Identifying Customer Satisfaction Patterns Via Data Mining: The Case Of Greek E-Shops

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 strategie

Extracting Greek Elections Tweet’s Characteristics

Social media offer platforms that anyone can use, giving the opportunity to share information among networks in an easy and interactive way. It is not a surprise that social media marketing has become a primary focus on both digital and traditional revenue models of businesses. In this work, information sharing by users in the context of Twitter is studied, by modeling message’s characteristics and users’ behavior about Greek 2015 January elections. A detailed data set about tweets’ characteristics such as length, existence of URLs or hashtags and mentioning of other users, is collected after the elections day, and the relationships between related users and network’s responses on the shared tweets, are examined. An unsupervised clustering model is implemented on tweets’ characteristics using CRISP-DM methodology. The empirical results suggest the existence of different content groups, such as tweets with extensive text, URLs and hashtags which can be characterized as “Linked” type of shared content