Business Situation
- A leading retail bank aimed to enhance its customer engagement and increase loan uptake. It wanted to implement a personalization strategy based on predictive analytics.
- It sought to identify customers with a high propensity to take out loans and tailor marketing efforts accordingly.
SGA Approach
Technology
- Daily summary reports, workflow monitoring tools, and automated summaries helped optimize performance and track progress.
- Periodic reports and automated workflows reduced human intervention and further optimized data extraction processes.
AI
- AI/ML models were trained to improve extraction accuracy, boosting performance from 55% to 80%.
- SGA partnered with the client to create a unified data extraction strategy, reducing human effort and enabling system-wide automation.
Data
- Extraction rates, processing times, and project turnaround times were tracked to ensure continuous improvement.
- Models were trained on diverse document types and exceptions, improving extraction accuracy from 55% to 80% over time and reducing manual intervention.
Key Takeaways
- Enhanced Customer Engagement: Personalized marketing campaigns led to a 30% higher engagement rate compared to generic campaigns
- Increased Loan Uptake: The predictive analytics model resulted in a 25% increase in loan applications from high-propensity customers
- Operational Efficiency: Automation reduced the time and effort required to identify potential loan customers, allowing staff to focus on strategic tasks
- Effective Use of Technology: Cloud-based infrastructure and AI tools enabled real-time data processing and personalized customer interactions
- Data-Driven Decision-Making: Comprehensive data collection, cleaning, and feature engineering improved the accuracy of the predictive model
- AI-Driven Personalization: ML algorithms and customer segmentation ensured targeted and relevant marketing efforts
