Associating Behavioural Patterns with Machine Learning to Understand Consumer Behaviour – A Jewellery Industry Case Study

Abstract

 The current economic landscape presents a dynamic scenario marked by globalization, intensifying competition, and rapid advancements in communication and information technology. In response to these challenges, businesses are compelled to move away from conventional marketing principles and embrace a customer-centric paradigm. This approach prioritizes the effective management of customer relationships as the cornerstone of success. Improving customer relationships at different stages of the customer lifecycle through machine learning is a transformative approach that has revolutionized the way businesses interact with their clientele. In this paper, we delve into the potential of utilizing machine learning techniques to empower businesses in multiple phases of the customer lifecycle. Specifically, during the acquisition phase, machine learning proves invaluable in enabling targeted marketing campaigns increasing Return on Investment (ROI) and crafting personalized offers. This, in turn, yields higher conversion rates and effectively reduces acquisition costs, optimizing the early stages of customer interaction. As customers progress to the retention phase, machine learning models come into play, forecasting customer churn and providing valuable insights for proactive retention strategies. By anticipating potential churn, companies can take swift actions to retain their most valuable customers. Additionally, as businesses seek opportunities for expansion, machine learning unveils upselling and cross-selling prospects, tapping into increased revenue streams from their existing customer base. The research is conducted with retail data from premium jewellery shop.