With my limited evening time, I am researching cross channel marketing attribution models to explain to my spouse how our holiday ad budget actually drove those offline store visits.
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
With my limited evening time, I am researching cross channel marketing attribution models to explain to my spouse how our holiday ad budget actually drove those offline store visits.
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
cross-channel marketing attribution offline store visits holiday advertising attribution models measurement incrementality guide Google ↗
offline store visit conversions measurement attribution ads privacy limitations geo experiments marketing Google ↗
- About store visit conversions · support.google.com
- About Google Ads store sales measurement · support.google.com
- Foursquare attribution | Complete guide · foursquare.com
- TwG_CMO_CFO_Media_Effectiveness_Guide · thinkwithgoogle.com
- IAB/MRC Retail Media Measurement Guidelines · iab.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
cross-channel marketing attribution offline store visits holiday advertising attribution models measurement incrementality guide OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
offline store visit conversions measurement attribution ads privacy limitations geo experiments marketing OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |