How do I build a cross-channel attribution model that accurately credits both content marketing and paid search for pipeline generation?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do I build a cross-channel attribution model that accurately credits both content marketing and paid search for pipeline generation?
1 real search · 1 real-search pass
B2B marketing attribution multi-touch pipeline incrementality measurement content marketing paid search Markov model guidelinesGoogle ↗
OBSERVEDReason
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 (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
B2B marketing attribution multi-touch pipeline incrementality measurement content marketing paid search Markov model guidelines Google ↗
- Methods & Models: A Guide to Multi-Touch Attribution · nielsen.com
- About attribution modeling · support.google.com
- The Definitive · business.adobe.com
- Full-funnel MMM: unifying upper-funnel and lower-funnel measurement | Meridian | Google for Developers · developers.google.com
- IAB - Guidelines for Incremental Measurement in Commerce Media · iab.com
- Mastering-Marketing-Measurement-Slideshow-June-2025 · web-assets.bcg.com