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USE CASE

Propensity to Buy

Objective

  • Expand offers to customers with accuracy and speed by implementing predictive analytics

  • Forecast behaviors of a target audience by analyzing their past behaviors

Outcome

  • Cut time to build models from 6 months to less than a week

  • Doubled customer propensity to buy rate

  • Realized additional revenue by being better able to target offers

  • Created propensity models that helped companies identify the right customers and prospects that have a high likelihood to purchase a particular product or service

Business Value

  • Optimize promotions based on knowing which leads to sell to with the right message and product/services

H2O's AI and Data Approaches

  • Create models and use custom recipes that generate features/variables that provide a probabilistic estimation of whether customers will perform any of such actions or not i.e. a propensity score.

  • Use propensity  scores to estimate value each customer brings in real-time. Make data available to Relationship Managers of the Bank (RMOs)  who can further slice and dice the data and consume the information intuitively.

  • H2O AI Apps, built with SDK for Data Scientists, can perform: 

    • Data Preparation

    • Supervised Machine Learning

      • Automatic feature engineering, model tuning and optimization, scoring pipeline generation

      • Accurate time series capabilities

      • Automatically generated visualizations and data plots

    • Nonlinear algorithmic modeling

    • Binary classification

    • Results viewed on Dashboards in a tabular format, grouped by different features (city, areacode, spending limit, etc) or for a single user; view target groups and selected features such as textual insights explaining the prediction, the propensity score for each user (mean or median) if a target group is selected.

Resources

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