The first step is collecting and aggregating diverse data that can be associated with a unique mobile phone number. This includes:
Communication patterns: Call frequency, duration, time of day, SMS activity, and contacts.
Data usage: Volume, type of content accessed (e.g., streaming, Browse, gaming), and app usage.
Location data: (with user consent) frequented places, movement patterns.
Transaction history: Purchases, subscription changes, payment patterns.
Customer service interactions: Queries, complaints, feedback. The mobile phone number acts as the primary key to unify these disparate datasets into a holistic user profile.
Applying Machine Learning Models
Once data is aggregated, machine learning models are applied to identify patterns and predict future behavior.
Classification models (e.g., Logistic Regression, Random Forest) can predict uruguay phone number list binary outcomes like "likely to churn" or "likely to convert."
Regression models can forecast quantitative measures like future data consumption or spending.
Clustering algorithms can segment users into groups with similar behaviors, allowing for targeted predictions. These models learn from historical data, identifying correlations between past actions (linked to the mobile number) and future outcomes, enabling predictions on a large scale.
Actionable Insights and Proactive Engagement
The ultimate goal of prediction is to generate actionable insights that drive proactive engagement. For instance, if a model predicts a user is likely to churn based on a sudden drop in usage or a change in communication patterns, a tailored retention offer can be sent to their mobile number. If it predicts a user is likely to upgrade, a personalized promotion for a higher-tier plan can be initiated. This allows businesses to anticipate needs and intervene strategically, optimizing marketing efforts and improving customer satisfaction through highly relevant and timely interactions.