System, method, and computer program product for increasing inventory turnover using targeted consumer offers

Abstract

Consumer, merchant, and transactional data from a closed loop network and external sources may be leveraged to increase demand of a merchant's inventory during normally low-demand periods. Extensive data mining is used to determine the excess merchant inventory and demand patterns at different times and different locations for merchants and groups of merchants. Similar data mining is used to analyze cardmember demand patterns to identify the cardmember preferences regarding when and where they which to purchase goods and/or services. Cardmembers may also be grouped based on their demand patterns. Using pricing as a lever, cardholders with specific preferences are targeted to shift the demand from peak periods and locations to non-peak periods and locations, and to increase the non-peak demand by location as well as time period. Higher precision may be obtained using product level transaction data from point-of-sale terminals used by merchants wherever applicable.

What is claimed is: 
     
         1 . A method, comprising:
 sorting, by a computer-based system configured for tailored marketing of consumers and according to a concentric circles algorithm, the consumers based on a likelihood of the consumers to respond to tailored marketing for a merchant, and based on a desire of the merchant for demand during a period of excess inventory,
 wherein the concentric circles algorithm includes the tailored marketing to a first group of consumers that are more likely to respond because the first group of consumers are existing high spending and highly profitable consumers of the merchant, and 
   progressively including, by the computer-based system, the tailored marketing to a second group of consumers based on a desire of the merchant for increased demand and depth of inventory,
 wherein the second group of consumers include consumers that are lower spending and less profitable than the first group of consumers. 
   
     
     
         2 . The method of  claim 1 , further comprising, identifying, by the computer-based system, the period of excess inventory of the merchant based on merchant data. 
     
     
         3 . The method of  claim 1 , further comprising combining, by the computer-based system, merchant data with related transaction data. 
     
     
         4 . The method of  claim 1 , further comprising segmenting, by the computer-based system, merchant data and transaction data based on at least one of: time, industry, location, complementary merchants, or competing merchants. 
     
     
         5 . The method of  claim 1 , further comprising segmenting, by the computer-based system, merchant data and transaction data to identify inventory turnover opportunities using a merchant demand pattern. 
     
     
         6 . The method of  claim 1 , further comprising analyzing, by the computer-based system, merchant data to determine a merchant revenue cycle. 
     
     
         7 . The method of  claim 1 , further comprising tailoring, by the computer-based system, marketing to the consumers to shift demand towards the merchant to create the tailored marketing. 
     
     
         8 . The method of  claim 1 , further comprising identifying, by the computer-based system, dates and times that the merchant posts the least revenue to determine a period of excess inventory. 
     
     
         9 . The method of  claim 1 , further comprising identifying, by the computer-based system, demand patterns of the merchants having a consumer base similar to a consumer base of the merchant to determine a period of excess inventory. 
     
     
         10 . The method of  claim 1 , further comprising contacting, by the computer-based system, the merchant to offer opportunities for the tailored marketing. 
     
     
         11 . The method of  claim 1 , further comprising integrating, by the computer-based system, point of sale data from the merchant into merchant data. 
     
     
         12 . The method of  claim 1 , further comprising determining, by the computer-based system, consumer spending patterns related to an industry of the merchant. 
     
     
         13 . The method of  claim 1 , further comprising analyzing, by the computer-based system, consumer spending patterns based on data from at least one of a competing merchant of the merchant or a complementary merchant of the merchant. 
     
     
         14 . The method of  claim 1 , wherein the sorting further comprises:
 identifying, by the computer-based system, transactions for the merchant over a period of time, wherein the period of time comprises a subperiod;   summarizing, by the computer-based system, the identified transactions to include a sum of an amount spent per consumer and a number of transactions per consumer for the subperiod;   determining, by the computer-based system, demographic information and financial information for each consumer;   determining, by the computer-based system, physical distance from each consumer to the merchant; and   sorting, by the computer-based system, the consumers based on the transaction summary, the demographic information, the financial information, and the physical distance for each consumer.   
     
     
         15 . The method of  claim 1 , wherein the sorting further comprises sorting, by the computer-based system, consumers based on volume of transactions of each consumer within low and high demand periods. 
     
     
         16 . The method of  claim 1 , wherein the sorting further comprises sorting the consumers based on at least one of: transactions of the consumers with competing merchants in a vicinity and direct marketing area of the merchant; transactions of the consumers with all competing merchants; transactions of the consumers with merchants in a same industry category as the merchant; or transactions of the consumers with merchants in complementary industries. 
     
     
         17 . The method of  claim 1 , wherein the sorting further comprises sorting, by the computer-based system, the consumers according to a weighted scoring mechanism. 
     
     
         18 . The method of  claim 1 , wherein the sorting further comprises:
 assigning, by the computer-based system, a weight to each of: a transaction amount per consumer, a number of transactions per consumer, and physical distance from each consumer to the merchant;   assigning, by the computer-based system, a value for each consumer to each of: a transaction amount per consumer, a number of transactions per consumer, and a physical distance from each consumer to the merchant;   determining, by the computer-based system, a score for each consumer based on assigned weights and assigned values; and   sorting, by the computer-based system, the consumer based on the score for each customer.   
     
     
         19 . A system comprising:
 a processor configured for tailored marketing of consumers;   a tangible, non-transitory memory configured to communicate with the processor;   the tangible, non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations comprising:
 sorting, by the processor and according to a concentric circles algorithm, the consumers based on a likelihood of the consumers to respond to tailored marketing for a merchant, and based on a desire of the merchant for demand during a period of excess inventory,
 wherein the concentric circles algorithm includes the tailored marketing to a first group of consumers that are more likely to respond because the first group of consumers are existing high spending and highly profitable consumers of the merchant, and 
 
 progressively including, by the processor, the tailored marketing to a second group of consumers based on a desire of the merchant for increased demand and depth of inventory,
 wherein the second group of consumers include consumers that are lower spending and less profitable than the first group of consumers. 
 
   
     
     
         20 . An article of manufacture including a non-transitory, tangible computer readable storage medium having instructions stored thereon that, in response to execution by a computer-based system configured for tailored marketing of consumers, cause the computer-based system to perform operations comprising:
 sorting, by the computer-based system and according to a concentric circles algorithm, the consumers based on a likelihood of the consumers to respond to tailored marketing for a merchant, and based on a desire of the merchant for demand during a period of excess inventory,
 wherein the concentric circles algorithm includes the tailored marketing to a first group of consumers that are more likely to respond because the first group of consumers are existing high spending and highly profitable consumers of the merchant, and 
   progressively including, by the computer-based system, the tailored marketing to a second group of consumers based on a desire of the merchant for increased demand and depth of inventory,
 wherein the second group of consumers include consumers that are lower spending and less profitable than the first group of consumers.