Customer Value Evaluation Model for SMEs Based on K-means Clustering Algorithm

Abstract

With the acceleration of the process of global economic integration, the market competition has become increasingly fierce, and the competition among SMEs has changed from the competition and technologies to the competition of customer value. The purpose of this paper is to build a customer value evaluation model for SMEs based on k-means clustering algorithm. Based on customer value theory, it analyzes the functions and costs that customers care about. Explore ways to maximize customer value through analysis of customer functions and customer costs. The AHP is used to determine the weight coefficient. After the weight is determined, based on the customer's questionnaire, data analysis is performed on the questionnaire to determine the proportion of each level of customer function indicators in the indicator area, and then according to the fuzzy Comprehensive evaluation to determine the function coefficient. The survey results show that the factors that affect customer value can be attributed to two points, namely customer function and customer cost. In terms of improving customer function, corporate image accounts for 46% and has the largest weight; Currency costs accounted for 26 percentage points higher.