Product group customer group extracting device, product group customer group extracting method, and product group customer group extracting program

A method for extracting a product group and a customer group, comprising

• estimating, based on history information, a purchase purpose of a purchase of a product, product information, and date and time information of the purchase of the product, wherein the purchase purpose is of which a customer has purchased the product, the history information comprises customer information of the customer, the product information is of the product purchased by the customer;
• generating learning data from the history information and the purchase purpose of respective customers of a plurality of customers, wherein the respective customers comprise the customer;
• extracting, based on the purchase purpose in the learning data, by clustering the learning data, a plurality of product groups associated with the respective customers, wherein the plurality of product groups comprises a product group, and the product group comprises the product purchased by the customer;
• and
• extracting, based on similarity among the plurality of product groups associated with the respective customers, a customer group, wherein the customer group comprises the customer.

A computer-readable non-transitory recording medium storing a computer-executable program instructions that when executed by a processor cause a computer system to execute operations comprising:

• estimating, based on history information, a purchase purpose of a purchase of a product, product information, and date and time information of the purchase of the product, wherein the purchase purpose is of which a customer has purchased the product, the history information comprises customer information of the customer, the product information is of the product purchased by the customer;
• generating learning data from the history information and the purchase purpose of respective customers of a plurality of customers, wherein the respective customers comprise the customer;
• extracting, based on the purchase purpose in the learning data, by clustering the learning data, a plurality of product groups associated with the respective customers, wherein the plurality of product groups comprises a product group, and the product group comprises the product purchased by the customer;
• and
• extracting, based on similarity among the plurality of product groups associated with the respective customers, a customer group, wherein the customer group comprises the customer.