Planning Marketing Strategies in Small-Scale Business Using Data Analysis

Abstract

The proposed research work develops a system focused on business opportunities to enhance market returns and improve marketing strategies and new strategies by identifying how customers interact with products and their behavior. Existing research efforts attempt to identify and market consumer attraction to products and marketplace in the marketplace. Current research focuses on the challenges of identifying consumer buying patterns and how consumers interact with products, Existing research has not, however, integrated the essential elements into a single system. Consequently, the recommended study has been conducted on a number of significant issues, such as determining the high-value client base and the number of sectors, understanding the purchasing pattern of products that comprise the customers' basket, identifying customer lifetime value, and Customer Trajectory Determination for identifying customer attractive shelf. This system focuses on various machine learning algorithms. Customer segmentation and value analysis using K Mean, Agglomerative, Clustering algorithm, and Arima model. Association rules are generated using the Apriori algorithm for market basket analysis, which is built on the idea that a set of frequently purchased items is a subset of a set of frequently purchased items. Also using RFM analysis to create and prepare our data frame by using BG/NBD model and the Gamma-Gamma model to calculate the customer lifetime value standardization. Using image processing algorithms and retail video analysis algorithms, background reduction technology clearly identifies moving objects/ tracks customer routes using different colors. Based on results from implementation and testing, it was determined that the suggested technique outperformed the use of CCTV to identify consumer behavior and satisfaction with the product in recognizing customer purchasing patterns. The proposed system can identify customers' buying patterns, how customers interact with product...