Google Ads attribution models: how to measure value?

A Google Ads campaign records 100 conversions and a ROAS of 5.

The result looks positive, but it leaves one question unanswered: how many of those purchases would have happened even without the campaign?

A customer might have discovered the brand on YouTube, visited the website through organic search and finally clicked on a Search ad before purchasing.

Google Ads can assign part of the conversion value to that final interaction or distribute it across several ads, depending on the attribution model.

This helps advertisers interpret performance and optimise campaigns, but attributed conversions do not necessarily reflect the incremental value generated by the investment.

The distinction becomes particularly important when deciding whether to increase a budget, invest in new channels or acquire customers who are not yet familiar with the brand.

Which Google Ads attribution models are available?

The main Google Ads attribution models available today are Last Click and Data-Driven Attribution (DDA).

Google has retired its previous First Click, Linear, Time Decay and Position-Based models.

With Last Click, all the credit for a conversion goes to the last clicked ad in the journey considered by the model.

Data-Driven Attribution, on the other hand, uses account data to distribute credit across the advertising interactions that contributed to the conversion.

As Google explains in its official DDA documentation, the system compares the journeys of users who convert with those who do not, identifying the interactions that contribute most to conversions.

DDA is the default model for most conversion actions and can provide Smart Bidding with more detailed signals than Last Click.

However, its measurement is based on the advertising interactions observed by Google Ads.

Evaluating the contribution of other channels and the overall impact on the business requires complementary tools.

Attribution and incrementality: what is the difference?

Imagine a campaign generates 100 attributed conversions.

Through a controlled experiment, we discover that a significant proportion of those users would have purchased even without the advertising.

The campaign therefore contributed to the conversion journey, but only some of those sales represent an additional result.

This is where incrementality comes in.

Attribution distributes credit for conversions across advertising interactions. Incrementality measures the difference between what happens with advertising and what would have happened without it.

This second perspective is particularly useful for branded campaigns, remarketing and activities targeting users who are already familiar with the brand.

A high attributed ROAS can coexist with a more limited incremental contribution.

Measuring that contribution requires experiments designed to estimate the causal effect of advertising investment.

Conversion Lift: how to measure additional sales

Google provides Conversion Lift, a tool designed to measure the incremental impact of campaigns through controlled experiments.

The principle is fairly straightforward.

One group is exposed to advertising, while a control group is excluded from exposure. By comparing the results of the two groups, advertisers can estimate how many additional conversions were generated by the ads.

In its official Conversion Lift guide, Google distinguishes between user-based experiments and geographical experiments, where different areas are compared.

Depending on the type of study, available metrics include incremental conversions, incremental conversion value, iCPA and iROAS.

iROAS, in particular, connects the additional value generated to advertising investment. Conversion Lift is not available to every account and has specific eligibility requirements.

Accessing the tool may also require support from a Google representative.

Experiments themselves need careful planning: comparable groups, sufficient data and an appropriate duration are essential for obtaining interpretable results.

Marketing Mix Modeling: when you need a broader view

Experiments are useful for measuring the effect of a specific investment. When several channels, promotions and brand activities are involved, a broader perspective can help.

Marketing Mix Modeling (MMM) uses aggregated data and statistical models to estimate how different marketing activities contribute to business results.

For example, it can consider investment in Search, social, video and TV alongside factors such as seasonality, promotions and changes in demand.

With Meridian, its open-source MMM framework, Google provides tools for analysing channel contributions and evaluating different investment scenarios.

In September 2026, Google also announced further updates to Meridian, including the global availability of Meridian GeoX, a library for running geographical experiments and using the results to calibrate the model.

Combining MMM with experiments can produce more robust estimates, although the results still depend on data quality and the assumptions behind the model.

For brands investing across several platforms, this approach can help determine how to allocate budgets between channels that contribute at different stages of the customer journey.

First-party data: reliable measurement starts with tracking

Attribution, experiments and statistical models work better when they are built on reliable data.

For Google Ads, that means checking conversion actions, conversion values, deduplication and tracking consistency.

Enhanced Conversions, for example, use user-provided first-party data to improve conversion measurement, subject to the applicable settings and requirements.

For lead generation, connecting Google Ads to the CRM is equally important. It helps distinguish a simple form submission from a qualified lead or an actual sale.

The updates announced by Google in September 2026 reinforce this approach, with additional Data Manager tools for connecting first-party data and identifying measurement issues.

One point is worth keeping in mind: recovering conversions that tracking previously missed improves reporting accuracy.

Measuring incrementality still requires an analysis of the causal impact of advertising.

These are two different improvements, both useful for making more reliable decisions.

ROAS, iROAS and business outcomes: which KPIs matter?

ROAS remains a central metric in day-to-day campaign management.

To evaluate the value of an investment, however, it is useful to combine it with indicators that are more closely connected to commercial results.

The most relevant include incremental revenue, iROAS, CAC, profit margins and Customer Lifetime Value.

An ecommerce business can generate a large number of conversions during a promotion while operating on narrow margins or acquiring customers who never purchase again.

Similarly, a campaign focused on acquiring new customers may have a lower initial ROAS while contributing to greater value over the medium term.

The choice of KPIs should therefore reflect the campaign's actual objective: acquisition, profitability, retention or overall growth.

Discrepancies between Google Ads, GA4 and transactional data should also be interpreted in light of the attribution model, conversion window and the point at which a conversion is recorded.

Budget decisions require a consistent interpretation of the numbers, with a clear understanding of each platform's role.

How to improve Google Ads campaign measurement

For many accounts, the first step is to make sure the current setup is reliable: correct conversion actions, accurate values and first-party data connected where necessary.

The second is to use Data-Driven Attribution and attribution reports to understand the contribution of different advertising interactions.

Where budgets and conversion volumes allow, incrementality experiments can help establish how much additional value specific campaigns generate.

For brands investing across several channels, Marketing Mix Modeling can then help translate those findings into planning decisions.

In 2026, effective Google Ads measurement therefore requires connecting attribution, data quality and incremental evidence.

The objective is to answer a very practical question: how much additional value does every pound invested generate, and where should the next one be allocated?