Publication | Open Access
Customer profiling, segmentation, and sales prediction using AI in direct marketing
99
Citations
32
References
2023
Year
Marketing AnalyticsCustomer SatisfactionCustomer ExperienceEngineeringDigital MarketingCustomer ProfilingDirect MarketingEffective CommunicationBusiness AnalyticsUser SegmentationValidation MetricsData ScienceData MiningManagementMarket SegmentationPredictive AnalyticsKnowledge DiscoveryK-means Clustering AlgorithmMarketingCustomer Journey AnalysisInteractive MarketingSales Prediction
In today’s business environment, customer focus and effective communication between marketing and senior management are vital, and customer profiling is a cornerstone of strategic decision‑making for digital start‑ups seeking sustainable growth and satisfaction. The study investigates clustering customers using RFM analysis and validation metrics to determine optimal segments. K‑means clustering, guided by the Elbow, Silhouette, and Gap Statistics methods, identifies distinct customer segments. The analysis reveals three clusters—new, best, and intermittent customers—each with distinct needs, where tailored content, personalized incentives, and re‑engagement strategies can enhance engagement and drive sustainable growth.
Abstract In the current business environment, where the customer is the primary focus, effective communication between marketing and senior management is vital for success. Effective customer profiling is a cornerstone of strategic decision-making for digital start-ups seeking sustainable growth and customer satisfaction. This research investigates the clustering of customers based on recency, frequency, and monetary (RFM) analysis and employs validation metrics to derive optimal clusters. The K-means clustering algorithm, coupled with the Elbow method, Silhouette coefficient, and Gap Statistics method, facilitates the identification of distinct customer segments. The study unveils three primary clusters with unique characteristics: new customers (Cluster A), best customers (Cluster B), and intermittent customers (Cluster C). For platform-based Edutech start-ups, Cluster A underscores the importance of tailored learning content and support, Cluster B emphasizes personalized incentives, and Cluster C suggests re-engagement strategies. By understanding and addressing the diverse needs of these clusters, digital start-ups can forge enduring connections, optimize customer engagement, and fuel sustainable business growth.
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