In an online marketplace reality in which customer satisfaction emerges as a key success factor for e- retailers, it becomes crucial to better understand whether the shoppers are satisfied and what factors affect their satisfaction experience. As we are in the Big Data era, Business Analytic techniques could assist us to better understand our customers and their respective satisfaction. To this end, this paper presents a data mining based approach to identify different satisfaction patterns/profiles from satisfaction survey responses. This approach was applied on data from over 120 Greek e-shops across 18 industries. Apart from its theoretical contribution, the proposed approach extracts hidden satisfaction patterns with a view to better understand the specific needs and preferences of customers. These insights may be used to support several decisions, ranging from marketing actions per customer satisfaction profile, to actionable decision making and customer-oriented strategies
Kalaidopoulou, Katerina; Triantafyllou, Stratos; Griva, Anastasia; and Pramatari, Katerina, "Identifying Customer Satisfaction Patterns Via Data Mining: The Case Of Greek E-Shops" (2017). MCIS 2017 Proceedings. 16.