×

Method of predicting sales based on triple-axis mapping of customer value

  • US 7,577,579 B2
  • Filed: 04/26/2002
  • Issued: 08/18/2009
  • Est. Priority Date: 05/31/2001
  • Status: Active Grant
First Claim
Patent Images

1. A sales-predicting method which is performed by execution of computer readable program code using at least one processor of at least one computer system, based on triple-axis mapping of customer value, employing a computer system that collects and analyzes purchase-history data in a database;

  • the computer system comprising a processing server having a group of analyzing programs for analyzing customer value, a database server for managing various databases, a web server, and a data input/output terminal connected to a communication line;

    the database server comprising a customer-purchase-history database for accumulating purchase-history data including (1) a customer name or code, (2) a product code, (3) the quantity of items purchased, (4) the amount expended on purchases, and (5) the time of purchase, with the purchase-history data collected through transactions of sales outlets for the merchandise, electronic transactions conducted via the Internet, and direct transactions including transactions by telephone and mail when customers purchase products of a specific manufacturer or brand in a specific market; and

    a total manufacturer/brand customer-purchase-history database for accumulating purchase-history data including product-purchase history of membership credit cards having a common ID and affiliated with a plurality of businesses in various industries, as well as data obtained through questionnaires and/or marketing approaches at sales outlets, questionnaires and/or marketing approaches in electronic transactions via the web server, questionnaires and/or marketing approaches by direct mail, email, and telephone, and customer data reported by sales clerks;

    the sales-predicting method comprising the steps;

    creating, using at least one of the processors, a purchase-amount index (first axis) of cells by searching the customer-purchase-history database at specified periods and classifying customers into a plurality of classifications in order of the amounts expended on purchases or the quantity of items purchased, based on purchase-history data extracted during the search;

    creating, using at least one of the processors, a user-type index (second axis) of cells by searching the customer-purchase-history database at said specified periods and classifying customers into a plurality of classifications according to user type as determined by combinations of merchandise from customers who purchase the majority of types of the merchandise to customers who purchase 0 or 1 type of the merchandise, by combining a plurality of types of merchandise by specific manufacturers or brands purchased in each specified period;

    creating, using at least one of the processers, a current customer-value map by dividing customers into cells according to a product of the plurality of classifications in said first index and said second index for analyzing the current value of customers in each cell;

    classifying, using at least one of the processers, customers by relevance by searching said total manufacturer/brand customer-purchase-history database at said specified periods and classifying customers by purchase amount into a plurality of categories according to purchase monetary sums or quantities of items purchased based on data abstracted from the total manufacturer/brand customer-purchase-history database;

    classifying, using at least one of the processers, customers by relevance by searching said total manufacturer/brand customer-purchase-history database at said specified periods and classifying customers by user type into a plurality of categories according to combinations of merchandise purchased during said specified periods, from customers who purchase the majority of the types of merchandise to customers who purchase 0 or 1 type of the merchandise, based on data extracted during the search;

    creating, using at least one of the processers, a customer-purchase-relevance index (third axis) based on a product of the plurality of classifications from each of said two relevance classifying steps; and

    creating, using at least one of the processers, a future customer-value map by dividing customers into cells formed by a product of each plurality of classifications in said user-type index (second axis) and said customer-purchase-relevance index (third axis) to analyze the future value for customers in each cell.

View all claims
  • 4 Assignments
Timeline View
Assignment View
    ×
    ×