Solution Description
Customer Risk Scoring
- Use machine learning models to analyze historical churn data and assign churn risk scores to individual customers.
- Factors include transaction patterns, service usage, complaints, sentiment analysis, and life events.
Behavioral Segmentation
- Segment customers based on behavior, demographics, and financial journeys.
- Identify micro-segments like high-net-worth individuals, digital-only users, or dormant account holders.
Predictive Triggers
- Detect early warning signs such as reduced logins, fewer transactions, or negative sentiment in support interactions.
- Use real-time data to trigger alerts for proactive engagement.
Personalized Retention Strategies
- Deliver targeted offers, loyalty rewards, or financial advice tailored to the customer’s profile and preferences.
- Use preferred communication channels (e.g., mobile app, email, chatbot).
AI-Powered Engagement
- Deploy conversational AI agents to engage at-risk customers with empathy and relevance.
- Offer solutions like fee waivers, product upgrades, or financial planning tools.
Continuous Learning
- Continuously retrain models with new data to adapt to changing customer behavior and market conditions.