be encrypted using a one-way encryption, such that the financial transaction processing agency 108 may be unable to identify the associated consumer. Methods of encryption suitable for performing the functions as disclosed herein will be apparent to persons having skill in the relevant art.
The financial transaction processing agency 108 may communicate with the demographic tracking agency 110 (e.g., via a network such as the network 906, discussed below). The financial transaction processing agency 108 may obtain non-personally identifiable information included the external database 114. Non-personally identifiable information included in the external database 114 may include geographical data, demographic data, financial data, or any other suitable data as will be apparent to persons having skill in the relevant art, hereinafter referred to generally as demographic data. In one embodiment, the information included in the external database 114 may be bucketed and thus not personally identifiable. The financial transaction processing agency 108 may combine the non-personally identifiable information provided by the demographic tracking agency 110 with information included in the database without PII 112 into a single data set. The combined data set may be stored in the enriched database 116. In some embodiments, the financial transaction processing agency 108 may aggregate (e.g., bucket, group, etc.) data in each of the external database 114 and the database without PII 112 prior to combining the information into a single data set. In a further embodiment, the financial transaction processing agency 108 may aggregate data to a level of ten prior to combining the information into a single data set.
Each of the databases 112, 114, and 116 may be any type of database suitable for the storage of data as disclosed herein. Each database may store data in a single database, or may store data across multiple databases and accessed through a network. Network configurations as disclosed herein may include a local area network (LAN), a wide area network (WAN), a wireless network (e.g., WiFi), a mobile communication network, a satellite network, the Internet, fiber optic, coaxial cable, infrared, radio frequency (RF) or any other suitable configuration as would be apparent to persons having skill in the relevant art.
Data may be stored on any type of suitable computer readable media, such as optical storage (e.g., a compact disc, digital versatile disc, blu-ray disc, etc.) or magnetic tape storage (e.g., a hard disk drive). The database may be configured in any type of suitable database configuration, such as a relational database, a structured query language (SQL) database, a distributed database, an object database, etc. Suitable configurations and database storage types will be apparent to persons having skill in the relevant art.
The database without PII 112 and the enriched database 116 may be included as part of the financial transaction processing agency 108 internally, or externally and accessed through a network. The external database 114 may be included as part of the demographic tracking agency 110 internally, or externally and accessed through a network. Each database may be a single database, or may comprise multiple databases which may be interfaced together (e.g., physically or via a network, such as the network 906). In some embodiments, the database without PII 112 and the enriched database 116 may be a single database.
The financial transaction processing agency 108 may include a processor 102, which may be any type of processing device capable of performing the functions as disclosed herein, such as a general purpose computer, a general purpose computer configured as disclosed herein to become a specific purpose computer, etc. The processing device may be a single system (e.g., a single specific purpose computer) or may be comprised of several interconnected (e.g., physically or through a network) systems or servers (e.g., a server farm). The processor 102 may be coupled to each of the databases 112, 114, and 116 either physically (e.g., through a cable such as a coaxial cable, fiber-optic cable, etc.) or through a network (e.g., the network 906).
The processor 102 may be configured to receive information from both the database without PII 112 and to receive information with the PII removed from the external database 114, and to combine the data to form a combined data set without PII. In some embodiments, the processor 102 may aggregate the information received from at least one of the two databases prior to combining the information into the combined data set. The processor 102 may also be configured to store the combined data set (e.g., that does not include PII) in the enriched database 116. The processor 102 may be further configured to review the combined data set or to select microsegments or audiences based on the combined data set, as discussed in more detail below. In some embodiments, the processor 102 may be configured to review selected microsegments and/or audiences and generate reports therein.

Creation of Microsegments

FIG. 2 illustrates a method for generating microsegments without the use of personally identifiable information. The method is disclosed with reference to the processor 102, the database without PII 112 and enriched database 116 as part of the financial transaction processing agency 108, and the external database 114 of the demographic tracking agency 110.
Information that is stored in the database without PII 112 may be retrieved (e.g., by the processor 102) in step 202. In one embodiment, all of the information stored in the database without PII 112 may be retrieved. In another embodiment, only a single entry in the database without PII 112 may be retrieved. The retrieval of information may be performed a single time, or may be performed multiple times. In an exemplary embodiment, only information pertaining to a specific microsegment (discussed further below) may be retrieved from the database without PII 112.
In step 204, the retrieved information may be associated with an entity (e.g., a cardholder, a business, a microsegment, any group or combination thereof, etc.) by the processor 102. In one embodiment, each entity may be represented by a unique identifier, such as a unique identification number (e.g., an account number). In one embodiment, entity information may be encrypted.
The processor 102 may retrieve, in step 206, information (e.g., that does not include any personally identifiable information) from the external database 114. The retrieval performed in step 206 may be of the same type and retrieve the corresponding information (e.g., relating to the same microsegment) as the information retrieved from the database without PII 112 in step 202. In one embodiment, if the external database 114 includes PII, the financial transaction processing agency 108 may be prohibited from accessing the PII. The information retrieved in this step may, in step 208, then be associated with an entity (e.g., the same entity from step 202). In step 210, a record may be created in the enriched database 116. The enriched database 116 may store the information obtained and associated in the prior steps, the information not containing any PII. As a result, the financial transaction processing agency 108 may not be in contact with or have access to any PII during the process.
Microsegments (as defined below) may be selected, in step 212, based on the information that was obtained and stored in the enriched database 116. The selection of information for representation in the microsegment or microsegments may be different in every instance. In one embodiment, all of the information stored in the enriched database 116 may be used for selecting microsegments. In an alternative embodiment, only a portion of the information may be used. The selection of microsegments may be based on specific criteria (e.g., from a research firm or advertising agency such as the advertiser 118 illustrated in FIG. 6).
In step 214, information may be reported by the processor 102. Reporting may include the review and/or reporting of the selected microsegments, of the information stored in the enriched database 116, or a combination thereof. Reviewing may include a review of financial account information of the entities in the microsegments, performing statistical analysis on financial account information, finding correlations between account information and consumer behaviors, predicting future consumer behaviors based on account information, relating information on a financial account with other financial accounts, or any other method of review suitable for the particular application of the data, which will be apparent to persons having skill in the relevant art. In an exemplary embodiment, statistical analysis may be performed on the financial data for specific microsegments stored in the enriched database 116 in order to determine the effectiveness of an advertisement without the use of any PII, as illustrated in methods discussed below.
The report may be transmitted to a third party (e.g., the advertiser 118) or the financial transaction processing agency 108, may be displayed (e.g., on a display device), or may be reported in any other manner suitable for reporting. The reporting may include a report on a review of the selected microsegments or information, or any other suitable information, such as an analysis of the review (e.g., and performed by the financial transaction processing agency 108). Reporting may be performed visually, aurally, tactically, or in any other suitable method as will be apparent to persons having skill in the relevant art.

Microsegment Definition and Creation

A microsegment is a representation of a group of consumers that is granular enough to be valuable to advertisers, marketers, etc., but still maintain a high level of consumer privacy without the use or obtaining of any personally identifiable information.
In step 214, information may be reported by the processor 102. Reporting may include the review and/or reporting of the selected microsegments, of the information stored in the enriched database 116, or a combination thereof. Reviewing may include a review of financial account information of the entities in the microsegments, performing statistical analysis on financial account information, finding correlations between account information and consumer behaviors, predicting future consumer behaviors based on account information, relating information on a financial account with other financial accounts, or any other method of review suitable for the particular application of the data, which will be apparent to persons having skill in the relevant art. In an exemplary embodiment, statistical analysis may be performed on the financial data for specific microsegments stored in the enriched database 116 in order to determine the effectiveness of an advertisement without the use of any PII, as illustrated in methods discussed below.
The report may be transmitted to a third party (e.g., the advertiser 118) or the financial transaction processing agency 108, may be displayed (e.g., on a display device), or may be reported in any other manner suitable for reporting. The reporting may include a report on a review of the selected microsegments or information, or any other suitable information, such as an analysis of the review (e.g., and performed by the financial transaction processing agency 108). Reporting may be performed visually, aurally, tactically, or in any other suitable method as will be apparent to persons having skill in the relevant art.

Microsegment Definition and Creation

A microsegment is a representation of a group of consumers that is granular enough to be valuable to advertisers, marketers, etc., but still maintain a high level of consumer privacy without the use or obtaining of any personally identifiable information.
Microsegments may be given a minimum or a maximum size. A minimum size of a microsegment would be at a minimum large enough so that nowould not result in entity could be personally identifiable, but small enough to provide the granularity needed in a particular circumstance. In some instances, the size of a microsegment may be dependent on the application. An audience based on a plurality of microsegments, for instance, might have ten thousand entities, but the microsegments would be aggregated when forming the audience and would not be discernable to anyone having access to an audience. As noted elsewhere, the entities in a microsegment that is used to form an audience might not be members of a resulting audience at all. In one embodiment, a microsegment may include at least ten unique entities. Microsegments may be defined based on geographical or demographical information, such as age, gender, income, marital status, postal code, income, spending propensity, familial status, etc. Categories may be bucketed to avoid the use of PII (e.g., representing age by a range of ages). In some embodiments, microsegments may be defined by a plurality of geographical and/or demographical categories. For example, a microsegment may be defined for any cardholder with an income between $50,000 and $74,999, that is between the ages of 20 and 29, and is single.
In this way, microsegments may be defined in such a way as to avoid the use of PII. For example, if a preliminary microsegment is defined for entities with an income between $100,000 and $149,999 in a particular postal code, and the preliminary microsegment contains less than a minimum number (e.g., as provided by the advertiser, governmental regulations, etc.) ofentitiesone entity, the preliminary microsegment may be combined with another microsegment (e.g., one corresponding to a neighboring postal code) as to further protect the personal identity of the entities in the preliminary microsegment. In this way, microsegments will be defined in a way so that no entity in any microsegment is personally identifiable.
Microsegments may also be based on behavioral variables. For example, the database without PII 112 may store information relating to financial transactions. The information may be used to determine an individual's likeliness to spend. An individual's likeliness to spend may be represented generally, or with respect to a particular industry (e.g., electronics), retailer (e.g., Macy's®), brand (e.g., Apple®), or any other criteria which may be suitable as will be apparent to persons having skill in the relevant art. An individual's behavior may also be based on additional factors such as time, location, season, etc. For example, a microsegment may be based on consumers who are likely to spend on electronics during the holiday season, or on consumers whose primary expenses are in a suburb, but are likely to spend on restaurants located in a major city. The factors and behaviors identified and used to define microsegments may vary widely and may be based on the application of the information.
Behavioral variables may also be applied to generated microsegments based on the attributes of the entities in the microsegment. For example, a microsegment of specific geographical and demographical attributes (e.g., single males in a particular postal code between the ages of 26-30 with an income between $100,000 and $149,999) may be analyzed for spending behaviors. Results of the analysis may be assigned to the microsegment. For example, the above microsegment may be analyzed and reveal that the entities in the microsegment have a high spending propensity for electronics and may be less likely to spend money during the month of February.
FIG. 3 illustrates consumer information data that may be used in the creation of a microsegment. The data represented in the six leftmost columns may be information that is stored in the external database 114 at the demographic tracking agency 110, with any included PII removed or made otherwise inaccessible to the financial transaction processing agency 108 or the processor 102, in order to protect consumer privacy. The data represented in the six rightmost columns may be information that is stored in the financial transaction processing agency 110 database without PII 112. In the illustrated embodiment, there is a unique identifier for each consumer that has been encrypted in order to protect the anonymity of the consumer.
The data from the external database 114 and the data from the database without PII 112 may be combined into a single set of data that does not contain PII, which may be stored in the enriched database 116. Information may be combined by use of the unique encrypted identifier for each entity. In one embodiment, if only one set of data contains a particular identifier, then that data may be left out of the enriched data set. In some embodiments, only some of the columns of data may be included in the enriched data set. For example, the marital status column may not be included (e.g., because the advertiser does not distinguish consumers based on marital status).
The enriched data set may be stored in the enriched database 116. The enriched data may be separated into a plurality of microsegments, with each microsegment being defined by at least one geographical or demographical limitation. FIG. 4A illustrates the data set of individuals in a microsegment MS1, one of a plurality of microsegments illustrated in FIG. 4B. Microsegment MS1 includes seven individuals, each with a unique encrypted identifier. As illustrated in FIG. 4B, microsegment MS1 is defined by individuals in age group C, income group B, with marital status B, and living in postal code 12345. Groupings (e.g., age group C) are defined in bucketed groups in such a manner as to not divulge any personally identifiable information. In this way, consumers of an ideal age may be placed into a microsegment (e.g., for advertising) without the financial transaction processing agency 108 knowing the actual age of the consumer or even a range of ages, and therefore protecting the privacy of the consumer. The corresponding values for the grouping (e.g., ages 25 to 34 corresponding to age group C), may not be available to the financial transaction processing agency 108.
As illustrated in FIG. 4B, preliminary microsegment MS4 only contains a single individual. As a result, preliminary microsegment MS4 may be combined with another microsegment in order to protect the privacy of that individual. For example, preliminary microsegment MS4 may be combined with microsegment MS1, because preliminary microsegment MS4 is defined by the same age, income, and marital groups, and the defined postal code is a neighboring postal code. It will be apparent to persons having skill in the relevant art that microsegments may be grouped or combined in any manner that may be suitable for the particular application. For example, a retailer may want to advertise to everyone in a particular postal code without regard for age or income, and therefore may desire to combine microsegment MS1 and microsegment MS3, whereas another retailer may want to advertise to a specific age group without regarding for other factors, and therefore would want to combine microsegments MS1, MS2, and MS4.

Exemplary Dataset of Microsegments

FIG. 5 illustrates an exemplary dataset 502 for the storing, reviewing, and/or reporting of a plurality of microsegments. In one embodiment, the dataset 502 may be reported in the reporting step 214 of FIG. 2.
The dataset 502 may contain a plurality of entries (e.g., entries 504 a, 504 b, and 504 c). Each entry of the plurality of entries may include a secure identifier 506, demographic information 508, and financial information 510. The secure identifier 506 may include any type of identifier that may be unique to the particular entry (e.g., entry 504 a). The secure identifier may be encrypted. Suitable encryption methods may include public key encryption, RSA encryption, XOR encryption, SHA-2 encryption, symmetric key encryption, etc. In an exemplary embodiment, the secure identifier may be encrypted using a one-way encryption process. The secure identifier may be encrypted in such a way as to make any P11 unavailable to the financial transaction processing agency 108.
The demographic information 508 may include any demographic, geographic, or other suitable information relevant to the particular application. For example, if a family restaurant is launching an advertising campaign and is requesting microsegments of families with a spend propensity on restaurants, then the demographic information may include familial status, but not age. If a bar is launching an advertising campaign, then demographic information may include age, but not familial status. In some embodiments, the demographic information 808 may be replaced by geographic or other information. Suitable types of information relevant for the selecting and supplying of microsegments will be apparent to persons having skill in the relevant art. Likewise, the financial information 510 may include any financial information relevant to the particular application. For example, a dataset provided to advertisers in the food service industry may contain entries with financial information that includes a spend propensity for restaurants, but not a spend propensity for electronics.

System for Measuring Advertising Effectiveness

FIG. 6 illustrates a system 600 for measuring the effectiveness of an advertisement. The system 600 may include the financial transaction processing agency 108, the merchant 104, an advertiser 118, a test audience 120, and a control audience 122.
The merchant 104 may communicate with the advertiser 118 to request advertising, such as for a product or service offered by the merchant. In some embodiments, the merchant 104 may be the advertiser 118, or the advertiser may be a third party. The advertiser 118 may distribute, publisher, or otherwise make available an advertisement to consumers on behalf of the merchant 104 through print media, online, e-mail, text (e.g., SMS messaging) or nearly any other type or method of conveyance of advertising material. In an exemplary embodiment, not all consumers may be exposed to the advertisement. For example, as illustrated in FIG. 6, only the consumers 102 a in the test audience 120 may be exposed to the advertisement, while the consumers 102 b in the control audience 122 would be deliberately exposed to the advertisement (though of course incidental exposure by a few might be expected.
The test audience 120 may be comprised of consumers 102 a that are deliberately exposed to the advertisement for the merchant 104. In one embodiment, the advertiser 118 may identify the consumers that are exposed to the advertisement. In an alternative embodiment, a third party may identify the consumers exposed to the advertisement. In another alternative embodiment, the financial transaction processing agency 108 may identify the consumers exposed to the advertisement (e.g., based on financial transaction data stored in the enriched database 116). The control audience 122 may be comprised of consumers 102 b that are not deliberatively exposed to the advertisement for the merchant 104. It will be apparent to persons having skill in the relevant art that the control audience 122 may be optional, and in fact may be the same audience but in a temporal sense are both the control and the test audience (e.g., advertising effectiveness may be measured based on behavior prior to and subsequent to exposure to the advertisement without the need for a distinct control group).
As discussed in more detail below, the test audience 120 and the control audience 122 may be generated by the financial transaction processing agency 108. The audience may comprise a plurality of microsegments as applied to an external data set (e.g., provided by the advertiser 118). For example, the advertiser 118 may provide characteristic data (e.g., geographical and demographical data) for a plurality of entities (e.g., consumers). In one embodiment, the financial transaction processing agency 108 may generate microsegments based on the plurality of entities. In another embodiment, the financial transaction processing agency 108 may apply the plurality of entities to previously generated microsegments (e.g., based on the characteristic data in the enriched database 116 and the received characteristic data). The test audience 120 may be comprised of entities that have been exposed to the advertisement, or may be comprised of the microsegments to which the entities have been applied.
In some embodiments, the generated microsegments and the plurality of entities may have no entities in common. In a further embodiment, the plurality of entities may have no associated activity data. In these embodiments, activity data for the entities of the corresponding microsegment may be applied to the entities in the plurality of entities mapped or applied to that microsegment. In this way, spending behaviors may be analyzed for the entity in the plurality of entities by its association in a microsegment of entities with similar or the same characteristic data.

Methods for Measuring Advertising Effectiveness

FIG. 7 illustrates a method 700 for measuring advertising effectiveness using the system 600.
In step 702, a processor (e.g., the processor 102 of the financial transaction processing agency 108) may receive (e.g., by a receiving device) characteristic data for a plurality of entities (e.g., from the advertiser 118). The characteristic data may include geographical and/or demographical data associated with the plurality of entities. In an exemplary embodiment, the characteristic data may include an indicator of the exposure of an entity to an advertisement for a merchant (e.g., the merchant 104). In another exemplary embodiment, the characteristic data may not include personally identifiable information (PII). In some embodiments, the processor 102 may also receive from the advertiser 118 a predetermined period of time for which the advertiser 118 requests a measure of the effectiveness of the advertisement, if the advertiser 118 requests analysis of behaviors before and/or after the predetermined period of time, if (e.g., and which) competitors should be analyzed, what spend behaviors are requested, or if reports during the predetermined period of time are requested (e.g., and at what intervals).
In step 704, the processor 102 may generate test and control audiences (e.g., the test and control audiences 120 and 122). The test and control audiences 120 and 122 may be generated by applying the received entities to previously generated microsegments (e.g., based on the data in the enriched database 116) based on the associated characteristic data. The test audience 120 may include only those entities or corresponding microsegments that were indicated as exposed or deliberately exposed to the advertisement (which is not to say the individuals actually saw it or paid attention to it). The control audience 122 may include only those entities or corresponding microsegments that were indicated as not having been deliberately exposed to the advertisement, though of course some may have seen it. It is again noted that this may be temporal, meaning that the control audience is the same or overlapping with the test audience insofar as the control audience is measured before exposure, and then measured afterwards as the test audience. In an alternative embodiment, the processor 102 may receive indicators of exposure to the advertisement for the plurality of entities from a third party. In another alternative embodiment, the processor 102 may determine exposure to the advertisement for each entity based on spending behaviors, as discussed in more detail below. In one embodiment, all of the entities may have been exposed to the advertisement, and there may be no control audience 122.
In step 706, the processor 102 may determine if the predetermined time period has ended. If the predetermined time period has not ended (e.g., the campaign for which the advertiser 118 is requesting effectiveness on is ongoing), then the processor 102 may, in step 708, continue processing financial transactions for entities in the test and control audiences 120 and 122. In step 710, the processor 102 may analyze financial transactions (e.g., only those financial transactions processing since the most recent analysis as performed). In an exemplary embodiment, the processor 102 may analyze transactions on a weekly basis. In an alternative embodiment, the advertiser 118 may select a recurring time period for analysis during the predetermined time period.
In step 712, the processor 102 may generate a report based on the analysis performed in step 710. In one embodiment, a report may be generated every time the analysis is performed, e.g., weekly. In another embodiment, a report may be generated when requested by the advertiser 118. The report may include at least a report on the financial transactions processed including the entities or microsegments in the test audience 120 and/or the control audience 122. In an exemplary embodiment, the report may include only those financial transactions processed in step 708 and analyzed in step 710. In an alternative embodiment, the report may include analysis of financial transactions since the beginning of the predetermined period of time. Appendix A shows two samples output measurement reports. The first is a segment comparison (between different, non-overlapping segments) with measurement stream data; and the second is a report based on pre-advertisements and post advertisements to the same or overlapping segments with measurement stream data.
After the weekly report is generated (e.g., and transmitted to the advertiser 118, the merchant 104, or a third party), the processor 102 may return to step 706 and determine if the predetermined period of time has ended. If the predetermined period of time has ended, then, in step 714, the processor 102 may analyze spend behaviors for the test audience 120 and the control audience 122. The analysis of spend behaviors may include analyzing the spend behaviors of microsegments in each audience based on activity data stored in the external database 116. In one embodiment, the activity data stored in the external database 116 may include activity data for entities not included in the received plurality of entities from the advertiser 118. In an alternative embodiment, the activity data in the external database 116 may be associated with only entities that are not included in the received plurality of entities (e.g., the external database 116 and received data have no entities in common). Activity data of entities in the generated microsegments may be analyzed and applied to the entities identified by the advertiser 118 based on similarities in the corresponding characteristic data. In this way, spending behaviors of the entities identified by the advertiser 118 may be analyzed by analyzing the spend behaviors of other entities in the same microsegment.
The analysis of spend behaviors may include analyzing activity data (e.g., financial transactions) for at least one (e.g., or all) entities in a given microsegment. Spend behaviors analyzed by the processor 102 may include spending propensities for a given industry (e.g., the industry of the merchant 104), for a specific vendor (e.g., the merchant 104 or competitors of the merchant 104), or any other behavior that may be analyzed based on available activity data. In one embodiment, spend behaviors analyzed for the test audience 120 and the control audience 122 may include spend propensity for the merchant 104 and spend propensity for a competitive set of the merchant 104 (e.g., competitors in the same industry and/or geographical location as the merchant 104). Other types of spend behaviors that may be analyzed will be apparent to persons having skill in the relevant art and may include, for example, location type of transaction (e.g., online or offline, specific merchant location, etc.), number of transactions, average spending amount, etc.
In one embodiment, spend behaviors may be analyzed for activity only during the predetermined period of time. In an alternative embodiment, spend behaviors may also be analyzed for activity prior to and/or after the predetermined period of time. In one embodiment, spend behaviors may be requested by the advertiser 118. In some embodiments, projected spend behaviors may also be calculated or generated by the processor 102.
In step 716, the processor 102 may determine the effectiveness of the advertisement exposed to the entities or corresponding microsegments of the test audience 120. Methods of determining the effectiveness of an advertisement based on activity data will be apparent to persons having skill in the relevant art. For example, the effectiveness may be based on an increase in activity of the test audience 120 during the predetermined period of time, repeat business by entities or corresponding microsegments in the test audience 120 during or after the predetermined period of time, and/or first-time consumers transacting with the merchant 104 during the predetermined period of time. In step 718, a report on the effectiveness of the advertisement may be generated by the processor 102 (e.g., and transmitted to the advertiser 118, the merchant 104, and/or a third party). Useful data, metrics, and analysis that may be included in the report will be apparent to persons having skill in the relevant art.
FIG. 8 illustrates an alternative embodiment of a method 800 for measuring advertisement effectiveness using the system 600.
In step 802, a processor (e.g., the processor 102 of the financial transaction processing agency 108) may receive (e.g., by a receiving device) characteristic data for a plurality of entities (e.g., from the advertiser 118). The characteristic data may include geographical and/or demographical data associated with the plurality of entities. In an exemplary embodiment, the characteristic data may include an indicator of the exposure of an entity to an advertisement for a merchant (e.g., the merchant 104). In another exemplary embodiment, the characteristic data may not include personally identifiable information (PII). The processor 102 may also receive a selected predetermined period of time from the advertiser 118 for which the advertiser 118 requests a measure of the effectiveness of the advertisement.
In step 804, the processor 102 may generate a test audience (e.g., the test audience 120) and a control audience (e.g., the control audience 122), as discussed above with respect to step 704 illustrated in FIG. 7. In one embodiment, the test and control audiences 120 and 122 may include entities corresponding to the received characteristic data from the advertiser 118. In another embodiment, the test and control audiences 120 and 122 may include microsegments that share at least some (e.g., all) characteristic attributes with the plurality of entities received from the advertiser 118.
In step 806, the processor 102 may analyze spend behaviors for the merchant 104 by analyzing activity data (e.g., stored in the enriched database 116) for the corresponding microsegments of the test audience 120 and/or the control audience 122 that occurred prior to the predetermined period of time. Spend behaviors analyzed may include spending propensities for a given industry (e.g., the industry of the merchant 104), for a specific vendor (e.g., the merchant 104), or any other behavior that may be analyzed based on available activity data. In some embodiments, the advertiser 118 or the merchant 104 may identify the spend behaviors for analysis. In step 808, the spend behavior analysis may be performed for activity data corresponding to a competitor set (e.g., competitors in the same industry, geographic location, etc. of the merchant 104). In one embodiment, the competitor set may be identified by the advertiser 118 or the merchant 104.
In steps 810 and 812, the processor 102 may analyze spend behaviors of activity data for the entities or corresponding microsegments of the test audience 120 and the control audience 122 for financial transactions including the merchant 104 or the competitor set, respectively, that occur during the predetermined period of time. In steps 814 and 816, the processor 102 may perform the analysis for transactions that occur after the predetermined period of time.
In step 818, the processor 102 may determine the effectiveness of the advertisement using the spend behaviors analyzed in steps 806-816. Methods of determining advertising effectiveness based on analyzed spend behaviors will be apparent to persons having skill in the relevant art. For example, the effectiveness of the advertisement may be based on an increase in spend propensity for the merchant 104 during the predetermined period of time (e.g., as compared to the spend propensity prior to the predetermined period of time), a decrease in spend propensity for the competitor set during the predetermined period of time, an increased spend propensity for the merchant 104 after the predetermined period of time (e.g., as compared to the spend propensity prior to the predetermined period of time), a greater spend propensity for the merchant 104 than for the competitor set during and/or after the predetermined period of time, etc.
In step 820, a report on the determination performed in step 818 may be generated by the processor 102 (e.g., and transmitted to the advertiser 118, the merchant 104, and/or a third party). In one embodiment, the report may also include results of the analysis performed in at least one of steps 806-816.
The use of microsegments to determine advertising effectiveness as disclosed herein may provide more efficient and more accurate measurements. Furthermore, if the enriched database 116 and the received characteristic data for the plurality of entities contains no personally identifiable information, than the advertising effectiveness may be measured while maintaining consumer privacy and security. The analysis of spend behaviors without the use of P11 may be performed by applying the entities received from the advertiser 118 to microsegments generated by the processor 102 based on the data in the enriched database 116. The analysis (e.g., in steps 806-818) may be performed on activity data for the entities in the corresponding microsegments, which may then be applied to the received entities.

Exemplary Method for Measuring Effectiveness of an Advertisement

FIG. 9 illustrates an exemplary method 900 for determining the effectiveness of an advertisement.
In step 902, entity information associated with a plurality of entities may be stored in a database (e.g., by a processor such as the processor 102 of the financial transaction processing agency 108). The entity information may include activity information and characteristic information associated with the corresponding entity. In one embodiment, the activity information may include transaction details for financial transactions including the corresponding entity. In one embodiment, the characteristic information may include demographic information associated with the corresponding entity. In a further embodiment, the demographic information may include demographical, geographical, or other information associated with the corresponding entity. In an exemplary embodiment, the activity and characteristic information may not include personally identifiable information. In a further embodiment, the characteristic data may be bucketed or aggregated as to render it not personally identifiable.
In step 904, a plurality of microsegments may be generated (e.g., by the processor 102), each microsegment including a subset of the plurality of entities based on the associated characteristic information, wherein no two subsets of the plurality of entities contains a common entity. In one embodiment, each entity in a subset of the plurality of entities may have similar characteristic information. In a further embodiment, each entity in a subset of the plurality of entities may have the same characteristic information. In one embodiment, each subset of the plurality of entities may contain at least two entities. In an exemplary embodiment, each subset of the plurality of entities may contain at least ten entities.
In step 906, a test audience including a plurality of first microsegments and a control audience including a plurality of second microsegments may be generated (e.g., by the processor 102). Each entity in the plurality of first microsegments may be exposed to an advertisement associated with a merchant (e.g., the merchant 104) during a predetermined period of time, and each entity in the plurality of second microsegments may not be exposed to the advertisement during the predetermined period of time. Then, in step 908, a processor (e.g., the processor 102) may analyze the activity information for the entities in the plurality of first microsegments and the entities in the plurality of second microsegments to determine spending behaviors for the associated entity during the predetermined period of time.
In one embodiment, the spending behaviors may be based on financial transactions between the associated entity and the merchant. In another embodiment, the spending behaviors may be based on financial transactions between the associated entity and a competitor of the merchant. In one embodiment, step 908 may further include analyzing the activity information to determine spending behaviors for the associated entity during a period of time prior to the predetermined period of time. In an alternative embodiment, step 908 may further include determining spending behaviors for the associated entity during a period of time after the predetermined period of time. In a further embodiment, the processor may analyze the spending behaviors for the associated entity prior to, during, and after the predetermined period of time.
In step 910, the spending behaviors determined for the entities in the plurality of first microsegments may be compared (e.g., by the processor 102) with the spending behaviors determined for the entities in the plurality of second microsegments to determine the effectiveness of the advertisement. Then, in step 912, the effectiveness of the advertisement may be transmitted by a communication component (e.g., of the financial transaction processing agency 108). In one embodiment, the effectiveness of the advertisement may be transmitted to the merchant (e.g., the merchant 104).
Where methods described above indicate certain events occurring in certain orders, the ordering of certain events may be modified. Moreover, while a process depicted as a flowchart, block diagram, etc. may describe the operations of the system as occurring concurrently, it should be understood that many of the system's operations can occur in a sequential manner or in a different order. For example, although the spend behavior analysis for before, during, and after the predetermined time period ( steps 806, 810, and 814) is illustrated as occurring concurrently, the analysis may be performed in a sequential manner such that the behaviors prior to the predetermined period of time are analyzed before the behaviors during the predetermined period of time, or vice versa.
Techniques consistent with the present disclosure provide, among other features, a system and method for protecting consumer privacy in the creation of microsegments and audiences. While various exemplary embodiments of the disclosed system and method have been described above it should be understood that they have been presented for purposes of example only, not limitations. It is not exhaustive and does not limit the disclosure to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practicing of the disclosure, without departing from the breadth or scope.

APPENDIX A

Sample Output for Measurement Clients

Output file returned to Measurement Partner:
Segment Comparison Measurement Data Stream:

Segment Comparison	Wk1	Wk2	Wk3	Wk4	Wk5	Total

Index (with	Spend
Merchant	Segment A	100.00	101.00	102.01	103.03	104.06	510.10
awareness) -	Segment B	100.00	102.00	104.04	105.08	106.13	517.25
Wk1 Base	Segment C	100.00	101.00	102.01	104.05	106.13	513.19
	Transactions
	Segment A	100.00	101.00	102.01	103.03	105.09	511.13
	Segment B	100.00	101.00	102.01	103.03	105.09	511.13
	Segment C	100.00	102.00	104.04	106.12	108.24	520.40
	Transaction Size
	Segment A	100.00	100.00	100.00	100.00	99.02	99.80
	Segment B	100.00	100.99	101.99	101.99	100.99	101.20
	Segment C	100.00	99.02	98.05	98.05	98.05	98.61
Index (with	Spend
Merchant	Segment A	100.00	100.00	100.00	100.00	100.00	100.00
awareness) -	Segment B	80.00	80.79	81.59	81.59	81.59	81.12
Seg A Base	Segment C	120.00	120.00	120.00	121.19	122.39	120.73
	Transactions
	Segment A	100.00	100.00	100.00	100.00	100.00	100.00
	Segment B	90.00	90.00	90.00	90.00	90.00	90.00
	Segment C	120.00	121.19	122.39	123.60	123.60	122.18
	Transaction Size
	Segment A	100.00	100.00	100.00	100.00	100.00	100.00
	Segment B	88.89	89.77	90.66	90.66	90.66	90.14
	Segment C	100.00	99.02	98.05	98.05	99.02	98.81
Actuals (with	Spend
direct Merchant	Segment A	$10,000	$10,100	$10,201	$10,303	$10,406	$51,010
permission)	Segment B	$8,000	$8,160	$8,323	$8,406	$8,490	$41,380
	Segment C	$12,000	$12,120	$12,241	$12,486	$12,736	$61,583
	Transactions
	Segment A	50	51	51	52	53	256
	Segment B	45	45	46	46	47	230
	Segment C	60	61	62	64	65	312
	Transaction Size
	Segment A	$200	$200	$200	$200	$198	$200
	Segment B	$178	$180	$181	$181	$180	$180
	Segment C	$200	$198	$196	$196	$196	$197

Pre/Post Comparison Measurement Data Stream:

Pre/Post Comparison	Wk-4	Wk-3	Wk-2	Wk-1	Wk-1

Index (with	Spend
Merchant	Segment A	100.00	102.00	103.02	105.08	106.13
awareness) -	Segment B	100.00	101.00	102.01	104.05	105.09
Wk1 Base	Segment C	100.00	102.00	104.04	106.12	108.24
	Transactions
	Segment A	100.00	102.00	103.02	105.08	107.18
	Segment B	100.00	102.00	104.04	105.08	107.18
	Segment C	100.00	102.00	104.04	105.08	107.18
	Transaction Size
	Segment A	100.00	100.00	100.00	100.00	99.02
	Segment B	100.00	99.02	98.05	99.02	98.05
	Segment C	100.00	100.00	100.00	100.99	100.99
Index (with	Spend
Merchant	Segment A	100.00	100.00	100.00	100.00	100.00
Awareness) -	Segment B	80.00	79.22	79.22	79.22	79.22
Seg A Base	Segment C	120.00	120.00	121.19	121.19	122.39
	Transactions
	Segment A	100.00	100.00	100.00	100.00	100.00
	Segment B	90.00	90.00	90.89	90.00	90.00
	Segment C	120.00	120.00	121.19	120.00	120.00
	Transaction Size
	Segment A	100.00	100.00	100.00	100.00	100.00
	Segment B	88.89	88.02	87.15	88.02	88.02
	Segment C	100.00	100.00	100.00	100.99	101.99
Actuals (with	Spend
direct Merchant	Segment A	$10,000	$10,200	$10,302	$10,508	$10,613
permission)	Segment B	$8,000	$8,080	$8,161	$8,324	$8,407
	Segment C	$12,000	$12,240	$12,485	$12,734	$12,989
	Transactions
	Segment A	50	51	52	53	54
	Segment B	45	46	47	47	48
	Segment C	60	61	62	63	64
	Transaction Size
	Segment A	$200	$200	$200	$200	$198
	Segment B	$178	$176	$174	$176	$174
	Segment C	$200	$200	$200	$202	$202

Pre/Post Comparison Measurement Data Stream:

Pre/Post Comparison		Wk2	Wk3	Wk4	Pre Total	Post Total

Index (with	Spend
Merchant	Segment A	108.25	110.42	111.52	410.10	436.33
awareness) -	Segment B	107.19	108.26	109.35	407.06	429.89
Wk1 Base	Segment C	109.33	110.42	111.52	412.16	439.51
	Transactions
	Segment A	108.25	109.34	110.43	410.10	435.20
	Segment B	108.25	110.42	111.52	411.12	437.38
	Segment C	108.25	109.34	110.43	411.12	435.20
	Transaction Size
	Segment A	100.00	100.99	100.99	100.00	100.26
	Segment B	99.02	98.05	98.05	99.01	98.29
	Segment C	100.99	100.99	100.99	100.25	100.99
Index (with	Spend
Merchant	Segment A	100.00	100.00	100.00	100.00	100.00
Awareness) -	Segment B	79.22	78.44	78.44	79.41	78.82
Seg A Base	Segment C	121.19	120.00	120.00	120.60	120.88
	Transactions
	Segment A	100.00	100.00	100.00	100.00	100.00
	Segment B	90.00	90.89	90.89	90.22	90.45
	Segment C	120.00	120.00	120.00	120.30	120.00
	Transaction Size
	Segment A	100.00	100.00	100.00	100.00	100.00
	Segment B	88.02	86.30	86.30	88.01	87.14
	Segment C	100.99	100.00	100.00	100.25	100.73
Actuals (with	Spend
direct Merchant	Segment A	$10,825	$11,042	$11,152	$41,010	$43,633
permission)	Segment B	$8,575	$8,661	$8,748	$32,565	$34,392
	Segment C	$13,119	$13,250	$13,383	$49,459	$52,741
	Transactions
	Segment A	54	55	55	205	218
	Segment B	49	50	50	185	197
	Segment C	65	66	66	247	261
	Transaction Size
	Segment A	$200	$202	$202	$200	$201
	Segment B	$176	$174	$174	$176	$175
	Segment C	$202	$202	$202	$201	$202

Claims

1. A method of analyzing advertising effectiveness, comprising:

storing, by a database in a processing system, entity information associated with a plurality of entities, the entity information including activity information and characteristic information associated with the corresponding entity;

identifying a plurality of microsegments, each microsegment including a subset of the plurality of entities based on the associated characteristic information, wherein no two subsets of the plurality of entities contains a common entity at the same time;

identifying a test audience including a plurality of first microsegments and a control audience including a plurality of second microsegments, wherein each entity in the plurality of first microsegments is exposed to an advertisement associated with a merchant during a predetermined period of time and wherein each entity in the plurality of second microsegments is not deliberately exposed to the advertisement during the predetermined period of time;

analyzing, by a processor in the processing system, the activity information for the entities in the plurality of first microsegments and the entities in the plurality of second microsegments to determine spending behaviors for the associated entity during the predetermined period of time;

comparing the spending behaviors determined for the entities in the plurality of first microsegments with the spending behaviors determined for the entities in the plurality of second microsegments to determine the effectiveness of the advertisement; and

reporting, by a communication component in the processing system, the effectiveness of the advertisement.

2. The method of claim 1, further comprising:

analyzing, by a processor in the processing system, the activity information for the entities in the plurality of first microsegments and the entities in the plurality of second microsegments to determine prior spending behaviors for the associated entity during a prior period of time, wherein the prior period of time is a time occurring before the predetermined period of time, and

wherein the comparing step further includes comparing the prior spending behaviors for the entities to further determine the effectiveness of the advertisement.

3. The method of claim 1, further comprising:

analyzing, by a processor in the processing system, the activity information for the entities in the plurality of first microsegments and the entities in the plurality of second microsegments to determine subsequent spending behaviors for the associated entity during a subsequent period of time, wherein the subsequent period of time is a time occurring after the predetermined period of time, and

wherein the comparing step further includes comparing the subsequent spending behaviors for the entities to further determine the effectiveness of the advertisement.

4. The method of claim 1, wherein the entity information does not include any personally identifiable information.

5. The method of claim 1, wherein the activity information includes financial transaction information, and wherein the characteristic information includes demographic attributes associated with the entity.

6. The method of claim 5, wherein the entities in the test audience and the entities in the control audience have similar associated demographic attributes

7. The method of claim 5, wherein the financial transaction information includes information on financial transactions conducted between the associated entity and the merchant.

8. The method of claim 1, wherein the subset of the plurality of entities includes at least ten entities.

9. The method of claim 1, wherein the spending behaviors are based on transactions conducted by the entity with the merchant.

10. The method of claim 1, wherein the spending behaviors are based on transactions conducted by the entity with a plurality of competitors of the merchant.

11. A system for analyzing advertising effectiveness, comprising:

a database component configured to store entity information associated with a plurality of entities, the entity information including activity information and characteristic information;

a processor configured to

identify a plurality of microsegments, each microsegment including a subset of the plurality of entities based on the associated characteristic information, wherein no two subsets of the plurality of entities contains a common entity at the same time,

identify a test audience including a plurality of first microsegments and a control audience including a plurality of second microsegments, wherein each entity in the plurality of first microsegments is exposed to an advertisement associated with a merchant during a predetermined period of time and wherein each entity in the plurality of second microsegments is not exposed to the advertisement during the predetermined period of time,

analyze the activity information for the entities in the plurality of first microsegments and the entities in the plurality of second microsegments to determine spending behaviors for the associated entity during the predetermined period of time, and

compare the spending behaviors determined for the entities in the plurality of first microsegments with the spending behaviors determined for the entities in the plurality of second microsegments to determine the effectiveness of the advertisement; and

a communication component configured to report the effectiveness of the advertisement.

12. The system of claim 11, wherein the processor is further configured to:

analyze the activity information for the entities in the plurality of first microsegments and the entities in the plurality of second microsegments to determine prior spending behaviors for the associated entity during a prior period of time, wherein the prior period of time is a time occurring before the predetermined period of time, and

wherein comparing the spending behaviors further includes comparing the prior spending behaviors for the entities to further determine the effectiveness of the advertisement.

13. The system of claim 11, wherein the processor is further configured to:

analyze the activity information for the entities in the plurality of first microsegments and the entities in the plurality of second microsegments to determine subsequent spending behaviors for the associated entity during a subsequent period of time, wherein the subsequent period of time is a time occurring after the predetermined period of time, and

wherein comparing the spending behaviors further includes comparing the subsequent spending behaviors for the entities to further determine the effectiveness of the advertisement.

14. The system of claim 11, wherein the entity information does not include any personally identifiable information.

15. The system of claim 11, wherein the activity information includes financial transaction information, and wherein the characteristic information includes demographic attributes associated with the entity.

16. The system of claim 15, wherein the entities in the test audience and the entities in the control audience have similar associated demographic attributes

17. The system of claim 15, wherein the financial transaction information includes information on financial transactions conducted between the associated entity and the merchant.

18. The system of claim 11, wherein the subset of the plurality of entities includes at least ten entities.

19. The system of claim 11, wherein the spending behaviors are based on transactions conducted by the entity with the merchant.

20. The system of claim 11, wherein the spending behaviors are based on transactions conducted by the entity with a plurality of competitors of the merchant.