Customer Behavior Analysis

Segment users on the basis of different attributes such as - demographics data, transaction history, transaction behavior (Recency, Frequency, Monetary)

Rapidly identify and evaluate customer behavior signals to take action on potential fraudulent activity, optimize marketing activities, etc

Create behavior scoring models

Continuously update customer data

Create foundation for

• Personalization strategies to improve customer experience, customer sentiment

• Targeting different groups in a tailored approach

• Recommending core banking products for cross-selling and up-selling, increase sales, revenues, and profits

• Deploying customer churn prevention schemes

Provide an end-to-end pipeline that collects and updates the customer data, performs data preparation, performs unsupervised machine learning, and generates clustering results

H2O Wave App with the following components:

• Data Preparation

• Unsupervised Machine Learning

• Perform rule fit analysis to identify cluster rules

• Also, perform additional analysis such as association rules mining to identify prominent rule patterns

• Clustering Dashboard to display insights

• Tailored schemes of users

• Interactive Component: application users can select a group (or a customer) - get their insights

• Graph Neural Network

• What if the component, let users define their business metrics equation

• Trigger retraining options