SYSTEM AND METHOD FOR IDENTIFYING AND PRESENTING BUSINESS-TO-BUSINESS SALES OPPORTUNITIES
First Claim
1. A computer-implemented method for identifying product customer retention opportunities for a target customer, the method implemented by computer-executable instructions being executed by a computer processor comprising the steps of:
- inputting target customer data attributes and target product data for a target customer stored in memory;
inputting sales transaction data for the target customer stored in memory for one or more products, the sales transaction data comprising historical sales transactions for the target customer;
inputting customer data attributes, product data, product affinity data and sales transaction data for a group of core customers;
clustering a set of core customers into clustered customer groups whereby the customers in the clustered customer group have similar customer data selected from the group consisting of;
customer data attributes, product data, product affinity data and sales transaction data;
using the clustered customer groups, computing a benchmark spending model by product for the core customers within the group to determine a purchase pattern profile representative of the clustered customer group;
normalizing a multivariate spend pattern for the noncore, core and target customers for an evaluation period to compute a purchase pattern profile of the clustered customer group to interpret changes in customer spend pattern;
calculating a multivariate spend pattern of the target customer for a baseline period and the evaluation period by product;
using the calculated multivariate spend pattern of the customer for baseline and evaluation periods by product and the target customer data attributes, target product data, and target sales transaction data, computing customer retention opportunities for the target customer.
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Accused Products
Abstract
The present invention relates to a system and method for efficiently identifying the sales opportunities in a business-to-business market environment. It provides a computer-implemented predictive sales intelligence system and method for identifying sales opportunities. The present invention is a computer implemented system and method for efficiently identifying reliable purchase pattern profiles through scientific analysis of customer data. It includes a system and method for calculating a customer'"'"'s purchase profile, clustering customers based on similarity of their purchase profile, and efficiently providing a reliable set of opportunities including lost sales (retention) and cross-selling (wallet share expansion) opportunities. It uses this reliable estimate of sales opportunities to retain and expand wallet share for customers.
36 Citations
17 Claims
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1. A computer-implemented method for identifying product customer retention opportunities for a target customer, the method implemented by computer-executable instructions being executed by a computer processor comprising the steps of:
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inputting target customer data attributes and target product data for a target customer stored in memory; inputting sales transaction data for the target customer stored in memory for one or more products, the sales transaction data comprising historical sales transactions for the target customer; inputting customer data attributes, product data, product affinity data and sales transaction data for a group of core customers; clustering a set of core customers into clustered customer groups whereby the customers in the clustered customer group have similar customer data selected from the group consisting of;
customer data attributes, product data, product affinity data and sales transaction data;using the clustered customer groups, computing a benchmark spending model by product for the core customers within the group to determine a purchase pattern profile representative of the clustered customer group; normalizing a multivariate spend pattern for the noncore, core and target customers for an evaluation period to compute a purchase pattern profile of the clustered customer group to interpret changes in customer spend pattern; calculating a multivariate spend pattern of the target customer for a baseline period and the evaluation period by product; using the calculated multivariate spend pattern of the customer for baseline and evaluation periods by product and the target customer data attributes, target product data, and target sales transaction data, computing customer retention opportunities for the target customer. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A computer system comprising:
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a processor; a memory coupled to the processor; a display device; wherein the memory stores a program that identifies product customer retention opportunities for a target customer, when executed by the processor causes the processor to; input target customer data attributes and target product data for a target customer stored in memory; input sales transaction data for the target customer stored in memory for one or more products, the sales transaction data comprising historical sales transactions for the target customer; input customer data attributes, product data, product affinity data and sales transaction data for a group of reference customers; cluster a set of core customers into customer groups whereby the customers in the clustered customer group have similar customer data selected from the group consisting of;
customer data attributes, product data, product affinity data and sales transaction data;use the clustered customer groups to compute a benchmark spending model by product for the core customers within the group to determine a purchase pattern profile representative of the clustered customer group; normalize a multivariate spend pattern for the noncore, core and target customers for an evaluation period to compute a purchase pattern profile of the clustered customer group to interpret changes in customer spend pattern; calculate a multivariate spend pattern of the target customer for a baseline period and the evaluation period by product; use the calculated multivariate spend pattern of the customer for baseline and evaluation periods by product and the target customer data attributes, target product data, and target sales transaction data, to compute customer retention opportunities for the target customer.
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Specification