Retail store product sales predicting method based on collaborative filtering

Abstract

The invention discloses a retail store product sales predicting method based on collaborative filtering. The sales prediction of unknown products (specifications) in retail stores is worked out based on the collaborative filtering technology according to sales data of known products (specifications) in different retail stores. According to the scheme, the method comprises the steps that firstly, data such as the retail stores, the products (specifications) and the known sales of the products in the retail stores are collected; secondly, sales-grade conversion is carried out on the known sales to obtain the corresponding grading value; thirdly, the retail stores correspond to users, the products correspond to projects, the grade is the sales-grading conversion result obtained in the second step, and unknown grades are estimated according to the collaborative filtering algorithm; fourthly, the sales-grade inverse transformation is carried out on the grade estimation worked out in the third step, and then the sales prediction is obtained. As the improvement, six kinds of sales-grade conversion and the corresponding inverse transformation are provided, and therefore the sales data of different products in the different retail stores have the comparable grading numerical values with the uniformed standard for the collaborative filtering computing. According to the designed predicting method, the enterprise accurate marketing can be achieved, and guidance can be provided for the marketing activity development and estimation of the products in the retail stores.

Description

technical field [0001] The invention belongs to the fields of product marketing and data mining, and in particular relates to the design of a method for predicting product sales at retail outlets based on collaborative filtering. Background technique [0002] Product precision marketing is to conduct a detailed analysis of different consumers in the target market through a combination of quantitative and qualitative methods. According to their different consumer psychology and behavior characteristics, the company adopts targeted modern technologies, methods and clear-cut strategies to achieve Marketing communication with strong effectiveness and high return on investment for different consumer groups in the target market. Due to the specific geographical location, environment and other characteristics of physical retail outlets, they are oriented to specific consumer groups and consumption habits. When manufacturers sell products through this traditional retail channel, the...

AI Technical Summary

Problems solved by technology

For offline traditional retail channels, due to the difficulty of obtaining specific customer data, there is currently no recommendation system application method for collaborative filtering

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