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• W4308796786 abstract "The use of the social media (SM) has become more and more widespread during the last two decades, the companies started looking for insights for how they can improve their businesses using the information accumulating therein. In this regard, it is possible to distinguish between two lines of research: those based on anonymous data and those based on customer specific data. Although obtaining customer specific SM data is a challenging task, analysis of such individual data can result in very useful insights. In this study we take up this path for the customers of a bank, analyze their tweets and develop three kinds of analytical models: clustering, sentiment analysis and product propensity. For the latter one, we also develop a version where, besides the text information, the structural information available in the bank databases are also used in the models. The result of the study is a considerably more efficient set of analytical CRM models." @default.
• W4308796786 created "2022-11-15" @default.
• W4308796786 creator A5087559913 @default.
• W4308796786 date "2023-01-30" @default.
• W4308796786 modified "2023-09-25" @default.
• W4308796786 title "Social media analytical CRM: a case study in a bank" @default.
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• W4308796786 doi "https://doi.org/10.3233/jifs-221619" @default.
• W4308796786 hasPublicationYear "2023" @default.
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