Marketing attribution, plain English
Contents:
What attribution actually is
Marketing attribution is the rule you use to decide which channel "gets credit" for a conversion. That's it. It is not a measurement of cause — it is a bookkeeping convention, and teams that confuse the two end up arguing about budget reallocations the data was never qualified to justify. The model you pick directly changes which channel looks profitable, which means it changes where money flows next quarter.
A growth marketer at Stripe runs a YouTube brand campaign, a paid search campaign on Google, and a retargeting flow through Meta. A prospect sees the YouTube ad Monday, searches the brand Wednesday, sees a retargeted Instagram ad Friday, and converts Saturday after clicking an email. Four touches, one sale. Whoever owns the model owns the story.
Attribution is the floor, not the ceiling — it tells you how to allocate credit for sales that already happened, not how to forecast what would happen if you turned a channel off. Hold that distinction tightly; most of the bad decisions downstream of an attribution dashboard come from forgetting it.
A realistic user path
Real journeys are almost never linear. Here's a typical multi-touch path for a SaaS signup or a $200 consumer purchase:
Day 1: saw a YouTube pre-roll (Google Ads — Display)
Day 3: clicked a paid search result (Google Ads — Search)
Day 5: came back via bookmark (Direct)
Day 7: opened a lifecycle email and clicked (Email)
Day 7: convertedFour touches across seven days, three paid channels and one owned. Who "brought" the sale? Different models answer this very differently — and the difference is large enough to flip a channel from "double the budget" to "cut entirely."
The six attribution models
1. Last-click
The final touchpoint before conversion gets 100% of the credit. In the example above, Email gets everything and Display, Search, and Direct get zero.
Last-click is the default in Google Analytics, most CRMs, and most affiliate platforms. It is easy to compute, easy to explain, and easy to argue about. It systematically underweights brand and awareness channels (which rarely sit at the end of the path) and overweights retargeting, branded search, and email — channels that intercept users who were already going to convert. If you ever see a last-click dashboard claim email ROAS of 30x, the email is probably catching demand created upstream.
2. First-click
The first known touchpoint gets 100%. In the example, Display gets everything.
First-click is the mirror image of last-click. It correctly elevates awareness channels but ignores everything that happens after the first touch, including the channels that actually closed the loop. It also rewards accidental first touches — a stray click on a low-quality network can take credit for a conversion that happened sixty days later for unrelated reasons.
3. Linear
Every touchpoint gets equal weight. Four touches in the path means 25% credit each.
Linear is "fair" in the sense that it acknowledges the full funnel exists. It's also lazy: it pretends the first impression and the closing email contributed identically to the outcome, which they almost never did.
4. Time-decay
Touches closer to the conversion get more weight; touches further back get less. A typical decay assigns roughly 50% / 30% / 15% / 5% from latest to earliest. There's a half-life parameter (often 7 days) that controls the curve.
Time-decay is a defensible middle ground for consideration-cycle products — software trials, financial accounts, anything with a 1-to-3 week decision window. It still requires you to set the half-life by hand, which is a tunable knob masquerading as a model.
5. Position-based (U-shaped)
40% to the first touch, 40% to the last touch, 20% split among everything in the middle.
Position-based is popular in B2B because it rewards both the discovery channel and the closing channel — a reasonable story when you have a sales-led funnel with a lot of nurturing in between. The 40-20-40 split is, however, completely arbitrary. There is no empirical basis for those numbers; they exist because they sound balanced.
6. Data-driven attribution (DDA)
An algorithm — typically a Markov chain or a Shapley value computation — assigns credit based on how often each channel appears in converting vs non-converting paths. Google Analytics 4, Adobe Analytics, and most mobile measurement partners (Adjust, AppsFlyer, Branch) ship a DDA model.
DDA is the most defensible "credit-allocation" model when you have enough volume — usually 5,000+ conversions per month and clean cross-device identity. It adapts to your actual paths instead of imposing a shape. The trade-off is opacity: when the channel mix shifts, the model recalculates and yesterday's "winner" can become today's "neutral" without a clear explanation. It is also still not causal.
Side-by-side comparison
Same user, same four touches — see how dramatically credit shifts:
| Channel | Last-click | First-click | Linear | Time-decay | U-shaped |
|---|---|---|---|---|---|
| Display | 0% | 100% | 25% | 5% | 40% |
| Search | 0% | 0% | 25% | 15% | 10% |
| Direct | 0% | 0% | 25% | 30% | 10% |
| 100% | 0% | 25% | 50% | 40% |
The thing to remember: the same conversion, allocated five different ways, produces five different ROAS rankings. Pick one model, document it, and force every team to grade themselves against the same yardstick.
Post-view vs post-click and the iOS twist
Post-click attribution counts a touch only if the user actually clicked. Post-view attribution counts a touch if the user merely saw an impression — even without a click — and converted later.
Post-view is where attribution gets generous. A user who scrolled past a display ad three days ago and converts today via organic search will, under post-view rules, hand display partial credit for the conversion. This is how display networks (and many influencer platforms) book inflated numbers.
Apple's App Tracking Transparency (ATT), effective since iOS 14.5 in 2021, collapsed the post-view window on iOS down to a much shorter SKAdNetwork-mediated signal — typically a single conversion postback, no individual user. This broke the install-attribution models of every mobile app that depended on Meta Ads and TikTok Ads. If you still measure iOS install ROAS with the same model you used in 2020, your numbers are wrong in a specific, biased direction.
Attribution windows
The attribution window is the lookback period for crediting touches. Anything outside the window is ignored.
| Window | Typical use case |
|---|---|
| 1 day | Pure performance / quick-decision products (food delivery) |
| 7 days | Consumer apps and e-commerce default |
| 28 days | SaaS, mid-size B2C, default for Google Ads conversions |
| 90 days | B2B with long sales cycles, enterprise software |
Mismatched windows are a stealth source of disagreement. If your paid social team uses a 7-day window and your finance team uses a 30-day window, you will book different conversion totals for the same week — and someone will be wrong about a budget call.
Incremental attribution: the harder question
Every model above answers "which channel should we credit?" None of them answer "how many of these conversions would have happened anyway?" That's the incrementality question, and it is the one that actually matters for budget decisions.
Incremental attribution estimates the lift caused by a channel — sales that would not have occurred without it. The main methods:
- Holdout tests. Randomly suppress a channel for a fraction of users (say, 10%) and compare conversion rates. Clean, causal, often expensive in opportunity cost. See holdout vs A/B testing in practice.
- Geo experiments. Turn off a channel in one matched geographic market and measure the gap versus comparable markets. Less precise than holdout but easier to negotiate with stakeholders.
- Marketing mix modeling (MMM). Econometric regression on aggregated spend and outcome time series. Works without user-level data — useful in the post-ATT world — but slow to refresh and sensitive to specification.
Incremental measurement is the gold standard. It is also slower, costlier, and harder to socialize. Most companies run attribution day-to-day and incrementality quarterly, and they reconcile the two when budgets shift.
In the interview room
A common growth interview question: "How would you pick an attribution model for our paid program?"
A strong answer covers four moves. First, acknowledge there is no single correct model — the choice depends on funnel length, data volume, and cross-device behavior. Second, default to last-click for tactical decisions (pacing, bid changes) because it's stable and well understood, while flagging the bias. Third, run two or three models in parallel — a comparison table shows the sensitivity of channel rankings to the choice of model. Fourth, layer incrementality testing on top for strategic budget calls.
If the interviewer pushes on causality, the right phrase is: attribution is correlational, incrementality is causal. When they disagree, trust incrementality.
Common pitfalls
When teams adopt attribution for the first time, the loudest mistake is using last-click for everything, including brand and awareness budgets. Last-click systematically starves upper-funnel channels — they almost never sit at the end of a path. Within two or three quarters the company stops generating new demand, performance channels become more expensive because they're harvesting a shrinking pool, and someone gets blamed for "rising CAC" when the real cause was the measurement model.
A second trap is conflating attribution with causation. The dashboard says display contributed 40% of the conversion, and a junior analyst writes "display drove $1.2M in revenue this quarter." It didn't drive anything; it was present on the path. The honest sentence is "display was a touchpoint on conversions worth $1.2M under our linear model." That's a longer sentence, but it survives the kind of follow-up question that ends careers.
A third pitfall is ignoring cross-device journeys. A user who sees the ad on mobile and converts on desktop, without a logged-in identifier bridging the two, will look like a desktop direct conversion. Attribution will under-credit mobile and over-credit direct. The fix is some form of cross-device identity — email login, device-graph stitching, or accepting the gap and modeling around it.
A fourth trap is comparing ROAS across teams using different models. If performance reports last-click ROAS and brand reports linear ROAS, every channel optimization conversation becomes a method argument disguised as a strategy debate. Pick one model for cross-team comparisons; let teams use other models internally for sanity checks.
A fifth pitfall is forgetting organic and direct. If brand-driven direct traffic grows because brand campaigns are working, but you don't credit brand at all, you'll conclude brand is unprofitable and cut it. Six months later, direct traffic collapses, and now you have a much bigger problem.
Related reading
- How to calculate CAC in SQL
- How to calculate first-touch attribution in SQL
- How to calculate last-touch attribution in SQL
- Holdout vs A/B testing in practice
- A/B testing complete guide
If you want to drill questions like these — attribution, growth, SQL — every day, naildd is launching with hundreds of interview problems across exactly this pattern.
FAQ
Which attribution model is "the right one"?
There is no single right model. The right approach is to run two or three in parallel — last-click for tactical reporting, data-driven or time-decay for strategic reads, and periodic incrementality tests for big budget calls. Where they agree, act. Where they disagree, dig in before reallocating money.
Did iOS ATT kill mobile attribution?
It changed the rules significantly. User-level post-view on iOS is effectively gone; SKAdNetwork provides aggregated signal with a small set of conversion values. Most mobile teams now mix SKAdNetwork, MMP-modeled conversions (Adjust, AppsFlyer, Branch), and MMM for strategic planning. Anyone reporting iOS install ROAS with three-decimal precision is overstating their confidence.
What's the difference between attribution and incrementality?
Attribution is bookkeeping: it allocates credit for conversions that already happened. Incrementality is causal: it measures how many of those conversions would not have happened without the channel. A channel can score high on attribution and low on incrementality if it mostly intercepts users who were already going to convert — branded search and retargeting are the classic examples.
How do I run an incrementality test cheaply?
Start with a geo holdout. Pick two matched markets — similar size, demographics, and historical conversion rate — and turn off the channel in one for four to six weeks. Compare the gap to your baseline. Coarse, but it requires no engineering and produces a defensible directional read.
Why does my data-driven model keep changing channel rankings?
DDA recomputes against current data. When the channel mix shifts — new campaigns, creative refreshes, seasonality — rankings move. This is mostly a feature, but it means DDA is unsuitable for short-term tactical decisions. Use last-click or time-decay for week-over-week pacing and reserve DDA for monthly strategic reads.
Should I include organic and direct as "channels"?
Yes. Excluding them inflates every paid channel's contribution. Model direct, organic, and referral as their own channels in the same comparison table as paid. If direct is growing while paid is flat, that's a brand effect worth investigating — not free conversions to spread across last-click rows.