Consumer identity resolution based on transaction data

a technology of transaction data and consumer identity, applied in the field of consumer identity resolution based on transaction data, can solve the problems of inability to specifically target marketing for product x to that potential consumer, difficulty in ascertaining exactly what characteristics to target, and difficulty in ascertaining whether a given person actually has the targeted characteristics and/or how to actually reach specific persons with those specific characteristics

Inactive Publication Date: 2014-06-26
QUOTIENT TECH INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For example, an entity that sells product X may determine that it wants to market to persons that have purchased a complimentary product Y within a timeframe of Z. Of course, one problem with targeted marketing is that it is sometimes difficult to ascertain exactly what characteristics to target.
Unfortunately, it is also generally difficult to ascertain whether a given person actually has the targeted characteristics and/or how to actually reach specific persons that have those specific characteristics.
For example, a particular retailer may have

Method used

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  • Consumer identity resolution based on transaction data
  • Consumer identity resolution based on transaction data
  • Consumer identity resolution based on transaction data

Examples

Experimental program
Comparison scheme
Effect test

example electronic receipts

[0481]FIG. 1 depicts a display of an email 100 comprising an electronic receipt 130 with offer information 151-154 that has been provided to an electronic address 111 of a consumer. Email 100 may be displayed in the depicted manner by, for example, a client-based or web-based email application that interprets various fields, metadata, and or markup language included in email 100. Email 100 comprises a variety of header metadata 110, some or all of which may be displayed at the top of the email. Header metadata 110 may include, for example, an electronic address 111 of a consumer. Email 100 may further include a message 120.

[0482]Electronic receipt 130 of email 100 is depicted in FIG. 1 as one or more hyper-text markup language (“HTML”) tables, but in other embodiments the electronic receipt may take any suitable form. Electronic receipt 130 comprises transaction details 135, such as a transaction number, a time or date of the transaction, a store location at which the transaction to...

example receipt

List Interface

[0496]FIG. 36 illustrates a webpage 3600 comprising an interactive list 3650 of a consumer's receipts, according to an embodiment. A transaction aggregation area 3601 displays the total number of receipts, total savings, and total spending for the consumer, as aggregated from all transactions mapped to the consumer. List 3650 comprises a number of different receipt summaries 3655, which may display different information depending on the view. For example, as depicted, the receipt summaries 3655 depict a date, day of week, transaction total, payment type, and total savings. Clicking on a receipt summary 3655 may take the consumer to a receipt web page such as web page 3500 above. Or, clicking on or hovering over a receipt summary 3655 may cause the summary to be transformed into an expanded receipt summary. Or, clicking on or hovering over a receipt summary may cause the expanded receipt summary to display in a popup overlay. Examples of expanded receipt summaries are d...

example kpis

[0535]Example metrics to be used in generating recommendations include, without limitation, those in the following table. These KPIs may be used in a number of functions, including, without limitation: assessing the performance of coupons, and identify the best-selling ones or the coupons with the estimate the benefits for the distributor and other stakeholders, identify the best customers, estimate the revenue for the distributor and other stakeholders (manufacturers, retailers) that each offer generates, post filter the generated recommendations by applying specific thresholds, either hard or soft, that one, several or a function of these KPIs need to satisfy, assess the performance of recommendations by estimating the lift that the provided recommendations generate in each of the monitoring KPIs, training the recommendation engine, calculating a universal score for an offer, scoring potential offers to propose that a provider create, estimating a potential offer's effectiveness, ...

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PUM

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Abstract

According to an embodiment, a data processing system for facilitating consumer identity resolution comprises: a first logic module adapted to receive at least two collections of consumer records from at least two different sources; a second logic module adapted to compute first trust scores for first data fields of the first collection and second trust scores for second data fields of the second collection; a third logic module adapted to generate a master collection of consumer records comprising at least one master consumer record that is correlated to a record from the first collection and a record from the second collection, the correlation being based on at least on the trust scores; and a fourth logic module adapted to receive a set of contextual transaction data. The data processing system is adapted to identify a consumer based on the contextual transaction data and the master collection of consumer records.

Description

BENEFIT CLAIM / RELATED APPLICATIONS[0001]This application claims the benefit under 35 U.S.C. §119(e) of Provisional Application 61 / 745,566, filed Dec. 22, 2012, and of Provisional Application 61 / 788,009, filed Mar. 15, 2013, the entire contents of each of which applications are hereby incorporated by reference for all purposes as if fully set forth herein.[0002]This application is related to: “Checkout-Based Distributed Of Digital Promotions” by Steven R. Boal, U.S. application Ser. No. 13 / 233,557, filed Sep. 15, 2011; U.S. application Ser. No. 13 / 831,716, filed Mar. 15, 2013; “Checkout-Based Distributed Of Digital Promotions” by Steven R. Boal, U.S. application Ser. No. 13 / 244,817, filed Sep. 26, 2011; “Check-Out Based Distribution And Redemption Of Digital Promotions” by Steven R. Boal, U.S. application Ser. No. 13 / 332,317, filed Dec. 20, 2011; “Automatic Recommendation of Digital Offers to an Offer Provider based on Historical Transaction Data” by Steven R. Boal, Attorney Docket N...

Claims

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Application Information

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IPC IPC(8): G06Q40/00
CPCG06Q30/0245G06Q30/0269G06Q40/10G06Q30/0255G06Q30/0251G06Q30/0207G06Q30/04G06Q30/06G06Q30/0201G06Q10/107G06Q20/209G06Q30/0211
Inventor BOAL, STEVEN R.
Owner QUOTIENT TECH INC
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