Data Preparation and Feature Engineerin
Posted: Tue May 20, 2025 9:41 am
Regardless of the model, the quality and relevance of features derived from mobile number data are paramount.Usage Patterns: Average daily call duration, number of unique contacts called, SMS sent/received count, data consumption (2G/3G/4G/5G), time of day/week usage peaks.
Recharge Behavior: Average recharge amount, frequency, last recharge date, consistency of recharges.
MFS Transactions: Transaction count, average value, types of transactions (cash-in/out, P2P, bill payments), frequency, geographical patterns.
Customer Service Interactions: Number of complaints, type of query, resolution time.
Demographics: Age, gender, location (e.g., Bogra city vs. rural areas), income group (inferred).
Network Features: Number of unique cell towers connected to, roaming activity.
Engineered Features: Ratios (e.g., calls to unique numbers vs. total calls), velocity features (e.g., sudden increase in data usage), recency (time since last activity), frequency.
Important Considerations
Data Privacy (Ethical & Legal): Given the sensitivity of mobile number data and recent data leaks in Bangladesh, privacy is paramount.
Anonymization/Pseudonymization: Sensitive identifiers must be masked.
Consent: Ensure users have explicitly consented to their data being taiwan phone number list used for predictive modeling.
Federated Learning: Explore FL for on-device model training, especially for personalized features, to keep raw data decentralized.
Data Quality & Imbalance: Mobile data can be messy. Cleaning, handling missing values, and addressing class imbalance (e.g., fraud cases are rare) are crucial.
Model Interpretability: Especially for fraud or credit scoring, understanding why a model made a certain prediction is vital for trust and actionability.
Real-time vs. Batch Prediction: Some predictions (fraud detection, NBO) require near real-time inference, while others (churn, CLTV) can be run in batches.
Compute Resources: Training complex ML models on massive mobile datasets requires significant computational power.
By carefully selecting and applying appropriate ML models, combined with robust data engineering and a strong commitment to privacy, MNOs and MFS providers in Bangladesh can unlock immense value from their mobile number data to enhance customer experience, boost revenue, and secure their operations.
Recharge Behavior: Average recharge amount, frequency, last recharge date, consistency of recharges.
MFS Transactions: Transaction count, average value, types of transactions (cash-in/out, P2P, bill payments), frequency, geographical patterns.
Customer Service Interactions: Number of complaints, type of query, resolution time.
Demographics: Age, gender, location (e.g., Bogra city vs. rural areas), income group (inferred).
Network Features: Number of unique cell towers connected to, roaming activity.
Engineered Features: Ratios (e.g., calls to unique numbers vs. total calls), velocity features (e.g., sudden increase in data usage), recency (time since last activity), frequency.
Important Considerations
Data Privacy (Ethical & Legal): Given the sensitivity of mobile number data and recent data leaks in Bangladesh, privacy is paramount.
Anonymization/Pseudonymization: Sensitive identifiers must be masked.
Consent: Ensure users have explicitly consented to their data being taiwan phone number list used for predictive modeling.
Federated Learning: Explore FL for on-device model training, especially for personalized features, to keep raw data decentralized.
Data Quality & Imbalance: Mobile data can be messy. Cleaning, handling missing values, and addressing class imbalance (e.g., fraud cases are rare) are crucial.
Model Interpretability: Especially for fraud or credit scoring, understanding why a model made a certain prediction is vital for trust and actionability.
Real-time vs. Batch Prediction: Some predictions (fraud detection, NBO) require near real-time inference, while others (churn, CLTV) can be run in batches.
Compute Resources: Training complex ML models on massive mobile datasets requires significant computational power.
By carefully selecting and applying appropriate ML models, combined with robust data engineering and a strong commitment to privacy, MNOs and MFS providers in Bangladesh can unlock immense value from their mobile number data to enhance customer experience, boost revenue, and secure their operations.