Statistical Modeling and Simulation of Online Shopping Customer Loyalty Based on Machine Learning and Big Data Analysis

Abstract

With the increase in the number of online shopping users, customer loyalty is directly related to product sales. This research mainly explores the statistical modeling and simulation of online shopping customer loyalty based on machine learning and big data analysis. This research mainly uses machine learning clustering algorithm to simulate customer loyalty. Call the k-means interactive mining algorithm based on the Hash structure to perform data mining on the multidimensional hierarchical tree of corporate credit risk, continuously adjust the support thresholds for different levels of data mining according to specific requirements and select effective association rules until satisfactory results are obtained. After conducting credit risk assessment and early warning modeling for the enterprise, the initial preselected model is obtained. The information to be collected is first obtained by the web crawler from the target website to the temporary web page database, where it will go through a series of preprocessing steps such as completion, deduplication, analysis, and extraction to ensure that the crawled web page is correctly analyzed, to avoid incorrect data due to network errors during the crawling process. The correctly parsed data will be stored for the next step of data cleaning or data analysis. For writing a Java program to parse HTML documents, first set the subject keyword and URL and parse the HTML from the obtained file or string by analyzing the structure of the website. Secondly, use the CSS selector to find the web page list information, retrieve the data, and store it in Elements. In the overall fit test of the model, the root mean square error approximation (RMSEA) value is 0.053, between 0.05 and 0.08. The results show that the model designed in this study achieves a relatively good fitting effect and strengthens customers’ perception of shopping websites, and relationship trust plays a greater role in maintaining customer loyalty.

A New Approach in Marketing Research: Identifying the Customer Expected Value through Machine Learning and Big Data Analysis in the Tourism Industry

“…In other words, machine learning help to regulate and discover the new ways and algorithms based on which computer would be able to learn. This learning happens in two ways" supervised learning" and "unsupervised learning" (Huang et al, 2021).…”

“…Also, consumer behavior analysis has testified with great accuracy (Kumar, Venkatesh & Rahman, 2021). Therefore, many companies have used machine learning algorithms to highlight hidden knowledge in consumer data in order to create a better experience for their customers, as well as improve their operational factors, such as speed and cost and time savings (Huang et al, 2021). Also, mechanized and computer-based market building has enabled machine learning algorithms to interact directly with markets and enable complex instant buy and sell orders based on complex decisions and identify potential customers with high potential (Kumar, Venkatesh & Rahman, 2021).…”

Customer Value and Customer Loyalty: Comparison and Application

“…There are many applications of customer value and customer loyalty in different fields. For example, some researchers explore the statistical modeling and simulation of online shopping customer loyalty based on machine learning and big data analysis [1]. Their research mainly uses a machine-learning clustering algorithm to simulate customer loyalty and construct an online customer loyalty measurement model.…”

“…There are many researches in the field of customer value and customer loyalty. For example, researchers have explored customer value and customer loyalty in online shopping [1], customer relationship management [2], and product recommendation [3]. However, there are few reviews on customer value and customer loyalty so far.…”

Know Your Enemy: User Segmentation Based on Human Aspects of Information Security

“…Most identified studies employ an experimental research design. For example, clustering has been applied to various research areas, such as users of smartphone applications [32], [33], behavior of retail customers [34], downloading behavior of academic search engine users [35], online shopping customer loyalty [36], and mobile network users [37]. All these studies use automatically generated data (e.g., application usage, online shopping transactions) as input into user segmentation.…”