Open Access

ARTICLE

Social Opinion Network Analytics in Community Based Customer Churn Prediction

by Ayodeji O. J Ibitoye^1,*, Olufade F. W Onifade²

1 Country bowen University, Iwo, +234, Nigeria
2 University of Ibadan, Ibadan, +234, Nigeria

* Corresponding Author: Ayodeji O. J Ibitoye. Email:

Journal on Big Data 2022, 4(2), 87-95. https://doi.org/10.32604/jbd.2022.024533

Received 23 October 2021; Accepted 27 June 2022; Issue published 31 October 2022

Abstract

Community based churn prediction, or the assignment of recognising the influence of a customer’s community in churn prediction has become an important concern for firms in many different industries. While churn prediction until recent times have focused only on transactional dataset (targeted approach), the untargeted approach through product advisement, digital marketing and expressions in customer’s opinion on the social media like Twitter, have not been fully harnessed. Although this data source has become an important influencing factor with lasting impact on churn management. Since Social Network Analysis (SNA) has become a blended approach for churn prediction and management in modern era, customers residing online predominantly and collectively decide and determines the momentum of churn prediction, retention and decision support. In existing SNA approaches, customers are classified as churner or non-churner (1 or 0). Oftentimes, the customer’s opinion is also neglected and the network structure of community members are not exploited. Consequently, the pattern and influential abilities of customers’ opinion on relative members of the community are not analysed. Thus, the research developed a Churn Service Information Graph (CSIG) to define a quadruple churn category (churner, potential churner, inertia customer, premium customer) for non-opinionated customers via the power of relative affinity around opinionated customers on a direct node to node SNA. The essence is to use data mining technique to investigate the patterns of opinion between people in a network or group. Consequently, every member of the online social network community is dynamically classified into a churn category for an improved targeted customer acquisition, retention and/or decision supports in churn management.

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Keywords

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