Predict Telco Churn with h2oGPTe Agents
- Agentic AI
- Enterprise h2oGPTe
- H2O Driverless AI
This lesson walks through a hands-on demo of an h2oGPTe agent built for telecom customer retention analysis, combining churn, service, and financial data with Python coding, H2O Driverless AI, and advanced reasoning tools. It covers writing a prompt that gives the agent an analyst persona, sets the churn target variable, and lists the required analysis steps and outputs. It shows the agent building a Driverless AI model and returning charts on churn probability, variable importance, and customer lifetime value. It closes with retention recommendations and an implementation roadmap generated from the model results.
Key takeaways
- How a prompt can give an h2oGPTe agent an analyst persona along with a defined target variable and output list
- Why H2O Driverless AI gets called from within the agent workflow to build the actual churn model
- What the agent's output includes, from churn probability charts to variable importance and lifetime value
- The agent turns model results into a retention roadmap with specific recommendations for reducing churn
- How downloadable files let you inspect the exact code and data behind each generated chart
Start owning your AI