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“I can get no e-satisfaction†. What analytics say? Evidence using satisfaction data from e-commerce

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  • Griva, Anastasia

Abstract

This study mines customer satisfaction (CS) segments using almost 270 thousand responses from a CS survey which ran in 140 e-commerce stores of a European country. To achieve this, it develops and applies a business analytics (BA)-informed framework. Then, it presents examples of how one e-commerce store exploited the extracted CS segments to build automated marketing actions for its customers, ranging from social media sharing strategies for the satisfied segments, to discounts for the less satisfied. This study contributes to customer satisfaction and segmentation literature. The extracted insights can be utilized to support decision making, ranging from targeted advertising for specific customer segments, to benchmarking for companies in similar industries.

Suggested Citation

  • Griva, Anastasia, 2022. "“I can get no e-satisfaction†. What analytics say? Evidence using satisfaction data from e-commerce," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:joreco:v:66:y:2022:i:c:s0969698922000479
    DOI: 10.1016/j.jretconser.2022.102954
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    1. Son, Youngdoo & Kim, Wonjoon, 2023. "Development of methodology for classification of user experience (UX) in online customer review," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).

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