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Claude - How Anthropic uses Claude in Product Management

Thumbnail of the video “Claude - How Anthropic uses Claude in Product Management”
Community upload. AI output has not been independently verified.
Author: Claude
Categories: Technology Science Business
Tags: technology, science, business
Views: 5 · Last viewed: 2026-10-15 19:00 UTC

Short Summary

Main recommendation: Product managers should use Claude (including Claude Code connected to company data via a BigQuery MCP) to explore product data, build visualizations, and generate evaluation test cases themselves instead of waiting on data science teams. The speaker says this lets them iterate faster, work more independently, and spend more time on strategy and customer conversations.

Key Points

  • Claude Code can be connected to BigQuery product data tables through an MCP set up by the data science team, so the user does not need to write SQL and only has to interpret the results.
  • In the demo, a request for dark mode usage over the past three months produced a graph with a 7-day rolling average and an overall average, which the speaker says was nicer than what they would have built manually.
  • Follow-up requests, such as splitting light and dark mode usage by plan type, were handled by asking Claude for permission to edit and then regenerating the plot.
  • Claude is used to generate evals (tests for evaluating AI systems and products): given a situation, a product experience, and a couple of example test cases, it can expand the set to around 50 cases.
  • The speaker frames the benefit as extending what they can accomplish alone, not just automating existing tasks.

Details Worth Noting

  • Concrete instructions:
    • Connect Claude Code to your product data by having your data science team set up a BigQuery MCP that links the BigQuery product data tables to Claude Code.
    • Ask for data in plain language, naming the data table to search and the time window, for example "the fraction of dark mode usage over the past three months."
    • Refine the output by asking for a new breakdown, such as plotting by plan type, and approve the edits Claude requests.
    • For evals, give Claude the situation, the product experience you want to build, and one or two example test cases, then ask it to expand the option space of test cases.
  • The speaker says the synthetic dark mode vs. light mode data used in the demo was generated with Claude beforehand.
  • The speaker estimates that doing the analysis manually could take hours or more.
  • The workflow is described as a way to avoid coordination and operations work so more time goes to product strategy, customer conversations, and decision-making.

Bottom Line

Claude lets product managers do data analysis and eval design that previously required a data scientist or hours of manual work, though the speaker's claims about speed and quality come from a single demo.