Testing a Pricing Hypothesis on Churn with h2oGPTe
- Agentic AI
- Enterprise h2oGPTe
This lesson shows using a collection in Enterprise h2oGPTe for collaborative, hypothesis driven analysis of customer churn, shared with specific users or groups. After a high level overview of the dataset, the lesson asks the agent a pricing hypothesis: what happens to churn, revenue, and retention if day minute pricing drops 10 percent. In under a minute, the agent returns current revenue, a churn distribution by charge level, and a business impact summary showing a tradeoff between an estimated ten thousand dollar loss and retaining price sensitive customers. It closes by opening the agentic analysis view to see each step the agent took.
Key takeaways
- How sharing a collection with specific users or groups lets a team collaborate on the same churn analysis chat
- What a single pricing hypothesis question returned in under a minute: current revenue, a churn distribution, and a business impact estimate
- Why the highest charge customers showed the worst churn rate, making them the group most likely to be saved by a price cut
- How opening the agentic analysis view shows each step the agent took and the code it ran to reach its answer
- How a first high level hypothesis test opens the door to testing further variations, such as a 7 or 12 percent price cut
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