Marketing Attribution Models

Why attribution is a modeling choice, not a measurement

A customer rarely converts off a single touchpoint. They might see a display ad, click a Google search ad two weeks later, read a blog post from organic search, click an email link, and finally convert after typing the brand name directly. Every one of those five touchpoints contributed — but credit for the conversion has to be assigned somehow, and there is no objectively correct way to split it. Attribution models are different assumptions about how credit should flow across a multi-touch journey, and switching models can change which channel looks like your best performer without any change in actual customer behavior.

Single-touch models: first-touch and last-touch

The two simplest models each assign 100% of conversion credit to one touchpoint:

- First-touch attribution credits the very first interaction that brought the user into your ecosystem. It favors top-of-funnel channels (organic search, display, social) and answers "what discovers customers for us?"
- Last-touch (last-click) attribution credits the final interaction before conversion. It favors bottom-of-funnel channels (branded search, retargeting, direct) and answers "what closes customers for us?"

Both are easy to compute and remain the default in most ad platforms' self-reported conversion numbers, which is precisely why platform-reported numbers from Google Ads, Meta, and GA4 rarely agree with each other — each platform's last-click model only sees its own touchpoints, so every platform tends to over-credit itself.

Platform numbers will never sum to 100% of your revenue

If Google Ads, Meta Ads, and email all report last-click-attributed conversions independently, and you add up all three platforms' claimed conversions, the total will exceed actual total conversions — because a single converting journey that touched all three channels gets counted as a full conversion by whichever platform happened to be last, and often gets partially double-counted across platforms that each see themselves as the closing touch. This is normal and expected, not a tracking bug to chase down.

Multi-touch models: linear, time-decay, and position-based

Multi-touch attribution (MTA) splits credit across every touchpoint in the journey using a defined weighting rule:

- Linear — equal credit to every touchpoint. Simple, avoids ignoring the middle of the funnel, but treats a brand's first blog visit as equally valuable as the final checkout click, which is rarely true.
- Time-decay — more credit to touchpoints closer to conversion, less to earlier ones, typically using an exponential decay curve. Useful for longer sales cycles where recency genuinely correlates with influence.
- Position-based (U-shaped) — a fixed split, commonly 40% to first touch, 40% to last touch, 20% distributed across the middle touchpoints. Explicitly values both discovery and closing while still acknowledging the middle of the journey happened.

Data-driven attribution (DDA) is different in kind, not just degree: rather than applying a fixed rule, it uses a statistical model (Google's implementation uses a Shapley-value-style algorithm) trained on your own converting and non-converting paths to assign credit based on each touchpoint's actual, empirically observed incremental contribution. It requires enough conversion volume to train reliably — Google's own guidance is a practical minimum around 300+ conversions per 30-day window per channel or campaign grouping for the model to be statistically meaningful.

Attribution model comparison

Model	Credit distribution	Best for	Weakness
First-touch	100% to first interaction	Measuring discovery / awareness channels	Ignores everything that closed the deal
Last-touch	100% to final interaction	Simple, matches ad platform defaults	Over-credits bottom-funnel/branded channels
Linear	Equal split across all touches	Long consideration cycles, no bias	Treats trivial and pivotal touches the same
Time-decay	More weight to recent touches	Shorter urgency-driven sales cycles	Undervalues early discovery channels
Position-based (U-shaped)	40/20/40 first–middle–last	Balancing discovery and closing	Arbitrary fixed weights, ignores actual behavior
Data-driven (DDA)	Statistically modeled per touchpoint	High-volume accounts with rich conversion data	Needs conversion volume; a black box, hard to audit

Why GA4's default model differs from what platforms report

GA4 defaults to data-driven attribution at the property level for reporting, and it evaluates the full cross-channel journey as recorded in GA4's own data — not just the touchpoints a single ad platform can see. Google Ads, by contrast, reports conversions using its own last-click-within-Google-Ads model by default (or its own DDA scoped only to Google Ads touchpoints), meaning it literally cannot see or credit a Meta ad or organic search touch that happened earlier in the same journey.

This is the single most common source of "why don't my numbers match" confusion between GA4 and Google Ads: they aren't measuring the same thing. GA4 sees the whole recorded cross-channel journey (subject to its own tracking gaps — no cross-device stitching without login IDs, consent-mode data loss, etc.); Google Ads only sees its own slice and assumes it deserves the credit for closing.

conversion_path_length.sql

sql

-- Approximate multi-touch journey length from GA4 BigQuery export
-- (requires a stitched user_pseudo_id / user_id across sessions)
SELECT
  user_pseudo_id,
  COUNT(DISTINCT
    (SELECT value.string_value FROM UNNEST(event_params)
     WHERE key = 'session_traffic_source_last_click.manual_source')
  ) AS distinct_traffic_sources,
  COUNTIF(event_name = 'session_start') AS total_sessions
FROM `project.analytics_123456789.events_*`
WHERE _TABLE_SUFFIX BETWEEN '20260101' AND '20260131'
GROUP BY user_pseudo_id
HAVING total_sessions > 1
ORDER BY distinct_traffic_sources DESC
LIMIT 100

No model is 'correct' — pick one for a decision, not for truth

The practical resolution isn't finding the one true model; it's choosing the model that matches the decision you're making. Use first-touch to evaluate top-of-funnel/awareness spend, last-touch or DDA to evaluate close-focused retargeting spend, and always cross-check platform-reported numbers against a single source of truth (GA4 or a CDP) before reallocating budget based on any one platform's self-reported conversions.

What's next

Attribution models explain how credit flows across channels once a journey happens at all, but they still measure only what tracking can see — cookie and pixel-based multi-touch data. When privacy restrictions erode that visibility, marketing mix modeling becomes the complementary (or alternative) measurement approach.

Next: Marketing Mix Modeling (MMM) →