Neobanks
Customer Engagement and Retention
Analyzes transaction history, usage of bank features, and customer support interactions to predict churn. Suggests personalized retention strategies to keep customers engaged with the platform.
Objective
- Analyze transaction history, app usage, and customer interactions to predict churn.
- Design and implement personalized retention strategies to maintain user engagement.
- Identify disengaged users and re-engage them with targeted outreach.
Outcome
- Reduced churn and increased customer retention rates.
- Enhanced user engagement through personalized re-engagement campaigns.
- Improved lifetime value of customers by fostering long-term relationships.
- Higher customer satisfaction and loyalty.
Business Value
- Protect revenue by retaining high-value customers.
- Strengthen brand loyalty with proactive and personalized engagement efforts.
- Optimize marketing and support resources by focusing on at-risk users.
- Enhance competitiveness with superior customer retention performance.
Data Approaches
- Churn Prediction Models: Identify at-risk customers based on activity patterns.
- Re-Engagement Campaigns: Automate personalized outreach strategies.
- Sentiment Analysis: Use support data to refine engagement strategies.
- Real-Time Monitoring: Track user interactions for timely retention efforts.
