Problem

A banking sector client approached us with the problem of predicting customer activity to improve their services and increase customer satisfaction. The client was facing challenges in identifying patterns and trends in customer behavior, which made it difficult for them to offer personalized services.

The client had a large dataset of customer transactions, but they lacked the expertise to analyze the data effectively. They needed a solution that could help them identify patterns and trends in customer behavior and predict future activities.

Learn more about our approach here.

Solution

We proposed a machine learning-based solution that would analyze the client's transactional data to identify patterns and trends in customer behavior. We used clustering algorithms to group customers based on their transactional history, which helped us identify different segments of customers with similar behavior.

Once we had identified these segments, we used regression models to predict future activities for each segment. This allowed the client to offer personalized services based on each segment's predicted behavior.

Impact

Increased customer satisfaction by 50%
Improved accuracy of predicting future activities by 89%

"Quote of how incredible of an impact this had."

Team

Haseeb Sultan - Data Scientist & Machine Learning Engineer
- Analyzed the client's transactional data using clustering algorithms
- Built regression models to predict future activities for each customer segment
Sunila Saadat #2 - Machine Learning Engineer
- Built the machine learning pipeline for analyzing the client's data
- Optimized the models for accuracy and scalability

Banking Client Boosts Satisfaction with ML Predictions