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How Anthropic uses Claude in Product Management

Getting data as a product manager means pinging a data science team and waiting. Lisa Crofoot, Product Manager, shares how Anthropic's PMs use Claude to query product data and build evals in minutes—no SQL required. See how product managers, including the Head of Product for Claude Code at Anthropic, use Claude: www.anthropic.com/news/product-management-on-the-ai-exponential Stay tuned for more stories in the "How Anthropic uses Claude" series.

Lisa Crofootguest
Mar 26, 20262mWatch on YouTube ↗

CHAPTERS

  1. Why AI changes the pace and independence of product management

    Lisa Crofoot explains that Claude lets her iterate on product ideas faster and validate concepts before looping in others. This reduces dependency on cross-functional coordination and feels empowering for PMs.

  2. The recurring PM pain point: getting reliable product data

    She describes how accessing and analyzing data is often slow and operationally heavy for PMs. Typical paths include asking data science for help or writing SQL against unfamiliar databases.

  3. Using Claude Code for data analysis workflows

    Lisa shares how she adopted Claude Code and quickly realized it could support day-to-day data analysis. This reframes analysis from writing queries to interpreting results.

  4. Connecting BigQuery to Claude via MCP

    Anthropic’s data science team set up a BigQuery MCP to connect product data tables directly into Claude Code. This makes querying accessible without requiring SQL expertise.

  5. Demo setup: synthetic dataset for light vs. dark mode usage

    Before the demo, Lisa uses Claude to generate synthetic product data to illustrate analysis. She frames a question: the fraction of dark mode usage over the past three months.

  6. Claude produces an immediate visualization with extra insights

    Claude generates a graph quickly and adds analytical enhancements Lisa didn’t explicitly request. These include a seven-day rolling average and an overall average, plus polished presentation.

  7. Iterating on the analysis: breakdown by plan type

    Lisa asks to refine the chart to show light/dark usage by plan type. Claude requests permission to make edits and then produces the updated plot.

  8. Time savings and reduced operational drag for PMs

    She emphasizes that doing the same analysis manually could take hours or longer. Claude compresses that work into quick prompts and revisions.

  9. Generating product evals with Claude to test AI experiences

    Lisa explains another key use case: creating evals to assess AI systems and AI product behavior. By describing the scenario and providing a few examples, Claude can expand to dozens of test cases.

  10. The bigger impact: more strategy, less coordination—and expanded capability

    Lisa’s goal is for PMs to spend more time on strategy, customer conversations, and decisions instead of operational coordination. She frames Claude as more than automation: it extends what she can accomplish independently.

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