Attribution models explained: choose the right view for your marketing

Keep the outcome fixed

Imagine one recorded order preceded by a paid social click, a search visit, and an email click. The order value stays the same when you change models. What changes is how the chosen rule distributes credit among those recorded interactions.

This matters because a model comparison is a sensitivity check, not a competition in which one answer must be the truth. If a channel's credited revenue moves sharply across reasonable models, the conclusion depends on the rule. That is useful information to bring into a budget conversation.

Know what the common rules do

Each model encodes a different view of the journey. The labels are shorthand; check the exact implementation, eligible touchpoints, and lookback window before comparing systems.

• First touch gives all modelled credit to the earliest eligible recorded touch. It foregrounds discovery but ignores later interactions.
• Last touch gives all credit to the final eligible recorded touch. It is simple to explain, but can overemphasize channels that appear near conversion.
• Linear attribution divides credit equally among eligible recorded touches. It treats every included touch as equally important, whether or not that assumption fits the buying process.
• Time decay assigns more credit to touches closer to the outcome according to a defined decay rule. The decay period must be stated; it is a choice, not a law of customer behavior.
• Position-based models reserve specified shares for positions such as first and last, then distribute the remainder across middle touches. The percentages should be visible and justified.
• Data-driven or custom models depend on their own inputs, training or configuration, and validation. Do not assume that a more complex label means a more causal or reliable answer.

Match the model to the decision

A team studying which recorded sources introduce prospects may use a first-touch view as one lens. A team reviewing the final interactions before a purchase may inspect last touch. A team that wants a neutral baseline across observed interactions may start with linear. None should be the sole basis for a high-stakes decision without checking the underlying journey and business context.

The buying cycle matters. A short, repeat-purchase journey may have different useful windows from a considered purchase with weeks of research. Choose a window that reflects the question and the data available, then apply it consistently. Do not tune the window after seeing which option makes a channel look strongest.

Read the differences, not only the totals

Compare the same conversion set, date range, revenue definition, eligible channels, and coverage across models. If those inputs also change, the model comparison is not isolating the model. Include unmatched journeys and offline or direct activity as visible limitations rather than silently dropping them.

When results disagree, inspect a sample of journeys and ask why the rules differ. Look for retargeting near checkout, branded search, email reminders, missing organic visits, or a long lag between first interaction and conversion. The disagreement can point to a measurement or strategy question worth testing.

Attribution is not incrementality

Even a perfectly implemented rule only allocates credit across observed, eligible touchpoints. It does not show what would have happened if a campaign had not run. People can encounter ads and purchase for many other reasons; that counterfactual is not visible in an ordinary journey report.

Use experiments, holdouts, or another suitable causal design when the decision depends on incremental impact. Use model comparisons to make credit-allocation assumptions clearer and more consistent. Keeping those jobs separate makes both analyses more useful.

Compare five models on the same £200 order

Take a fictional £200 order preceded by four eligible touchpoints: paid social, organic search, paid search, and email. All four touches fall inside the same reporting window, and direct visits are excluded from this example. First touch gives paid social £200. Last touch gives email £200. Linear gives each channel £50. The order is unchanged; the accounting rule changes.

For an illustrative 40/20/40 position-based rule, the first and last touches receive £80 each and the two middle touches receive £20 each. A time-decay model cannot be calculated from the channel order alone: it also requires timestamps and a decay parameter. If someone presents a time-decay allocation without those inputs, ask how the weights were derived.

Use a primary model and a comparison model

Teams work faster when they agree which model anchors their regular reporting. Select a primary model that the people using the report understand, document its assumptions, and keep it stable. Then use a second model as a comparison. For example, a team may use linear as a view of the full recorded journey and last touch to understand which interactions close it.

The purpose is not to average the results into a new truth. It is to see whether a decision depends heavily on the allocation rule. If paid social receives a meaningful share under linear and little under last touch, inspect whether it consistently appears early in journeys. If email dominates last touch, check the share of returning customers and how often email arrives shortly before purchase.

Choose a review cadence for settings. Changing a model every week makes the trend difficult to interpret. If the buying cycle changes or new sources become available, evaluate the rule and communicate any change. Keep historical comparisons on one consistent rule or clearly mark where the definition changed.

Decide how direct traffic and repeated visits behave

Direct traffic can mean a customer typed an address, followed an untagged link, returned through a bookmark, or arrived in a way that lost its referrer. A last-touch model that credits direct will answer a different question from last non-direct click. Neither label is sufficient unless the report says how direct is handled.

Repeated touches also matter. If a journey has three email clicks and one social click, equal credit per touch gives email three quarters of the value. Equal credit per channel would give email half. Both are possible implementations, and their outputs differ despite sharing an informal linear label. Ask whether the unit being weighted is the event, session, channel, or another grouping.

Google Analytics currently documents data-driven attribution and two last-click models. It removed first-click, linear, time-decay, and position-based models from its attribution reporting in November 2023. These remain general modelling approaches and may exist in other tools; do not assume that a list of common models is a list of settings available in every platform.

Read modelled ROAS without confusing it with profit

Attributed return on ad spend divides a channel's credited revenue by its eligible ad spend. If the fictional channel receives £8,000 of credit against £2,000 of spend, its attributed ROAS is 4.0. That ratio changes if the model changes the credit, even though the campaign spend and store sales have not changed.

A 4.0 attributed ROAS does not say the campaign generated £6,000 in profit. Product costs, delivery, returns, payment fees, and other business costs still need to be considered. It also does not establish that the sales would disappear without the advertising. Use the ratio to compare a consistent reporting view, then bring margin and experiments into the decision where needed.

Create a model selection note your team can reuse

Write down the intended question, eligible touchpoints, conversion set, lookback window, treatment of direct traffic, and chosen rule. Add one worked example. If colleagues cannot reproduce a simple journey's credit, the model is not yet sufficiently explained for routine reporting.

Record why the team chose the model and which decisions it should not make alone. Assign an owner to settings so that one user cannot quietly change the definition behind a shared report. That small amount of documentation usually prevents more confusion than another dashboard chart.