SkuWatch AI Visibility Agent Scan your store or site

Stage 05 of the SkuWatch AI Visibility loop

Attribute later visibility changes without manufacturing causation.

Connect products, prompts, releases, source changes, referral events, and defined comparison windows while preserving uncertainty and competing explanations.

Attribution needs a change graph

SkuWatch AI Visibility connects each observation to:

  • product, variant, and market
  • changed field or Theme release
  • source evidence and approver
  • deployment and verification time
  • feed or channel event
  • stable prompt IDs
  • completed provider runs
  • source citations and factual accuracy
  • referral or commerce events when legitimately available

Without this graph, a later ChatGPT citation may be celebrated even though the relevant change was unrelated.

Use defined windows

A comparison can report:

Before:
4 canonical citations in 18 completed prompt runs.

After:
7 canonical citations in 19 completed prompt runs.

Material changes:
compatibility page published; feed title corrected; two products
returned to stock; provider model changed during the period.

The evidence supports an association during a defined test set. It does not prove that one edit caused every later recommendation.

Attribution levels

Level Defensible statement
Temporal the observation occurred after the release
Matched evidence the answer used the corrected fact or source
Controlled comparison stable prompts and comparable periods changed
Commerce link a legitimate referral or order carried channel attribution

SkuWatch AI Visibility reports the level earned. It does not replace missing evidence with a confident score.

The completed attribution record returns to Monitor and establishes the next baseline.

Continue the operating loop

A stage is useful only when its evidence survives into the next one.

SkuWatch AI Visibility carries product identity, observations, sources, decisions, approvals, and change history through the complete lifecycle.