The Percise Markteing Method And Stragety Of Dining Ecommerce Platform Based On Clustering Algorithm

With the integrative trend of world economy, China’s marketizationdeepen, and the penetration of information technical, consumerrequirement appear the trend of differentiation and individuation. In sucha market environment, retail enterprises should properly use the moderninformation technical, review customer behavior characteristics, designmore efficiency, precise marketing strategy.This paper adopts a combination of theoretical analysis and dataempirical research, aim to study the ecommerce platform of K company-the leading restaurant chain company over china, build online mealordering customer behavior model based on data mining technologyunder the guidance of precision marketing theory, and propose a targetedorient customer management and marketing methodology based onsegmentation customer groups.This paper has six chapters, the main content includes as follows:1. Introduction and literature review, propose concept model andhypothesis base on relative theory.2. According to external environment PEST analysis, internal SWOT analysis of K company ecommerce platform, this paper make aclear judgment of enterprise ecommerce operation current stage,then compare with the successful precision marketing case,conclude precision marketing strategy based on operationexperience, provide theory framework of next action step.3. Consumer behavior modeling. Extract and clean consumerbehavior data on ecommerce platform, use k-means clusteringalgorithm to build behavior model based on customerdecision-making behavior and ordering behavior. Aftersegmentation, the consumer of K company could be divided into5groups: cost control group, promotion driven group, businessordering group, impulsive group and family group.4. Based on model of consumer behavior, this paper propose to usethe model into practical marketing process in the company,especially focus on loyalty maintain and promote, communicationstrategy of subdivided group, personalized service, and designdifferent measures of different business scenario.5. The conclusion and thanks.This research provides the necessary quantitative theoretical supportin the meal ordering ecommerce industry applications for the data miningtechnology. It provide decision-making basis for the meal orderingecommerce marketing strategy options, and enhance the efficiency and effectiveness of marketing strategy. Therefore, it has a significance intheoretical research and the practical application.