Data Mining Techniques to Enhance Customer Segmentation and Targeted Marketing Strategies

The retail industry is facing an ever-increasing challenge of effectively identifying and targeting its customers. Using traditional segmentation techniques to fully capture the intricate and ever-changing character of customer behavior is difficult. This project will examine sales data from a general shop using an assortment of data mining technologies in order give insights into customer habits and purchasing trends. Retail sales records builds the dataset. K-means clustering, association rule mining, and regency, the frequency, and monetary (RFM) analysis will all be employed to look into the data. This study contributes to create something of focused marketing strategies and consumer segmentation by identifying high-value and atrisk clients. Association rule mining illuminates consumer taste and actions by identifying hidden patterns and correlations in large datasets. These discoveries extend the scope of our comprehension of consumer purchasing habits and offer data for more targeted advertising initiatives. Additionally, the K-means clustering algorithm divides customers according to their purchasing habits and behavior, allowing profound knowledge to enhance marketing and sales strategies. Findings from the research will give an extensive awareness of customer behavior and purchasing dynamics, which will improve the efficacy of the general store’s marketing and sales campaigns. The most effective technique for exploiting insights from sales data will be discovered by contrasting the outcomes of RFM analysis, K-means clustering, and association rule mining. This work promises to make substantial improvements to data mining and buyer behavior research algorithms, and it has the capacity to be implemented across an extensive selection of corporate restrictions intended to improve their sales strategies.