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USE CASE

Customer Behavior Analysis

Objective

  • 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

Outcome

  • Create behavior scoring models

  • Continuously update customer data

Business Value

  • 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

H2O's AI and Data Approaches

  • 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

 

Resources

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