A Multi-Class Machine Learning Customer Classification For Personalised Voice Bundle Recommendations: A Case Study of Airtel Uganda Limited

Abstract

Airtel Uganda Limited relies on Average Revenue Per User (ARPU)-banded segmentation to deliver voice bundle promotions, yielding conversion rates below 3 percent and wasting an estimated 20 percent of the marketing budget on untargeted communications. The absence of a personalised, data-driven recommendation system represents both a commercial ine"ciency and an academic gap this study addresses. This study developed and validated a machine learning recommendation framework using real transactional data from 1,458,900 Airtel Uganda prepaid subscribers (February–April 2026). Four classification models — Logistic Regression, Decision Tree, Random Forest, and XGBoost — were evaluated following a preprocessing pipeline of median imputation, capped SMOTE oversampling, and behavioural feature engineering. Three statistical tests were applied to evaluate relationships between customer attributes and bundle segment: Spearman Rank Correlation to measure the strength and direction of monotonic associations between numeric features and the target; one-way ANOVA to test whether feature means di!er significantly across the eight value segment groups; and Chi-Square to assess the independence of categorical features from the target variable. Statistical tests confirmed 34 of 35 features significantly associated with bundle segment (p < 0.05); UPGRADE_RATE was the only non-significant feature. Feature importance analysis identified TOTAL_REVENUE (18.01%), SPEND_PER_MINUTE (6.57%), CALLS (5.97%), VOICE_INTENSITY (5.87%), and MOBILE_MONEY (4.64%) as the top five predictors. XGBoost achieved the highest F1-Score of 0.661 on the 8-class hold-out test set — a 5.4-fold improvement over a random baseline. A simulated pilot projected a four- to five-fold improvement in conversion rate (3% to 12–15%) and a 9 percent ARPU uplift (simulated pilot projection). Three strategic recommendations were formulated: a VOICE_INTENSITY-triggered recommendation engine, enriched segmentation incorporating device type and purchase channel, and a formal A/B test with model governance. This is the first empirically validated ML bundle recommendation framework built on real Ugandan telecom data.

Keywords: Machine Learning, Telecommunications, Voice bundle recommendation, Customer behavioural attributes, Predictive Analytics.