INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ARTIFICIAL INTELLIGENCE · 2015 · 67 citations · 7 references
Marketing AnalyticsCustomer SatisfactionEngineeringMachine LearningBusiness IntelligenceCustomer ProfilingBig Data AnalyticsBusiness AnalyticsTargeted Customer ServicesUser SegmentationEfficient Customer SegmentationOptimization-based Data MiningData ScienceData MiningCustomer SegmentationManagementMarket SegmentationClustering (Nuclear Physics)Systematic Customer SegmentationKnowledge DiscoveryBusiness Data MiningMarketingCustomer Journey AnalysisClustering (Data Mining)Marketing InsightsCustomer ServiceK-means Algorithm
The competitive business environment has heightened the importance of customer segmentation, and the rise of big data and machine learning has made automated approaches like k‑means attractive for identifying customer groups with shared market characteristics. This study applies the k‑means clustering algorithm to segment customers for targeted service and marketing. A MATLAB implementation of k‑means was trained on a z‑score‑normalised dataset of 100 retail customers, using average monthly purchase amount and visit frequency as features. The algorithm identified four distinct clusters—High‑Buyers‑Regular‑Visitors, High‑Buyers‑Irregular‑Visitors, Low‑Buyers‑Regular‑Visitors, and Low‑Buyers‑Irregular‑Visitors—with 95 % accuracy.
The emergence of many business competitors has engendered severe rivalries among competing businesses in gaining new customers and retaining old ones. Due to the preceding, the need for exceptional customer services becomes pertinent, notwithstanding the size of the business. Furthermore, the ability of any business to understand each of its customers’ needs will earn it greater leverage in providing targeted customer services and developing customised marketing programs for the customers. This understanding can be possible through systematic customer segmentation. Each segment comprises customers who share similar market characteristics. The ideas of Big data and machine learning have fuelled a terrific adoption of an automated approach to customer segmentation in preference to traditional market analyses that are often inefficient especially when the number of customers is too large. In this paper, the k-Means clustering algorithm is applied for this purpose. A MATLAB program of the k-Means algorithm was developed (available in the appendix) and the program is trained using a z-score normalised two-feature dataset of 100 training patterns acquired from a retail business. The features are the average amount of goods purchased by customer per month and the average number of customer visits per month. From the dataset, four customer clusters or segments were identified with 95% accuracy, and they were labeled: High-Buyers-Regular-Visitors (HBRV), High-Buyers-Irregular-Visitors (HBIV), Low-Buyers-Regular-Visitors (LBRV) and Low-Buyers-Irregular-Visitors (LBIV).
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