Consumer behavior analysis model based on machine learning

Abstract

The accurate mastery of demand information enables retailers to better respond to consumers and effectively manage inventory. However, the precise connection and interaction between this information collection and inventory management is more difficult to measure. In view of this, this paper proposes a research on inventory model based on consumer web search. Moreover, centering on the two main actors in the online search environment, consumers and retailers, this paper fully considers their characteristics and situations to construct an inventory model in the online search environment, and analyzes the ordering strategy. Moreover, based on the digital traces left by consumers in the decision-making process, this paper uses general search indicators and specific search indicators to measure consumer web search to explore the relationship between consumer web search indicators and the demand conversion rate proposed in the model. Moreover, this paper analyzes the model with examples. The research results are in line with model construction expectations.