Customer satisfaction is the key of the success of any retail store or business and has an effect on their profitability. Companies have identified the importance to gain insights on their customer’s behavior, so as to better satisfy their needs. Both retailers and suppliers have
understood the need to cooperate in order to combine their data, gain knowledge and improve the offered services and customer satisfaction. Additionally, they have realized the potential mutual benefits if they combine their individual knowledge and expertise. Nowadays, the advent of business analytics, aids this cooperation. Thus, data mining techniques could be utilized to analyze the vast amount of data both retailers and suppliers have, to extract knowledge and support data-driven decision-making. However, not enough research has been
conducted to analyze such data in order to investigate consumers’ behavior regarding a specific product category. This study presents an effort to fill this gap by introducing a Data Mining-based framework, which could be used to discover sales affinities in customers' visits
in a supermarket related to a specific product category, and extract behavioral insights. The utility of this framework has been evaluated by applying it in real data of two representative supermarket stores of a Greek retailer. The proposed approach is useful for both an academic
and business perspective. It gives retailers the opportunity to extract how shoppers buy specific categories when they visit their stores, while it also enriches the suppliers’ knowledge about their shoppers. This knowledge can be used to support the decision-making process for all stakeholders in the retail domain, and improve their relationships with shoppers.