The product marketing model of the economic zone by the sensor big data mining algorithm

Abstract

Since the reform and opening up, China's market economy has gradually prospered, and the development of various industries and their products has become a key research issue of the government. In the context of big data, this article believes that future industrial development should be driven by big data. At the same time, the advent of the era of big data will surely bring about changes in the marketing model of products in the economic zone. This article puts forward the research on product marketing model based on big data mining algorithm, the purpose is to do a detailed investigation of the core algorithm of data mining, in order to improve the marketing efficiency of enterprises and reduce the investment and loss of marketing. The method of this paper is to study the Hadoop parallel framework and the K-Means algorithm in the mining algorithm to collaboratively filter the big data mining algorithm, and integrate these algorithms into the marketing process. These methods can recommend products to target users based on their previous interests and other users who have similar interests. Finally, this article sorts the web pages according to the PageRank value of each node in order to achieve the purpose of accurately recommending products to customers. Through the K-means algorithm mining data experiment and financial product marketing examples, this article puts forward some suggestions and measures for the marketing model. The experimental results show that the customer response rate of precision marketing is 11.92 % higher than that of ordinary marketing, and the sales efficiency of marketers has improved significantly. In addition, precision marketing is better than ordinary marketing in many aspects such as the proportion of large orders, average consumption price, and refund rate.