Data Fusion Model for High-Tech Products Marketing

Product differentiation is one of the highlights of the success of today’s increasingly competitive marketing. This article aims to study the construction of differentiated marketing strategies for high-tech products. This paper proposes to investigate and study customers, high-tech industry workers and leaders, etc., through the analysis of massive data, introduce the concept of data fusion and the weighting algorithm for data fusion, introduce DS evidence theory to judge the accuracy of the data on this basis, make the data more real and clean up abnormal data, perform relevant data processing on the determined data, and calculate and analyze the data differences of relevant groups. It also proposes to refer to the characteristics of high-tech products, understand the characteristics and implementation directions of differentiated marketing, draw differences between different groups, summarize customer characteristics and marketing directions, so as to trace the needs of customer groups, find customer pain points and difficulties, pinpoint product positioning, and achieve the goal of differentiated marketing. The experimental results of this paper show that big data fusion analysis can find customers in a targeted manner and polish the highlights of high-tech products. Compared with previous results, the performance is improved by more than 30%. Consumer satisfaction increased by more than 20%.