Demographic analysis using time-based consumer transaction histories

Abstract

Systems, apparatus, and methods for determining groups of similar consumers and for identifying a trend in consumer behavior are provided. Likelihoods of a transaction being initiated at various times by one consumer can be calculated based on previous transactions of the consumer. The likelihoods for different consumers can be used to determine a group of similar consumers as a demographic. The likelihoods of a transaction being initiated at various times by a consumer of a demographic (or other entity) can be used to forecast trends (such as a demand for a product) and make business decisions, such as for marketing campaigns, inventory levels (e.g. at particular stores or for all stores), pricing, and store locations. Such likelihoods when focused to a particular category of transactions can provide even greater accuracy.

Description

CROSS-REFERENCES TO RELATED APPLICATIONS[0001]The present application claims priority from and is a non provisional application of U.S. Provisional Application No. 61 / 175,381, entitled “SYSTEMS AND METHODS FOR DETERMINING AUTHORIZATION, RISK SCORES, AND PREDICTION OF TRANSACTIONS” filed May 4, 2009, the entire contents of which are herein incorporated by reference for all purposes.[0002]This application is related to commonly owned and concurrently filed U.S. patent applications entitled “PRE-AUTHORIZATION OF A TRANSACTION USING PREDICTIVE MODELING” by Faith et al. (attorney docket number 016222-046210US), “DETERMINING TARGETED INCENTIVES BASED ON CONSUMER TRANSACTION HISTORY” by Faith et al. (attorney docket number 016222-046220US), “TRANSACTION AUTHORIZATION USING TIME-DEPENDENT TRANSACTION PATTERNS” by Faith et al. (attorney docket number 016222-046240US), and “FREQUENCY-BASED TRANSACTION PREDICTION AND PROCESSING” by Faith et al. (attorney docket number 016222-046250US), the ent...

AI Technical Summary

Benefits of technology

[0006]Embodiments provide systems, apparatus, and methods for determining groups of similar consumers and for identifying a trend in consumer behavior. Certain embodiments can use likelihoods of a transaction being initiated at various times to determine a group of similar users as a demographic (affinity group). The likelihoods at a plurality of times can be used to forecast trends (such as a demand for a product) and make business decisions, such as for marketing campaigns, inventory levels (e.g. at particular stores or for all stores), pricing, and store locations. Such likelihoods when focused to a particular category of transactions (e.g. a particular product) can provide even greater accuracy.

Problems solved by technology

Thus, a marketing campaign or other business decision based on such demographics can be faulty due to conflicting data since the consumers have disperse characteristics.

Moreover, such demographics typically provide only very general information, and thus do not provide information for making specific business decisions.

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