Abstract
The adoption of product centric approach to customer acquisition by many subscriber based companies has become a factor, which influences customer misclassification in existing churn predictive models. While the transaction volume, velocity, and varieties for basic churn processes continues to increase exponentially, every customer remained a potential churner to a certain degree. Although, existing churn prediction models classifies customers as churner or non-churner, many of its approaches assign equal weight to features while the customer’s power of influence from socio-transactional data mining are neglected in churn behaviour management. Here, the developed Churn Predictive System is a composite of Recency-Frequency-Monetary-Influence model through customer segmentation management and Fuzzy-Weighed Feature Engineering model, which trained and tested transactional records using Random Forest and Adaboost Ensemble Learning in a 5-fold cross validation protocol. This System was coupled (Customer Segmentation + Ensemble Learning) to achieve a quadrupled customer’s churn category as Churner, Potential Churner, Inertia Customer and Premium Customers. The results from the developed system juxtapose the need for a new approach to churn prediction in customer behavioural management.
Highlights
In modern business world, the risk of losing customers to another service provider is so enormous especially for subscriber based organizations
Instead of using the traditional approach to detect customer churn, machine learning models [2] have been adopted by various researchers to find patterns and relationships in large amount of data
As machine learning algorithms continue to dominate the churn prediction space, customer segmentation approach like Recency-Frequency-Monetary model have been used optimised for churn prediction [14] towards effective customer relationship management
Summary
The risk of losing customers to another service provider is so enormous especially for subscriber based organizations. The behavioural data through these methods are represented as attributes or features that are manipulated to define a customer and their churn prediction category [3]. While existing Recency-Frequency-Monetary model through customer segmentation neglects the customer’s power of influence in community based churn prediction, existing methods in churn prediction via machine learning assign equal weights to features. This processes as increasing led to International Journal on Data Science and Technology 2020; 6(2): 56-59 churn misclassification, and less effective targeted decision support in acquiring new customers or retaining existing once. Sample experiments and evaluations are documented in section four (4) while the research is concluded in section five (5)
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