A Cluster-based Analysis for Targeting Potential Customers in a Real-world Marketing System

Abstract

In today’s fast-paced, customer-focused marketing world, customer service management is key to revenue growth and profitability. Customer behavior knowledge can help marketing managers reevaluate existing customer tactics and plan to improve and increase effective strategies. Maintaining a productive relationship with business customers is crucial because business transactions require more decision-making and professional buying effort than consumer purchases. Most customer segmentation methods based on customer value fail to account for time and value changes. Today’s business is based on new concepts because so many customers are unsure of what to buy. Businesses can’t identify target customers. Machine learning algorithms detect hidden data patterns to make better decisions. Customer segmentation groups customers by gender, age, interests, and spending habits. Customers with unique needs are segmented by companies. Companies want to know their customers. Their goals must be clear and individualized. By analyzing data, businesses can better understand customer preferences and identify profitable segments. This helps them strategize their marketing while minimizing investment risk. Customer segmentation depends on many factors. Demographic, geographic, economic, and behavioural data help the company approach different sectors. Customer segmentation uses clustering to determine which consumer segment to target. This paper demonstrates machine learning-based customer segmentation. This is the unsupervised clustering problem, and we apply four of the most popular clustering algorithms: K-Means, Affinity Propagation, DBSCAN, and Hierarchical Clustering. We find the optimal number of clusters from a shopping mall dataset using the Elbow method and Silhouette score with the key factors like age, spending score, and annual income of customers. It helps shopping malls improve business by finding customer behavioural patterns. We compare the results using the Silhouette and Davies-Bouldin scores. We find that Affinity Propagation and K-Means work best.

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