implementation

Churn Analysis Strategy

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Linked challenge: Enterprise Churn Analyst with LlamaIndex and Claude Opus 4.6.6

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Linked challenge

Enterprise Churn Analyst with LlamaIndex and Claude Opus 4.6.6

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Original prompt text with formatting preserved for inspection.

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Prompt the agent to analyze the decline in Business Insider subscriptions from 185k to 135k. Ask it to use Exa to find current content trends and suggest a pricing strategy inspired by Netflix's 2025 hourly revenue model.

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This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Enterprise Churn Analyst with LlamaIndex and Claude Opus 4.6.6

Based on news of declining paid subscriptions at Business Insider and pricing adjustments at Netflix, this challenge tasks you with building an agentic Churn Analysis platform. You will use LlamaIndex to create a multi-agent system that searches current market trends using Exa, retrieves historical subscriber behavior stored in Zep long-term memory, and predicts churn risks for specific segments. Claude Opus 4.6.6 will serve as the primary cognitive engine to synthesize web search data and memory to recommend specific price-retention strategies. You will monitor the agents reasoning using Weights and Biases to ensure the logic behind pricing recommendations is sound and explainable.

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