A social CRM analytic framework for improving customer retention, acquisition, and conversion

Abstract

• Proposes a social CRM analytic framework for improving customer retention, acquisition, and conversion. • The framework uses topic modeling, sentiment analysis, and Fuzzy-Kano to extract knowledge from social media data. • Identifies customers’ classes and categories using Random Forest and K -means. • Optimization techniques are used to improve the decision-making process. Social Customer Relationship Management (social CRM) has become one of the central points for many companies seeking to improve their customer experience. It comprises a set of processes that allows decision-makers to analyze customer data, in order to launch an efficient, customer-centric, and cost-effective marketing strategy. Nonetheless, the inclusion of social media data in CRM introduces new challenges, as it requires advanced analytical approaches to extract actionable insight from such a huge amount of data. Thus, in this paper, we propose a social CRM analytic framework, which includes various analytical approaches, aiming at improving customer retention, acquisition, and conversion. This framework has been tested on various datasets and extensively evaluated based on several performance metrics. The obtained results suggest that the proposed framework can effectively extract relevant information and support decision-making processes. From an academics perspective, the study contributes to an understanding of customers’ experiences throughout their engagement on social media and focuses on long-term relationships with customers. From a managerial perspective, companies should leverage the insight generated through every customer engagement on social media to drive effective marketing strategies.