Attribution: Why Your Channels Don't Add Up

Open the ads platform: it claims eighteen conversions. Open the analytics property: it claims eleven from paid search. Count the actual enquiries in the inbox: fourteen. Add up every channel in the analytics report and the total is short of the site-wide figure.

Nothing is broken. This is what attribution is, and understanding why the sums refuse to reconcile is more useful than finding a tool that hides the discrepancy.

The journey is not a single step

Attribution is the act of assigning credit for one outcome to several causes. Note the word assigning. Nobody is measuring which touchpoint caused the sale, because that is not observable. A model is applied, and the model is a rule somebody chose.

A realistic path to a moderately considered purchase looks something like this. A person reads something you published after a search. Weeks later they see your name mentioned somewhere. They search your brand directly, look at two pages, leave. A fortnight after that they click a paid advert on their phone and ring you.

Four interactions. One enquiry. Any system that reports a whole number against a channel name has already made a decision about how to split that, and different systems make different decisions.

Three separate reasons the totals disagree

These get conflated constantly. They are distinct problems with distinct fixes.

Different systems count different things. An advertising platform counts a conversion when someone who interacted with its advert converts within its own window — often crediting a view rather than a click, and crediting the day of the click rather than the day of the conversion. Analytics counts a conversion in the session where it happened, on the day it happened. The two were never going to agree, because they are not measuring the same event.

Windows and models differ. A lookback window of thirty days and one of ninety days will produce different numbers from identical data. So will a last-click rule versus a distributed one. Two reports of “conversions by channel” that use different settings are simply two different questions.

Some journeys cannot be observed at all. Consent refusals, tracking prevention in browsers, ad blockers, a switch from phone to laptop, an app that strips referrer information, a link copied and pasted into a message. Every one of those breaks the chain, and the resulting conversion lands in direct or unassigned. That bucket is not a channel. It is the pile of things the system could not identify, and it is bigger than most people assume.

What last-click quietly does

Last-click attribution gives the whole outcome to the final interaction before conversion. It is easy to explain, it is stable, and it is wrong in a specific and predictable direction.

It flatters anything that appears near the end. Brand search is the clearest case. Somebody who already knows your name and types it into Google is not being persuaded by that search result — they were persuaded earlier, by something else. Under last-click, brand search and retargeting collect credit for demand they did not create. Retargeting in particular is structurally advantaged: it only ever shows to people who already visited, so it is always standing at the finish line.

It starves everything that appears near the beginning. The article that first introduced you. The generic search that started the process. The social post that made someone curious. All of these receive nothing, because none of them were last. Their reported return sits at or near zero, and a rational manager reading that report cuts them.

Then, two quarters later, the channels that still look efficient stop delivering, because the supply of people arriving at the end of a journey depends on something putting them at the start of one. The report never showed that dependency, because last-click cannot see it.

The same logic applies in reverse to first-click, which over-credits discovery and ignores whatever closed the deal. Neither rule is a description of reality.

Models are assumptions wearing a lab coat

The alternatives all have the same character. A linear model splits credit evenly across every touchpoint, which assumes each mattered equally — a strong assumption stated as a default. A time-decay model weights recent touchpoints more heavily, which assumes influence fades, sometimes true and sometimes not. A position-based model loads credit onto the first and last interaction, which assumes the middle is filler.

Data-driven attribution, which analytics platforms increasingly default to, is more sophisticated: it compares converting and non-converting paths to estimate each touchpoint’s contribution. It is better. It is still a model, it needs a substantial volume of conversions before it can produce anything meaningful, and platforms will quietly fall back to a simpler rule when that volume is not there. If you are a local business with a handful of enquiries a month, assume you are on the simpler rule regardless of what the setting says.

None of this makes models useless. It makes them lenses. Changing the model changes the numbers without changing what happened, and the honest way to use one is to pick a consistent lens and compare periods through it — not to hunt for the model that makes your favourite channel look best.

What to do instead of chasing one true number

Stop reconciling platforms. Accept that the ads platform will always report more than analytics and use each for its own job: the platform for optimising within itself, analytics for comparing across channels. Never add them together.

Anchor on the total. Total enquiries, counted at source — the inbox, the call log, the diary. That figure is not a model. Watch it against total spend and you have a blunt but honest measure of whether the whole effort is working, which is the question that actually matters.

Ask people. A single optional field on the enquiry form — “how did you hear about us?” — is imprecise, poorly answered and full of guesses. It also catches the offline sources no script will ever see: the recommendation, the van, the sign, the conversation. Use it as a sanity check against the analytics, not as a replacement.

Test by absence. The most reliable attribution evidence available to a small business is turning something off. Pause a channel for a month and watch total enquiries. If nothing changes, the report was overstating it. This is crude, it is confounded by seasonality, and it is still more persuasive than any dashboard.

Look at the paths, not just the totals. Most analytics tools will show you the sequences that preceded conversions. Even a rough look at which channels appear early and which appear late tells you more about how your marketing works than any single-number report.

Attribution is not a problem you solve. It is a limitation you plan around. If your channel reporting currently disagrees with itself and nobody can explain why, that is a good reason to have the measurement looked at properly before the next budget decision.