Brand Lift Studies: measuring the effect of advertising campaigns on the brand
What to do with the results: practice, not just numbers
Getting a report with a percentage point increase — half the job. The other half is understanding what to do with it. If the study showed a failure (Ad Recall increase below 3 p.p. or negative dynamics in Purchase Intent), don’t rush to blame the measurement methodology itself. More often, the problem lies in one of three places:
- The creative doesn’t stand out from the surrounding content — the audience saw the ad but didn’t remember the brand;
- The frequency of impressions is too low to form memorability — one or two contacts rarely produce an effect;
- The message conflicts with the already formed perception of the brand, and the audience 'rejects' it as not matching.
Practical conclusion: if Ad Recall is high but Purchase Intent isn’t moving — the problem isn’t with placement, but with the offer itself. Such a metric divergence is a signal for a brief on a new creative, not for increasing the budget for the same ad.
What increase to consider a good result
There is no universal standard — the benchmark depends on the category and how familiar the audience already is with the brand. Relying on 'a good result = 10%' without market context — is a common and crude error in interpretation.
| Category | Normal increase | Comment |
|---|---|---|
| FMCG | 4–8 p.p. | High competition, audience overloaded with ads |
| Automobiles | 6–12 p.p. | Expensive product, higher engagement in viewing |
| Financial services | 3–7 p.p. | Low trust in the category as a whole, increase is harder to achieve |
Generally speaking, an increase of 5–10 percentage points is a good result for most categories, and anything above 15 percentage points can be considered an excellent campaign outcome. However, comparing an automotive brand with an FMCG product on the same scale is methodologically incorrect: categories have different baseline awareness and different attention thresholds for their audiences.
How to choose metrics and avoid misinterpreting results
Google recommends including Ad Recall in any study — this metric is often underestimated, although it reflects the direct impact of advertising on audience memory. The following set of metrics depends on the campaign goal:
- If the goal is to reach a new audience, priority should be given to Brand Awareness and Ad Recall;
- If the brand is already known and the goal is to move the audience toward purchase — focus on Purchase Intent and Favorability;
- If a new product is being launched under an established brand — it is advisable to add the Message Association metric to check whether a specific message is being understood.
The main mistake in interpretation is looking at one metric in isolation from the others. An increase in Awareness coupled with a decline in Favorability means that the brand has become more noticeable, but this noticeability is working against it. This is not a reason for panic, but a clear signal to reassess the tone of communication.
What distorts results
Virtual auction and division into test and control groups — a reliable methodology, but not immune to external noise. The purity of the results is affected by:
- Parallel campaigns on TV or in other channels — the control group may see the brand not in your channel, but passing by it, and the difference between the groups becomes blurred;
- Competitor activity during the same period — a major competitor launch can 'draw' audience attention and reduce the increase in Awareness, regardless of the quality of your creative;
- Seasonality and news environment — mentions of the category in media distort the baseline level of brand knowledge in the control group;
- Too narrow a sample — when the number of responses is less than needed for the target lift (for example, fewer than 2,000–4,100 responses depending on the lift size), the result is statistically unreliable, and drawing conclusions from it is risky.
Brand Lift or internal data: what to choose
Brand Lift and CRM or website data measure fundamentally different things, and comparing them is not quite correct.
| Parameter | Brand Lift | CRM / website data |
|---|---|---|
| What it measures | Audience perception and memory | Behavior of users who have already clicked |
| Covers those who did not click | Yes | No |
| Speed of data acquisition | 3–14 days (deep dive — 9–14) | In real time |
| Minimum threshold | 2 million unique users reached | Depends on website traffic |
The correct combination is to use both layers of measurement. CRM will show that advertising led to a sale, Brand Lift will explain why it did or did not lead to a sale: was the message effective or not, did the brand stick in the minds of those who have not yet purchased yet.
Is Brand Lift B2B needed for niche brands
Technically, Brand Lift is available to any brand, but economically it is not always justified. The minimum reach requirement — at least 2 million unique users — already excludes a portion of narrow B2B niches, where the entire target audience is below this threshold. The minimum budget for a ten-day study in Google Ads varies from $5,000 to $60,000 USD depending on the country and the number of questions (1-3 questions), which for a niche product with a long sales cycle may be an excessive cost for the insight.
For B2B with narrow audience, surveys via professional panels or own contact database are more reasonable — they are more expensive per respondent, but more accurate for small samples. Brand Lift is justified where the campaign is aimed at mass reach: even a niche brand entering the broad digital space can use the tool if it is ready to reach the minimum reach and budget.
How the study is organized internally
The methodology is based on dividing the audience into test and control groups through a virtual auction: a portion of ad impressions is 'given' to the control group artificially to ensure comparability. Users from both test and control groups are invited to participate in the survey, forming a sample: half saw the ad, half did not. Then the responses of the two groups are compared based on selected metrics, and the difference in percentage points is the desired lift. A standard study takes 3 to 14 days, while the deep dive version takes 9–14 days and collects three times more responses, which reduces error and allows results to be segmented by audience segments.
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