The evaluation of marketing competitiveness of B2B E-commerce enterprises based on optimized deep learning networks

Abstract

In the context of e-commerce trade between enterprises, there are problems such as price wars, false advertising, and unreasonable operations aimed at seizing market share. However, traditional estimation methods cannot provide a reasonable evaluation of the marketing competitiveness of e-commerce enterprises from an overall perspective. To help enterprises better clarify their own position and quantify their marketing competitiveness, a competitiveness evaluation model based on optimized deep learning networks is proposed. By combining subjective and objective evaluation methods, the indicators that affect the marketing competitiveness of enterprises are assigned to obtain the final competitiveness value. The research outcomes expressed that the max absolute error of the model constructed by the research institute was 0.0006, the max relative error was 0.0045, and the model accuracy was 99.85%. In the secondary indicator experiment of marketing competition, the model determined that the turnover rate of fixed assets had the greatest impact, with a weight value of 0.1263. Nine companies were randomly selected for market value estimation, and the average relative error of the model was 14.90%, which was lower than the mean relative error of the relative valuation, cash flow and absolute valuation methods, with numerical differences of 8.03%, 2.94%, and 0.12%, respectively. The research findings illustrated that the model constructed by the research institute had good performance and certain reference values for evaluating the marketing competitiveness of e-commerce enterprises.

Keywords

• marketing competitiveness
• backpropagation neural network
• e-commerce enterprises
• absolute valuation method
• combination weight coefficient method