Modeling product stickiness in a grocery storeOražem, Maruša (Avtor) Šubelj, Lovro (Mentor) grocery storeStickiness Indexmachine learninganalysisIn the business world, keeping customers coming back and staying loyal is crucial for making money. We often measure this loyalty through customer satisfaction and how often people buy from a store or brand. This master’s thesis introduces the innovative index called Stickiness Index, tailored to capture and quantify repetitive purchases of items in the grocery stores. Our primary aim is to construct a robust Stickiness Index that effectively models items with high stickiness—those frequently repurchased after initial acquisition. The research unfolds in three key stages, including 3 different approaches for calculating Stickiness Index. Each stage is upgraded version of the previous, from simple event counting to incorporating item frequencies in the equation. In each stage we reveal the results and rationalize them. Additional fourth stage is presented, where we look at different item frequencies and how they affect the Stickiness Index. Analyzing the results, including price dynamics, item categorization, and seasonality, informs our understanding of consumer behavior. Moreover, this study demonstrates the real-world utility of the Stickiness Index by integrating it into machine learning models and strategic guidance for decision-making including the Stickiness Index.20232023-10-11 13:55:02Magistrsko delo/naloga151595sl