Model of Real-Time Market Segmentation Based on Usage Scenarios Under the Monitoring Platform

Abstract

Precise market segmentation is a prerequisite for commercial vehicle companies to effectively carry out product development and marketing. This paper introduces an index-based improved spatial-temporal big data computing platform, uses adjacent continuous storage technology to improve the data reading performance of the monitoring platform, proposes two models of interest point (POI) matching and adaptive interest point clustering, analyzes the actual use of the vehicle, and then provides a real-time segmentation market identification and segmentation model. In response to the characteristics of real-time changes in vehicle usage scenarios, adaptive learning methods are introduced to solve the problems of the generation of new market segments and the refinement of the original market segments. The model is deployed on the actual monitoring platform and verified in actual use scenarios, experimental results and practical applications show that the results obtained are of great significance for studying the actual characteristics of vehicles and guiding product improvements and upgrades.