Behavioral and Migration Analysis of the Dynamic Customer Relationships on Twitter

ORCID Identifiers

0000-0001-9565-2375

Document Type

Article

Source of Publication

Information Systems Frontiers

Publication Date

1-1-2020

Abstract

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Relationship management has been of strategic importance for businesses that are interested to evaluate the state of the relationship with the customer and if possible to migrate customers to better and more binding states. This work addresses the problem of estimating the relationship state of a customer and examining the migration policy of the customer, using social media analytics. We propose an innovative framework, where clustering, linguistic and emotional analytics are used to automatically assign users to relationship states. Our research is of multi-disciplinary nature, where we are using existing results from surveys on users’ behavior when mitigating states to verify the semantics of our metrics, showing that they follow similar behavior. Our results show that clustering users based on communication, emotions and perceived product mix can result in an automated assignment of users to states. Furthermore, trust, commitment and homophily are defined and our results show that users are migrating states influenced by these values. Our work provides data analytics metrics for businesses that will identify and address the problem of relationship management thus improving the overall users’ satisfaction using a data analytics approach.

ISSN

1387-3326

Publisher

Springer

Last Page

14

Disciplines

Computer Sciences

Keywords

Business data processing, Dynamic relationship marketing, Social media analytics

Scopus ID

85087304013

Indexed in Scopus

yes

Open Access

no

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