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AI agent for ux researchers

Qualitative and Analytics Triangulation Agent

Each research finding carries a clear verdict from product data before it is shared.

AnalysisQuality controlProduct developmentScience and research
See how it worksGet access
Qualitative and Analytics Triangulation Agent: what goes in, what the agent does and what you get

What it does

You report that users abandon checkout because shipping costs surprise them, and a product manager asks, show me the data. This agent builds that check for each finding. It designs a data test, such as the share of sessions leaving after the shipping step, queries analytics, and compares behavior with what participants said. It then marks the finding supported, partly supported or contradicted, with numbers and charts. If the data is inconclusive, it tries another cut, such as a segment, device or time window. You review before findings are shared. Edge case: a finding that applies to a small segment is tested on that segment, since the whole population may hide it.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueYes, continueApprovedNoNo 1 STARTS WHEN Findings ready 2 USES A TOOL Read each finding and what participants said 3 DOES Design a data test with event, segment and period 4 USES A TOOL Run the analytics query 5 DOES Compare behavior with the claim 6 CHECKS THE RESULT Is the data conclusive with enough sessions? If not: Try another segment, window or event, up tothree attempts. Back to step 3. 7 DOES Label the finding supported, partly supported orcontradicted 8 USES A TOOL Create a short evidence summary with charts 9 CHECKS THE RESULT Do the numbers in the summary match the queryresults? If not: Rerun and correct the summary. Back to step 4. 10 YOU APPROVE Researcher reviews before findings are shared 11 RESULT Triangulated findings report
Read the steps as a list
  1. Findings ready
  2. Read each finding and what participants said
  3. Design a data test with event, segment and period
  4. Run the analytics query
  5. Compare behavior with the claim
  6. Is the data conclusive with enough sessions?If not: Try another segment, window or event, up to three attempts. Back to step 3.
  7. Label the finding supported, partly supported or contradicted
  8. Create a short evidence summary with charts
  9. Do the numbers in the summary match the query results?If not: Rerun and correct the summary. Back to step 4.
  10. Researcher reviews before findings are sharedThe agent waits here for your OK.
  11. Triangulated findings report

How it decides

It labels a finding supported when the data shows the same direction with meaningful size, partly supported when only in some segments, and contradicted when the data points the other way.

  • Need at least 500 sessions for a verdict
  • Supported when direction matches and the effect is above 5 points
  • Test three cuts before calling it inconclusive
  • Report contradictions as prominently as support

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Minimum sessions (default 500)
  • Number of cuts to try
  • Effect size threshold
  • Chart style

What keeps you in control

It always asks you first

  • Researcher reviews all labels before sharing

Hard limits

  • Never change findings to match the data
  • Never share data outside the research team without approval

It stops when

  • Done: all findings labeled
  • Stop: analytics events missing for a finding

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensThe finding 'shipping cost surprises users' was tested on checkout sessions. 61% left at the shipping step, versus 34% the month before. The first cut was too thin on mobile, so it widened to 6 weeks and 2,900 sessions. The finding was labeled supported, strongest on mobile. The researcher added the chart to the report.

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