The Research On Online Product Pricing Based On Multi-factor Anchoring Effect

With the rapid development of Internet and information technology,platform economy has become a global business model,and vigorously developing platform economy is an extremely important strategic deployment for China’s economic development.Under the platform model,online product pricing is an important factor for online retailers to occupy the market and gain profits,and directly affects the survival and development of enterprises.However,in the process of online consumption,consumers are strategic and learning.On the one hand,in order to simplify the decision-making process,consumers are unconsciously anchored by product price,sales,online reviews and other factors,and they will conduct anchoring-adjustment behavior.On the other hand,consumers will learn and process their cognition based on product data,word of mouth and their own purchasing experience.The consumption habits of consumers have undergone great changes,which no longer satisfy the hypothesis of traditional rational man.Therefore,in the trend of increasingly fierce market competition,the factors of consumer’s anchoring-adjustment psychology and learning behavioral characteristics pose new challenges to the product pricing of online retailers.Based on this,this paper will consider the characteristics of online consumers’ anchoring psychology,loss avoidance and social learning in the complex environment,and conduct research on online product pricing in the platform economy.Firstly,this paper summarizes the current research methods and progress of product pricing and consumer behavior by combing relevant domestic and foreign literature,and makes targeted comments.Through the analysis of the existing literature,the reality of the online pricing mechanism and purchasing environment,we summarized the research problem of online product pricing under multi-factor anchoring effect,determined to sort out the anchoring factors and anchoring mechanism that affect consumer behavior,explored the optimal product pricing strategy under the consumer anchoring and learning behavior,and put forward feasible management suggestions and operation strategies as the research objective,and further determined the anchoring effect,Bayesian theory and prospect theory as the theoretical basis of this paper.Secondly,considering that platform customers are anchored by product price and sales,we use the Multinomial Logit Model(MNL)to study customer choice behavior and online product pricing based on the anchoring-adjustment heuristic.Thirdly,in the context of discount advance selling,we use MNL model to establish an N-stage dynamic pricing model considering that consumers are anchored by product price and sales,and analyze the effect of two-factor anchoring effect and platform sales update rules on product dynamic pricing,sales and profit of online retailers.Finally,from the perspective of consumer social learning,based on the utility maximization criterion,we consider that consumers can enhance product cognition by browsing online reviews one by one in the process of purchasing decisions,and use anchoring effect and Bayes rule to quantify the cognitive updating process,and establish a product revenue pricing model.Using deep learning method and Python crawler technology to conduct four-category sentiment analysis on review text,we study the influence of the order and ratio of positive and negative review information on consumer cognition,product pricing,sales and profit.The results show that the anchoring point and degree have an important influence on the optimal pricing.When the customer is only anchored by sales,there is a monotonous relationship between the optimal solution and anchoring degree.Due to the anchoring effect,discount advance selling is more suitable for online retailers to sell new products and can bring advance selling ‘dividends’.However,restricted by sales update rules,this advance selling ‘dividends’ will disappear after a period of time.Online retailers should constantly optimize product structure and performance,and improve the matching degree between products and consumer needs to improve the market competitiveness of products.Different from traditional cognition,a small amount of moderate negative information is beneficial to product pricing and marketing compared with zero evaluation.Online retailers should make proper use of product-related data to predict consumer behavior and adjust pricing strategy dynamically on a regular basis to optimize operation management.This study summarized the factors affecting consumer purchase behavior,puts forward the problems existing in the online product pricing,exploration platform enterprise’s pricing strategy and operations management mechanism under the complex environment,provided a more flexible and specific pricing mechanism from the perspective of consumer psychology and data driven for online retailers,further expanded the behavior operation management theory,and enrich the platform online product pricing system.