Customer Data Processing in Internet of Things Environments Using RFM Analysis and K-means Clustering

Abstract

Businesses, shops, banks, and other types of organizations all engage in cutthroat competition today. The retrieval techniques are responsible for arranging, processing, and listing the documents in the corpus in response to the user query. The strategies are distinct from one another in any of the processes stated above. As a result, the body of published work is crammed full of different retrieval theories and strategies. It is possible to combine the advantageous aspects of several different strategies to improve the retrieval systems' overall performance. Once again, the success of the merging process is dependent on the careful selection of the separate schemes that will be merged together. The selection process is carried out using optimization-seeking tools. The Genetic Algorithm is going to be used for this job. Using GA as the instrument and with the intention of evaluating both the even and the odd point crossover's effects, The odd and even point crossover is primarily employed as an exploratory tool, and its influence on the Internet of Things is evaluated throughout the information retrieval process. The enormous combination that results from the fusion function retrieval strategies and their weights may be understood as follows: The investigation is the only thing that can help us find the best answer out of all of these possible permutations. As a method of investigation, we made use of both odd and even point crossing. This exploration tool has a lack of convergence, which is a setback. It is possible to get a higher convergence rate by combining the genetic algorithm with tabu search, which is the best local search. In a scenario like this one, customer segmentation may be helpful in bringing in new customers while also helping to keep the ones you already have. An effective customer segmentation strategy for a business splits consumers into groups based on the RFM (Recency, Frequency, and Monitor) values of the Monitors. These groups have behavior in common. This will be of use to us in determining the possible customers for the firm. Following the completion of an RFM analysis, we use a conventional k-means method in order to extend the scope of the research to include clusters. Maintaining positive relationships with customers makes it much easier to market effectively to certain demographics of consumers, which in turn helps bolster a company's competitive position.