CUSTOMER SEGMENTATION IN OMNI CHANNEL ENVIRONMENT USING PRINCIPAL COMPONENT BASED K MEANS CLUSTERING
Keywords:
E-Commerce, Omni Channel Strategy, Customer Segmentation, K-Means Clustering, Shopping Experience, Customer LoyaltyAbstract
Along with spawning a plethora of new retail formats, the explosive growth of e-commerce and the growing maturity of mobile payments have also had a significant impact on customer preferences and buying patterns. The improvement in the quality of consumption has led to customers being more conscious of the promptness and ease of buying, which has encouraged the merging of offline and online sales channels. Using an Omni channel strategy could provide a company with a competitive edge in attracting and keeping consumers while also ensuring the long-term viability of the company. By channel integration, consistency, and a smooth customer experience, an organization may become Omni channel by placing the customer at the center of all business interactions. Our goal in this research is to create a consumer segmentation model that will enhance the retailing market industry's decision-making procedures. The three main categories of client segmentation characteristics are behavioral, psychographic, and geographic. The behavioral component of the client has been the focus of this investigation. Consequently, the clustering method will analyze user data to ascertain the E-commerce system's purchasing patterns. Clustering is done to maximize dissimilarity between clusters and optimize experimental similarity within a cluster. To assist vendors in determining the most profitable portion to focus on rather than the less profitable portion, which can play a significant role in enhancing the business, our research examined groups that had comparable criteria. Furthermore, the suggested methodology can be utilized for any dataset linked to trade, such as sustainability and health-related data, to ascertain consumer behavior. This kind of analysis might be beneficial in enhancing the company. Putting their customers in groups based on shared behavioral traits can help them stay in business longer and make more money. Additionally, it allows for maximum exposure of the e-offer to attract the interest of possible clients. This study presents a customer segmentation methodology based on K-means clustering. In this research, we have used K-means clustering to evaluate the dataset to determine whether we can divide the customers into several groups (clusters) that share traits and behaviors. A more profound comprehension of the patrons of shopping centers is what we seek. Then, by using this data, marketers can better target, segment, and relate to their target audiences. It might also be applied to loyalty programs and enhance the general shopping complex patron experience.
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