US 2011/0213661 A1 · Computer-Implemented Method For Enhancing Product Sales

Abstract

A computer-implemented method for providing product recommendations to customers is disclosed. For each customer for whom a product recommendation is desired, hereinafter “the target customer”, a group of other customers, hereinafter “a group of nearest neighbors” is identified using an algorithm that examines each customer's previous spending across a plurality of different product categories and optionally, one or more demographic attributes, and then selects those customers whose attributes most nearly matches that of the target customer. The products purchased by these nearest neighbors that have not been purchased by the target customer are then ranked using a ranking algorithm that weights such purchased products based on the frequency of their purchase and the number of nearest neighbors purchasing such products. A product recommendation for the target customer is formed by selecting a predetermined number of the highest ranked products purchased by the nearest neighbors.

Claims23

Dependent claims

A computer-implemented method of recommending one or more products offered by a seller to an existing target customer of that seller, said method comprising the steps of

identifying, with respect to the target customer, at least one similar customer of the seller whose spending attributes approximate that of the target customer,
identifying the products purchased by each similar customer that have not been purchased by the target customer during a predetermined time interval,
ranking the identified products based on at least one factor, and
providing a product recommendation for the target customer that includes at least one product from the ranked identified products.

1 more element

A computer-implemented method of providing a product recommendation for a target customer, the product recommendation being with respect to products of a seller that extend over a plurality of product types and the target customer being an existing customer of the seller, said method comprising the steps of

assigning the target customer and each of a plurality of other existing customers of the seller to one of a plurality of clusters based on their respective spending attributes with respect to the seller, the cluster to which the target customer, is assigned also including first customers,
forming a reference group for the cluster to which the target customer has been assigned, said reference group comprising those first customers that are within the top N % of spending with respect to the seller in a predetermined time period and wherein N is a predetermined percentage,
determining a signature vector for the target customer and for each customer in the reference group, each signature vector having S dimensions wherein each of the S dimensions represents a different category of products sold by the seller and wherein the magnitude of each dimension of the vector of the target customer corresponds to the target customer's spending in the product category corresponding to that dimension and the magnitude of each dimension of the vector of each customer in the reference group corresponds to that customer's spending in the product category corresponding to that dimension,
determining P nearest neighbors for the target customer, wherein P is an integer, by processing the signature vectors that have been formed,
identifying the products purchased by each of the P nearest neighbors that have not been purchased by the target customer,
ranking the identified products using a ranking algorithm, and
providing a product recommendation for the target customer from the ranked identified products, the recommendation including at least one of the ranked identified products.

4 more elements

Dependencies are inferred from the claim text.

Computer-Implemented Method For Enhancing Product Sales

Life of this patent

Computer-Implemented Method For Enhancing Product Sales

Life of this patent

Abstract

Claims23