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An AI Toolkit for Telecom Operators: 90% Gender-Prediction Accuracy and a 25% Lift in Female Mobile Adoption in Six Months
Case Study Summary
Executive summary
Whizzbridge built an AI Gender Analysis Toolkit for a telecom operator working to close the mobile gender gap among its subscriber base. Built on a Python and Docker stack with Node.js, React, Flask, PySpark, and machine learning libraries including Scikit-learn, LightGBM, XGBoost, HyperOpt, and SHAP, the toolkit ingests call and transaction data, predicts subscriber gender at 90% accuracy, and surfaces underserved segments through explainable dashboards. Acting on those insights lifted female mobile adoption by 25% within six months.
90%
Gender prediction accuracy
Subscriber data
25%
Female mobile adoption lift
Post-toolkit deployment
6 months
To measurable adoption lift
Data-driven outreach
Challenges
The client struggled with a gender gap in mobile service adoption. Limited insights into gender-based usage patterns hindered their ability to create targeted services. They needed a solution to:
- Predict gender from mobile usage data.
- Identify gaps in service adoption.
- Design inclusive and targeted marketing strategies.
Close the mobile gender gap by equipping telecom operators with tools to identify and address subscriber gender disparities.
WhizzBridge’s Solution
We developed an AI Gender Analysis Toolkit tailored to the client’s needs. Key features included:
- Data Preparation:
Integrated diverse data sources like call records and transaction history.
Preprocessed data for accuracy. - Custom AI Models:
Built machine learning models for accurate gender prediction.
Incorporated model explainability for transparency. - Insights Dashboard:
Visualized gender-based usage trends.
Provided actionable recommendations to close the gender gap. - Ethical Data Practices:
Anonymized user data and ensured secure processing.
Results We Achieved
- Improved Accuracy: Achieved 90% gender prediction accuracy.
- Actionable Insights: Identified underserved segments.
- Increased Adoption: Boosted female customer adoption by 25% in six months.
- Enhanced Decision-Making: Provided intuitive dashboards for real-time analytics.
Enabled operators to design targeted services, promoting digital inclusion and reducing the gender gap in mobile connectivity.
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