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Estimate the total revenue a customer is expected to generate over their relationship with the company.
Data Points: ARPU (Average Revenue Per User), tenure, usage patterns (voice, data, SMS), MFS transaction volume, subscription to value-added services.
Why it matters: Helps prioritize high-value customers for special offers or tailored customer service.
Customer Segmentation & Profiling:

Goal: Group customers with similar behaviors or characteristics to enable targeted marketing and service development.
Data Points: Demographic data, geographic location (e.g., users in Bogra vs. Dhaka), usage habits, MFS usage, preferred channels.
Why it matters: Allows MNOs to develop specific packages (e.g., student packs, rural data plans) and MFS providers to offer relevant financial products.
Next Best Offer (NBO) / Personalized Recommendations:

Goal: Predict which product or service a customer is most saudi arabia phone number list likely to be interested in next.
Data Points: Past purchases, Browse history (if available), usage patterns, demographic info, segment.
Why it matters: Increases cross-selling and up-selling opportunities, improving customer satisfaction and revenue.
Credit Scoring (for MFS-based micro-loans):

Goal: Assess the creditworthiness of users for micro-loans or pay-later services based on their MFS transaction history and phone usage.
Data Points: MFS transaction volume and frequency, regularity of recharges, bill payment history, network tenure.
Why it matters in Bangladesh: Crucial for financial inclusion initiatives, enabling access to credit for underserved populations.
Common Machine Learning Models Applied
The choice of ML model depends on the prediction task (classification, regression, clustering) and the nature of the data.