Measurement
Part of What a keyword research measurement plan means for UK content teams
Why keyword research attribution methods decide your reporting
Compare first-touch, last-touch and data-driven keyword research attribution methods, with a before and after table and the evidence each model gives you.
What to take away
- Attribution decides which keyword gets credit for a conversion, so the method you pick changes the content you fund next quarter.
- Last-touch flatters bottom-of-funnel terms; first-touch flatters discovery terms; data-driven models split credit across the whole path.
- Google Analytics 4 moved to data-driven attribution as the default for most conversions, which means many teams are already reporting on a model they did not choose.
- Match the model to the decisionuse first-touch to defend discovery content, last-touch to defend conversion pages, and data-driven when you need a neutral split.
Why two models score the same keyword differently
Every keyword research attribution method answers a different question. A discovery guide and a commercial page rarely earn credit for the same sale, and the gap between their last-touch and first-touch scores is where most reporting arguments start.
First-touch and last-touch in practice
First-touch credit rewards the broad, early-stage terms that introduce a brand. If your team is arguing for more discovery content, this model supports that case because it records the entry query rather than the closing one.
Last-touch credit rewards the specific, commercial terms that appear immediately before a conversion. It is the easiest model to explain to a finance team, and the easiest to game by bidding on brand terms.
A practical middle route is to run both and report the difference. When a discovery keyword looks weak on last-touch but strong on first-touch, that gap is the story, not a data error.
Data-driven and position-based models
Data-driven attribution uses observed conversion paths to allocate credit, rather than a fixed rule. Google Analytics 4 applies it as the default for most conversion events, so check Admin > Attribution settings in GA4 before you claim you are reporting on last-touch.
That page also sets the lookback window, which defaults to 30 days for acquisition conversions and 90 days for other conversion events. Where an event has too few conversions for the model to learn from, report a rule-based model instead.
Position-based models give a set share to the first and last interaction and spread the rest across the middle. They suit longer research cycles where several searches contribute before a decision. Our keyword research key metrics: data and sources piece sets out which numbers to feed into any of these models.
How to compare two models on your own conversions
Run the comparison on your own data rather than on a vendor's example. Export the same conversion set under two models and put the keyword-level totals side by side.
Keyword credit by attribution model
Last-touch credit
- Discovery term
- 12%
- Mid-funnel term
- 18%
- Commercial term
- 58%
- Brand term
- 12%
Data-driven credit
- Discovery term
- 31%
- Mid-funnel term
- 27%
- Commercial term
- 29%
- Brand term
- 13%
A worked example in GA4
Open the Model comparison report under Advertising in GA4 and select the conversion event you report on, for example purchase. Keep the date range fixed, say the last 28 days.
Set the first model to Last-touch and the second to Data-driven. Choose the closest keyword grain the report offers, such as Session source/medium for organic search or Google Ads keyword text for paid. Then use the export control to download both tables as CSV and join them on that column.
| Step | Where in GA4 | What you get |
|---|---|---|
| 1 | Model comparison report, Advertising | The conversion event scored by two models |
| 2 | Model selectors | Last-touch credit and data-driven credit side by side |
| 3 | Dimension selector: Session source/medium or Google Ads keyword text | Rows at keyword grain rather than account totals |
| 4 | Export control, top right | Two CSV files you can join on the keyword column |
Read the exports as a funding signal. A model that gives most of the credit to closing terms will keep paying for closing pages, even when the discovery terms are doing the introducing.
Choosing a model for your reporting cycle
Pick the model that matches the decision on the table. If the question is whether to keep funding early-stage guides, first-touch or data-driven will answer it. If the question is which commercial page to optimise, last-touch is closer to the truth.
Whichever you choose, state it in the report header. A monthly report that silently switches models is worse than one that uses a crude model consistently. The keyword research: measurement and reporting guide 2027 covers how to document that choice across a reporting year.
What to check before you trust a model
Attribution only works if the underlying capture is sound. Consent, cookie behaviour and cross-device gaps all distort the path before any model touches it.
Tracking, consent and rank data
UK organisations handling analytics data must follow the ICO's online tracking guidance when deciding what to collect and how to record consent. Missing consent data creates gaps that no attribution model can repair.
Rank data sits alongside conversion data in the same report. Tools such as Semrush rank tracking record position changes over time, which helps you separate a ranking shift from an attribution shift when a keyword's credited conversions move.
Reporting when search results include AI answers
Search results increasingly include AI-generated answers, so some journeys never reach your site. Tools such as HubSpot AEO are built to show how a brand appears in AI search, which gives a second signal when click-based attribution looks thin.
A cheap check is to run your top three commercial queries in Google each month and note whether an AI Overview appears and whether your domain is cited.
Treat that as a coverage check, not a replacement for attribution. If your brand appears in AI answers but your click data shows nothing, the gap belongs in the report as a limitation.
Common questions
Which attribution model should a small content team start with?
Start with data-driven if your analytics platform offers it as a default, because it requires no manual weighting. If you need a simpler story for stakeholders, report first-touch and last-touch side by side.
Does attribution change the keyword research itself?
It changes which keywords you prioritise, not how you find them. A discovery term that earns credit under first-touch may deserve a brief it would never get under a last-touch report.
How often should we review the model?
Review it when the business question changes, such as moving from growth to retention.
What if two models disagree on the same keyword?
Treat the disagreement as useful evidence. Report both figures and explain the difference, then let the funding decision follow the model that matches the question being asked.
