Analysis of Blind Box Consumption Data and Marketing Strategy Based on Improved Genetic Algorithm

With the development of Internet technology and the rise of fan economy, blind boxes have become an indispensable part of young people's lives. In recent years, blind box has become a popular economic phenomenon, and “blind box economy” has emerged. Blind box consumption has established a high degree of customer stickiness and brand dependence through the combination of accurate consumer groups in the early stage, in-depth development of IP resources and horizontal wide fields in the middle stage, and carefully planned marketing activities in the later stage. In this paper, we use the MapReduce programming of Hadoop cluster to implement improved genetic algorithm, which is used to quickly and accurately find the goods that best meet customer needs in the n-dimensional commodity space. The experimental results show that the improved genetic algorithm has an average increase of 15.6% in recommendation accuracy and 41.9% in recommendation response time compared with the traditional genetic algorithm.