Behavioral Signal Audit (QA)

Behavioral Signal Audit (QA)

1. 01

   Inventory every research tool feeding client decisions

   List each platform in the stack, from conversational forms to behavioral analytics, and note what data type each captures: self-reported, observational, or both.

2. 02

   Classify each data source as self-reported or behavioral

   Self-reported data comes from surveys and forms where users state preferences; behavioral data tracks actual clicks, scrolls, and session paths. Mark each tool accordingly.

3. 03

   Flag decisions relying solely on self-reported inputs

   Identify any client recommendation that rests only on survey answers or form responses without observational backing. These are the highest-risk claims.

4. 04

   Cross-check at least one self-reported insight against behavioral data

   Pick a key finding, such as a top feature request, and verify it against session recordings or heatmaps to see if user actions align with stated preferences.

5. 05

   Document citation gaps in AI-generated client summaries

   Run client-relevant queries through AI platforms and note whether the client appears in answers. A study of 107 million AI answers shows citation gaps make clients invisible at decision moments.

6. 06

   Review data freshness and update frequency for each source

   Check when each dataset was last refreshed. Stale survey data or outdated behavioral recordings can mislead strategy, especially when client markets shift quickly.

7. 07

   Produce a confidence score for each major client recommendation

   Rate each recommendation on a scale from 'validated' (backed by both self-reported and behavioral evidence) to 'unverified' (single-source or stale data), and share this with the client.