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Identifying customer satisfaction patterns via Data Mining The case of Greek online shops

Thesis

Identifying customer satisfaction patterns via Data Mining The case of Greek online shops

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It is a marketplace reality that more e-retailers promise their customers that online experiences will be satisfying, and therefore it becomes crucial to understand what creates a satisfying customer experience. In a competitive setting, such as the Internet market, where competition may be only one click away, it is even more important to satisfy the customers, as switching costs have been diminished. Therefore, many organizations have identified the need to not only understand customer purchase behavior, but also the customer satisfaction through the online purchase journey. Additionally, technological advances enable direct communication with customers by sending surveys and/or promotional information. This new method of collecting customer responses has led to large volumes of satisfaction data, available for knowledge extraction. Many researches, in both academia and the industry have focused on these data in order to calculate the satisfaction levels or identify those factors which affect satisfaction. However, to the best of my knowledge there is no research which is focused on extracting customer satisfaction patterns from survey data in order to identify different satisfaction types. To address that issue, this thesis presents a Data Mining based framework, which can be used to identify customer satisfaction types from satisfaction survey data in an e-shop or in e-commerce in general. A customer satisfaction type describes how satisfied the customer is in terms of specific attributes, and how the satisfaction is changed between them, i.e. attributes. For instance, some customers may be highly satisfied with the website characteristics such as navigation or security, but less satisfied with product characteristics, such as price or variety. Such an information can be extracted by applying the clustering data mining technique to customer satisfaction data, and more specifically by applying the k-means algorithm to satisfaction surveys collected after the customer checkout and after the order delivery. This thesis is based on the Design Science approach, which means that it first explores the relevant researches for the research problem and then an artifact is developed. The artifact is a Data Mining based framework, which draws on CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology. It describes an explicit way to handle the data through the following five phases. (A) Business and Data Understanding, with sub-tasks the Data Acquisition and Data Exploration, (B) Data Preparation with sub-tasks the Data modeling and Data Sampling, (C) Data Mining Modeling, (D) Evaluation, and (E) Deployment. As part of the Data Modeling task, the framework proposes the following sub-tasks: Data Integration, Data Cleansing, Data Transformation, and Data Validation. Then the proposed framework is applied to real data and more specifically to survey data from 83 e-shops and 11 industries of the Greek market. The results, evoked from the customer web-based surveys in two phases, indicate that there exist different types of customer satisfaction. Next, this thesis uses sales data from one of the 83 e-shops in order to extract customer segments based on their purchase history and combine the results with the satisfaction types. It is validated that different customer segments have different prominent satisfaction types, indicating that e-shops should address each segment’s need separately. Lastly, the proposed framework has some limitations, but it contributes a lot to both academia and the industry. It identifies for the first time the customer satisfaction types from satisfaction surveys, using a clustering algorithm. In light of the major findings, the thesis sets forth strategic implications for customer loyalty in the setting of e-commerce. Moreover, it combines the results with customer segments and thus enriches the results with a combination of knowledge from two sources. Such finding can support e-shops’ decision making and customer-oriented strategies. Lastly, further research could be applied using a different Data Mining technique and evaluate if different ways of identifying satisfaction types exist, e.g. by applying text analytics on customer reviews.
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