Bridging the Gender Gap with AI-Driven Customer Insights
Industries
Telecommunications
Services
AI Development, Data Analytics
Tools We used
Docker, Node.js, React, Python, Celery, Redis, Flask, PySpark, Pandas, Scikit-learn, LightGBM, XGBoost, HyperOpt, SHAP
Challenges We Faced
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.
