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Choose the lift study according to the business decision you need to make. Use Brand Lift when you need to know whether advertising changed perceptions such as awareness, ad recall, consideration, favorability, or purchase intent. Use Search Lift when the question is whether exposure prompted more brand-related searches on Google or YouTube. Use Conversion Lift when you need an estimate of incremental purchases, sign-ups, visits, sales, or another defined business action.
All three are controlled experiments, not attribution reports. A treatment group is exposed to ads and a control group is not; the difference in outcomes estimates the campaign’s incremental effect. The right choice depends on the outcome, available campaign types, experimental unit, data volume, budget, and reporting detail. No one study proves every stage of the customer journey.
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The short answer: match the study to the decision
- Choose Brand Lift if the next decision concerns how people think or feel about the brand, such as whether awareness or consideration improved.
- Choose Search Lift if you need evidence that advertising caused more active information seeking through brand-related searches.
- Choose Conversion Lift if the decision depends on incremental business outcomes rather than conversions merely credited to an ad interaction.
Running more than one study makes sense only when each one answers a different question. For example, Brand Lift can test perception, Search Lift can test information seeking, and Conversion Lift can test business action. Running all three does not turn a campaign into a complete customer-journey measurement system; each estimate still applies only to its own population, outcome, period, and design.
How a lift study differs from attribution
Google Ads Help describes a lift study as “a controlled experiment where your audience is split into two groups automatically.” The treatment group is shown ads, while the control group is not shown them. The outcome difference between those groups is the estimated lift.
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That design addresses an incrementality question: what happened because the campaign was shown to the treatment group, compared with what would likely have happened without that exposure? Attribution answers a different question by assigning credit among observed ad interactions and conversion paths. Attribution is useful for ongoing campaign reporting, while randomized lift experiments can inform channel budgets and future campaign decisions. John Chen, Google’s Group Product Manager for Measurement, described lift experiments in an October 12, 2020 Google Ads & Commerce Blog post as a way for businesses to move beyond data-driven attribution and use randomized controlled experiments to set channel-level budgets or optimize future campaigns.
A positive attributed conversion count therefore does not automatically mean the ads caused that many additional conversions. Some people would have converted without advertising, and a lift design attempts to estimate that counterfactual difference.
What each study measures
| Study | Primary outcome | Decision it informs | Typical examples | Best fit |
|---|---|---|---|---|
| Brand Lift | Change in consumer perception | Whether the campaign improved upper-funnel brand health | Ad recall, awareness, consideration, favorability, purchase intent | Brand or creative decisions where perception matters before a transaction |
| Search Lift | Change in brand-related search behavior | Whether exposure prompted people to seek more information | Organic searches for the brand on Google or YouTube, as defined by Google’s product materials | Campaigns where branded discovery and active research are important signals |
| Conversion Lift | Incremental business actions | Whether the campaign caused additional measurable outcomes | Sales, purchases, sign-ups, website visits, or another configured conversion | Budget and optimization decisions tied to measurable business results |
Brand Lift: when perception is the outcome
What it can tell you
Brand Lift uses survey-based experimental measurement to estimate whether exposed people report different perceptions from people in the control group. Depending on the study configuration, questions can cover ad recall, awareness, consideration, favorability, and purchase intent. The result is useful when a campaign is intended to influence future demand, not just capture an action that happens immediately.
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Choose Brand Lift when the next action is a creative, positioning, reach, or upper-funnel budget decision. It is appropriate for a new product introduction, a repositioning campaign, or a video campaign whose objective is to make the brand more memorable even when a purchase is not expected during the measurement window.
What the experiment looks like
The platform assigns eligible users to treatment and control groups. Treatment users see the campaign, and both groups may be invited to answer a survey. The lift estimate compares the relevant response rate between groups rather than simply reporting the percentage of exposed respondents who answered positively.
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Limits to keep in mind
Survey experiments can be affected by solicitation and response bias. People who agree to answer may differ from those who do not, and the act of being asked can influence a response. There can also be a mismatch between intended treatment and actual ad exposure. The 2018 Joint Statistical Meetings paper “Methods for Measuring Brand Lift of Online Ads,” by Rachel Fan, Tim Hesterberg, Ying Liu, and Lu Zhang, discusses these methodological issues, including how analysts compare treated users who took an action with control users who might have acted.
Brand Lift is therefore evidence about measured perception in the study population, not a universal statement about every customer. Use the exact question wording, audience definition, field period, and response rates when deciding how much confidence to place in the result.
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What it measures
Search Lift tests whether ad exposure increased searches associated with the brand on Google or YouTube, according to Google’s product descriptions. It sits between perception and conversion: a person may remember an ad and then search for the brand, but may not yet be ready to purchase.
When it is useful
Select Search Lift when your strategic question is, “Did this campaign cause more people to look us up?” It can help evaluate awareness campaigns, creator or video campaigns, and launches where the desired next step is active information seeking. Search activity can reveal demand that ordinary click reports miss, particularly when people search later, use a different device, or visit through an unpaid result.
How to interpret the outcome
The estimate concerns the defined set of brand-related searches and the study’s observation period. It does not prove that every additional search became a qualified lead or sale. A search may reflect curiosity, comparison shopping, customer service needs, or an existing customer returning to the brand. Pair the result with downstream conversion measurement when revenue is the decision being made.
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Availability and timing
Google’s March 2025 “Expert Deepdive: Incrementality Testing in Google Ads: Brand & Search Lift” training describes Brand Lift and Search Lift as incrementality tools. Product access, supported markets, audience requirements, and reporting fields can change, so confirm availability in the relevant Google Ads account before promising a Search Lift readout.
Conversion Lift: testing incremental business outcomes
What it answers
Conversion Lift estimates whether the campaign caused more defined conversions than would have occurred without exposure. Depending on setup, the outcome can be a purchase, sale, sign-up, website visit, app action, or another conversion event. This is the study to choose when the budget question is fundamentally about incremental return rather than credited return.
User-based and geographic designs
Google’s comparison guidance distinguishes user-based and geographic Conversion Lift. A user-based study randomizes people and can provide detailed breakdowns, but it relies mostly on opt-in traffic and can leave measurement gaps. A geographic study compares assigned regions or other geographic units. It can support cross-channel and online/offline conversion measurement, but geographic variation creates more noise and the design does not provide the same demographic breakdowns as a user-based study.
Campaign eligibility
Google’s current comparison page lists user-based Conversion Lift for Video, Discovery, and Demand Gen campaigns. It lists geographic Conversion Lift for Video, Display, Discovery, Demand Gen, Search, Shopping, App, and Performance Max, with access through a Google account representative. These are product-level descriptions, not a guarantee that every account, country, campaign, or conversion action qualifies. Check the current setup screen or ask the account representative before allocating budget for a study.
Does it prove the ads caused more sales?
It provides a causal estimate within the experiment’s design, not absolute proof that every reported sale was caused by advertising. The estimate depends on correct assignment, sufficient data, stable measurement, a clearly defined conversion, and the assumption that treatment and control groups were comparable apart from ad exposure. A result for one campaign and period should not be presented as a permanent multiplier for all future campaigns.
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Comparison by the criteria that affect your choice
| Criterion | Brand Lift | Search Lift | Conversion Lift |
|---|---|---|---|
| Outcome | Surveyed perception: recall, awareness, consideration, favorability, or purchase intent | Brand-related searches on Google or YouTube | Incremental configured conversions such as sales, purchases, sign-ups, or visits |
| Primary business question | Did the campaign change how people think or feel? | Did exposure prompt active information seeking? | Did exposure cause additional business actions? |
| Experimental unit | Usually users assigned to treatment or control for a survey experiment | User-level exposure and search behavior as defined by the product | User-based assignment or geographic units, depending on the study |
| Reporting detail | Survey responses and lift by supported question or audience breakdown | Incremental search behavior for the measured brand terms | User-based studies can offer detailed breakdowns; geographic studies support broader online/offline measurement but are noisier |
| Cross-channel reach | Primarily measures the tested campaign’s effect on perception | Measures search behavior associated with the tested exposure | Geographic designs can support cross-channel and online/offline conversion measurement |
| Main limitation | Solicitation, response, and treatment-mismatch bias in survey experiments | A search is not necessarily a lead, purchase, or incremental revenue | Results can be noisy or incomplete; eligibility and data requirements vary by design |
A practical selection process
- Write the decision in one sentence. Use wording such as “Should we change the creative because people do not remember it?”, “Did the launch create more branded demand?”, or “Should we move more budget into this campaign because it generates incremental sales?” The noun in that sentence usually identifies the study.
- Define the outcome before launch. For Brand Lift, specify the survey question and response definition. For Search Lift, define which brand-related search behavior is relevant. For Conversion Lift, name the exact conversion event, attribution window used for operational reporting, and period in which the experiment will be read.
- Confirm the experimental population. Decide whether the study will cover users, geographic regions, or another supported unit. Record the countries, campaign types, audience eligibility, and any exclusions so the result is not generalized beyond the tested population.
- Check platform eligibility. Review the current Google Ads comparison and lift-study setup for the account. Campaign support, market access, representative involvement, thresholds, and available breakdowns can change.
- Estimate whether the data can support a result. Historical conversion volume, expected response rates, geographic variability, and campaign reach affect the required scale. Do not use a generic minimum sample size copied from another account.
- Choose the reporting detail you actually need. If demographic or audience breakdowns are essential, a user-based design may be preferable where available. If online and offline outcomes across channels matter more, a geographic design may be worth the additional noise.
- Pre-register the interpretation. Decide what confidence interval, minimum detectable effect, and decision rule will be used before seeing the result. State whether the study is exploratory or will determine a budget change.
- Keep attribution separate. Continue using attribution for operational pacing and path analysis, but label the lift estimate as the causal incrementality result. Do not add attributed and incremental conversions together without a defined methodology.
Eligibility, budget, and data requirements
There is no reliable universal price or minimum conversion count that applies to every lift study. Google’s comparison guidance says user-based Conversion Lift budget is determined during setup using historical conversion data, while geographic studies generally require more data and higher requirements. The appropriate threshold depends on the conversion rate, expected lift, audience size, geographic stability, and the study’s confidence target.
Before launch, gather:
- Historical conversion or search volumes for the period and markets being considered.
- The campaign types, countries, languages, and audience settings that will be included.
- A stable conversion definition and tagging or measurement implementation.
- Any online and offline data feeds needed for a geographic design.
- The minimum business effect that would justify a creative, channel, or budget change.
Ask for the current account-specific requirements rather than publishing a fixed number. A study that is technically available may still be impractical if the campaign cannot generate enough observations during the measurement period.
Reporting trade-offs: detail versus reach
User-based studies
User-level assignment can support more granular analysis because the experimental unit is an individual user. The trade-off is that measurement may rely on opt-in traffic or observable users, so some journeys are not captured. A detailed dashboard does not remove that coverage limitation.
Geographic studies
Geographic assignment can include outcomes that cross devices, platforms, or online and offline environments when the data is available. Regions, however, differ in population, seasonality, media mix, and baseline demand. Those differences increase statistical noise, and the design cannot provide the same demographic slices available in a user-based study.
What to put in the readout
Report the treatment and control definitions, study dates, included markets, outcome definition, estimated lift, uncertainty interval, sample or geographic-unit counts, and any missing-data or eligibility constraints. Include the absolute incremental number as well as the relative percentage when both are available; a large percentage applied to a tiny base and a modest percentage applied to a large base imply different business decisions.
How to avoid overclaiming a lift result
- Keep the population attached to the estimate. “Among eligible users in the tested campaign and period” is more accurate than saying “the brand gained this lift.”
- Distinguish relative and absolute change. A relative lift percentage is not the same as additional people, searches, or sales.
- Use uncertainty, not just a point estimate. A result near zero with a wide interval does not support the same decision as a precise positive result.
- Do not treat one test as a benchmark. The reviewed sources establish no universal “good” lift percentage. Results vary with audience, creative, market, season, baseline demand, and outcome definition.
- Check interference and contamination. Control users or regions may encounter related campaigns, organic activity, or other media. Document overlapping activity instead of assuming the experiment isolated every influence.
- Do not claim that randomization removes every bias. Assignment improves causal identification, but implementation gaps, noncompliance, missing outcomes, survey response behavior, and geographic noise can still affect the estimate.
Using multiple studies across the funnel
Google reported in 2020 that Fiverr used Brand Lift, Search Lift, and Conversion Lift in one campaign test. The reported figures were a 10 percent lift in consideration, a 62 percent lift in searches on Google and YouTube, and a 30 percent lift in new users. Those numbers describe one vendor-reported case study, not expected performance or a planning benchmark.
A multi-study plan is defensible when each result has a separate decision owner: brand marketing can act on perception, demand generation can act on search behavior, and performance marketing can act on incremental conversions. Define the studies independently so a strong result in one layer does not substitute for missing evidence in another.
Which study should you run for common scenarios?
A YouTube campaign intended to build awareness
Start with Brand Lift if success means improved recall, awareness, or consideration. Add Search Lift when the campaign’s next desired behavior is a branded search. Add Conversion Lift only if there is enough eligible conversion data and a business decision depends on incremental actions.
A campaign whose budget is justified by sales
Prioritize Conversion Lift, selecting user-based or geographic design according to the required reporting and cross-channel scope. Continue attribution for day-to-day optimization, but use the lift result for the causal budget decision.
A product launch with little immediate purchase volume
Brand Lift is usually the most direct first test because perception can move before sales accumulate. Search Lift can add evidence that people moved from seeing the launch message to actively seeking information.
A cross-channel program with online and offline outcomes
Investigate geographic Conversion Lift where the account and data qualify. Its broader outcome coverage may be more valuable than demographic detail, provided the organization can tolerate greater statistical noise and supply consistent regional measurement.
Quick Recap
Pre-launch checklist
- Decision stated in one sentence.
- Single primary outcome defined and measured consistently.
- Treatment and control population or geographic units documented.
- Campaign, market, language, and account eligibility verified.
- Historical data and expected scale reviewed with the platform team.
- Budget and measurement period approved without relying on a universal threshold.
- Attribution reports labeled separately from incrementality results.
- Interpretation rule, uncertainty standard, and budget action agreed before results arrive.
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