Research On Car Sales Forecasting Method Based On Improved Bass Model

With the global economic development and the improvement of living standards,the automobile industry has experienced continuous growth and transformation.Over the past few decades,the automobile industry has become one of the crucial pillar industries in many countries.Due to the acceleration of production technology updates and the constant changes in consumer demand,competition in the automotive market has become increasingly fierce.Accurate sales forecasting is significant for the development of automotive enterprises,helping to optimize production processes,formulate reasonable business strategies,and thereby establish competitive advantages.Hence,forecasting automobile sales is a crucial issue for automobile enterprise managers.However,the accurate prediction of automotive sales poses certain challenges,especially given the influence of numerous factors,including an increasingly competitive market environment.Nowadays,with the rapid development of internet technology,consumers are more inclined to browse car information online and post comments,making online reviews a potent form of consumer feedback.This has a significant impact on automotive sales.Online reviews can provide more extensive consumer data,thereby more comprehensively understanding consumer needs.Meanwhile,internet search behavior records consumers’ potential consumption footprints.By analyzing search data,companies can better understand user needs and preferences,adjust automobile production and marketing strategies,and increase sales.In traditional methods of automobile sales forecasting,few consider the impact of online reviews and competition simultaneously.This article studies the automobile sales forecasting method based on online reviews,internet search data,and competitive factors.It aims to improve the accuracy of automobile sales forecasting through the improved Bass model.The Bass model is a classic market prediction model,widely used in the field of new product sales forecasting.However,the traditional Bass model has limitations when considering competitive factors.Therefore,this article proposes an automotive sales forecasting method based on the improved Bass model.The main research work is carried out from the following aspects:(1)A revised Bass model that considers competitive factors is proposed,which gives a method to determine the competitive coefficient combining network search data and online ratings,and a method to select competitive products.(2)Based on this,it integrates online reviews,processes online reviews according to attributes,and calculates the sentiment value of online text comments through the sentiment dictionary method,thereby improving the imitation coefficient of the Bass model.(3)An application study of the improved Bass model’s automotive sales forecasting method,considering online reviews and competitive factors,is presented.Taking the BMW 5 series sales forecast as an example,it verifies the rationality of the method proposed in this article.Finally,the improved Bass model in this article is compared with the traditional Bass model,ARIMA,linear regression,and SVM methods.The results show that the sales forecasting accuracy of the improved Bass model,which considers online reviews and competitive factors,is higher than other methods.