Analysis of Influencing Factors and Enterprise Strategy of Online Consumer Behavior Decision Based on Association Rules and Mobile Computing

Abstract

Consumers are constantly generating a large amount of data, thanks to the arrival of the big data era and the advancement of mobile edge computing capabilities. Massive behavioral data points to the need to mine and analyze potentially valuable information. The commonality and individuality of customer groups’ consumption behaviors must be researched before marketing decisions and strategies can be implemented. Because of the unique advantages of mobile edge computing technology, the Internet’s application has become more and more widespread, and businesses are increasingly paying attention to network marketing. With the system’s long-term use, decision-makers began to wonder if useful information could be extracted from vast amounts of historical data to help them summarize or even predict changes in customer demand and purchasing behavior. This assumption is possible thanks to the rise and development of data mining technology. Association rules are increasingly being applied to customer behavior analysis as the most active branch of data mining in the last ten years. The majority of association rule research currently focuses on one-dimensional data association analysis of a user’s package using classical algorithms. The use of association rules mining on multidimensional data with multiple attributes in the telecom service industry is limited due to the complexity of the data structure and algorithm.