A Comparative Analysis of Use of Machine Learning Algorithms for Customer Churn Prediction at Supermarkets in Lagos Nigeria
Abstract:
The
retail industry needs customer churn prediction to create successful customer
retention strategies. The identification of churn customers in Nigerian
supermarkets relies on conventional manual survey methods which take up time
and generate limited predictive results. The research investigated customer
behavior through transaction data analysis to predict churn while identifying
the most effective supervised machine learning models for customer churn risk
assessment. The study used transaction data from multiple supermarkets based in
Lagos, Nigerian. Retrospective data from Plenty Africa, a digital loyalty
platform, was used for this comparative analysis. The research employed
predictive modeling methods to study customer characteristics and shopping
patterns which helped detect patterns that lead to customer loss. The model
performance was evaluated through conventional assessment metrics which led to
a comparison for determining which approach performed better. The research showed
that machine learning methods succeed in predicting customer departure through
supermarket data collection which enables businesses to develop effective
customer retention strategies. The research also shows how supermarkets can enhance
their operational performance by using data-driven churn prediction systems
which generate better results than human-based methods in retail operations.
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