Research And Forecast Of Product Price Optimization And Revenue Management Based On Machine Learning

With the rapid development of Internet technology and the substantial improvement of China's overall strength,people tend to live a intelligent life.The Sharing Economy relying on big data technology has emerged and developed vigorously.Particularly,Car-sharing has become one of the fastest growing industries in modern tourism and transportation.Similar to Airlines and hotels,Car-sharing has all the features and conditions to perform demand forecasting and revenue management.However,there is little research on these issues in the past few years.Therefore,based on the hotel industry's revenue management experience and machine learning algorithms,this paper makes a study of Cars-haring industry about its demand forecasting and revenue management through a combination of theoretical derivation and empirical analysis.First,this paper summarizes the theory and practice related to demand forecasting and revenue management of the travel industry,mainly including demand forecasting algorithms based on time series,machine learning,and neural networks,and classic models for revenue management based on dynamic pricing and inventory scheduling.Meanwhile,taking as a background of the actual data in many domestic travel companies,depth analysis of the effect of subjective and objective factors for demand forecasting has been taken.After data processing and feature extracting,different forecasting methods are used to predict future travel demand at different classification levels,and the best prediction model and optimal parameters have been chosen.Finally,there is a time series Prophet model about price to simulate the initial acceptable pricing range decision of managers.Besides,we customize the personalized loss function to optimize the final recommended price according to the demand forecasting and the current goals of the enterprise.Then we will obtain the comparison results of managers adopting or rejecting suggestions through evaluation indicators under different objective considerations.Research shows that the proposed demand prediction model,in this paper,has better prediction results than classical regression algorithms and differentiated pricing strategies.Moreover,the formulated price optimization scheme has more flexible expressive ability and more general in different demand strengths.The main difference from previous works is that we fully consider the applicability of tools such as machine learning algorithms and the specificity of Car-sharing data.And this work would provide a scientific theoretical basis for the practitioners of Car-sharing revenue management to formulate price in some extent.