Intellectual analysis of consumer demand in terms of information asymmetry

Abstract

Purpose: the work is aimed at solving the relevant problem of segmentation of the customer base, identifying the intentions of consumers and studying consumer demand for selected categories of goods and services for a certain period of time in order to optimize the sales plan. Discussion: the proposed method is based on the application of clustering algorithm based on the method of k-means relative to the dynamic characteristics of customers about their purchases, when there is no need to maintain personalized customer accounting. The implementation of the k-means algorithm and its modifications, that is quite simple and flexible, is shown in the work with the use of spreadsheet tools. Results: the questions of choosing the number of clusters, measures of proximity of objects for their separation between clusters, as well as the interpretation of the results of clustering and further analysis and application of the obtained partition are considered. The implemented technique allows, having data about the portrait of the target audience, to address different segments of the customer base with certain proposals, thereby increasing customer loyalty. With the help of the described methods and approaches it is possible to effectively investigate the models of consumer demand for individual sets of goods depending on the intentions of different groups of buyers.