International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 4
July-August 2026

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A Random Forest System for Predicting Customer Purchasing Behaviour and Price Sensitivity in the Telecommunications Sector: Empirical Evidence from a Developing Market Context

Author(s) Ms. Mercyline Ngonidzashe Dhlakama, Ms. Linda Susan Amos
Country Zimbabwe
Abstract This study involved the design, training, and evaluation of a Random Forest (RF) classifier within a cloud-based predictive architecture for predicting customer purchase behaviour and price sensitivity in the Telecommunications industry. Four engineered features to represent relational pricing and behavioural dynamics were created using the IBM Telco Customer Churn dataset (n = 7,043), and the Synthetic Minority Over-sampling Technique (SMOTE) was used to combat class imbalance. The model's cross-validation accuracy was 85.75% (0.40%), and the mean Area Under the Receiver Operating Characteristic Curve (ROC-AUC) was 93.03% (±0.45%), which surpassed the published performance of similar studies. Tenure-to-monthly charges ratio (13.19%) and contract type (10.33%) were the major behavioural determinants highlighted by feature importance analysis, with a small contribution from demographic factors. The study empirically confirms the hypothesis that multi-dimensional telecommunications data usefully increases the predictive value and offers an operator-deployable, validated framework for a cloud-based solution in developing markets. Theoretical implications concerning the Technology Acceptance Model (TAM) and Systems Theory are discussed.
Keywords Random Forest, Customer Churn Prediction, Telecommunications Analytics, Cloud Computing, Price Sensitivity, Machine Learning, SMOTE, Feature Importance, Developing Markets, Predictive Behavioural Modelling
Field Computer > Data / Information
Published In Volume 8, Issue 4, July-August 2026
Published On 2026-07-05
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E-ISSN 2582-2160
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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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