To evaluate whether marketing is affecting a brand’s reputation, measure the perception the campaign is meant to change—such as trust, favourability, perceived quality, or consideration—before and after exposure. A before-and-after change alone does not prove the campaign caused it. For stronger campaign-level evidence, compare survey responses from people exposed to the campaign with responses from a suitable unexposed control group, then interpret the result alongside ongoing brand tracking and other plausible influences.
Choose the reputation outcome before measuring
“Reputation” is broader than whether people remember an ad or click it. Decide what change would matter for the decision at hand, then select measures that reflect it. Possible measures include awareness, favourability, trust, perceived quality or value, consideration, satisfaction, recommendation, and word of mouth. The DMA’s 2025 Guide to Best Practice Measurement treats reputation and consumer impact as part of brand measurement and discusses measures such as awareness, satisfaction, and purchase intent.
Delivery figures—including reach, impressions, clicks, and engagement—show whether people encountered or interacted with marketing. They are not substitutes for reputation measures. Google Ads describes Brand Lift as measuring goals such as ad recall, brand association, awareness, and consideration rather than relying only on delivery metrics.
Write down the exact question the measurement needs to answer. For example: “Are target consumers more aware of the brand after viewing this video ad?” or “Did the ad move people to consider the brand or product?” Those questions point to specific outcomes; a general claim that “reputation improved” is harder to test.
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Use a comparison that can support the claim
For a campaign-level effect, compare exposed and control groups
A campaign lift study compares perceptions reported by people exposed to advertising with perceptions reported by an unexposed control group. Amazon describes its Brand Lift method as surveys issued to both groups to measure advertising’s impact on customer perceptions. The design is useful for assessing campaign-related change, but the quality of the conclusion depends on the control group, the survey, and the audience represented.
Randomized experiments can provide stronger causal evidence than simply asking whether a measure went up after launch. In Methods for Measuring Brand Lift of Online Ads, presented at the 2018 Joint Statistical Meetings, Google Research authors Rachel Fan, Tim Hesterberg, Ying Liu, and Lu Zhang write: “We estimate the causal effect of ads using randomized experiments.” The paper also discusses challenges such as survey response bias and mismatches between intended and actual treatment.
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For reputation over time, use continuing brand tracking
Ongoing brand tracking answers a broader question: how brand health changes over time, across audience segments, and relative to competitors. It can show whether a campaign period coincides with a broader shift, but does not by itself isolate one campaign’s effect. Use campaign-specific lift studies when the decision is about a particular campaign, and continuous tracking when the decision concerns reputation over time.
Why before-and-after movement is not enough
If a survey measure changes between a pre-campaign baseline and a later measurement, that is evidence of movement, not proof of cause. Other campaigns, public relations, product or service changes, pricing, competitor activity, category shifts, and major news may also affect perceptions. A suitable exposed/control comparison helps separate campaign-related change from some of these influences; an ongoing time series helps put the change in context.
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Run the evaluation consistently
- Set the baseline before launch. Record the selected reputation measures and the population, geography, and audience the results are intended to describe. Retain a comparable unexposed group if the study design allows it.
- Keep the measures comparable. Repeat the same question wording and use sufficiently consistent target populations, geographies, and timing. If those change, document the change so readers can tell whether the figures are directly comparable.
- Record the study scope. Note the campaign and platform, exposed and control audiences, survey window, response counts, exact metric, and any reporting threshold. A survey lift result describes reported perceptions for its sampled population and questions; it does not automatically establish a brand-wide effect.
- Check what else changed. Record concurrent marketing, PR, product or service experience, price, competitor activity, and significant news that could plausibly affect the same measures. Treat these as context for interpreting results, not as causes proven by the survey.
- Separate perception from behavior. Consideration and purchase intent are survey responses, not observed purchases. If the business question concerns sales or profit, add behavioral or commercial evidence rather than inferring those outcomes from a positive lift or reputation measure.
Interpret the size and uncertainty of the result
Report the absolute difference between exposed and control responses, the number of responses, the audience, metric, survey period, and whether the provider’s reporting threshold was met. Do not describe a directional estimate as a confirmed effect, and do not read “not enough data” as evidence that the true effect is zero.
Smaller detectable effects require more responses. Google Ads says results can fluctuate while a study is underway and recommends using the final report. Its current help page gives platform-specific guidance of about 2,000 survey responses per lift metric for high-performing campaigns and 4,100 at the recommended budget minimum. It also says 16,800 responses per metric without detected lift may mean the study could not detect lift under its conditions—not that the true effect is necessarily zero. These figures are Google Ads guidance, not universal sample-size rules.
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Display & Video 360 gives different provider-specific guidance: 1,200–2,800 responses for a detectable absolute lift above 4%, and 45,000–180,000 responses for a 0.5% lift. The large difference illustrates why a sample-size figure should be read with its platform, design, metric, and detectable effect rather than applied to every brand study.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare study options on fit, not just speed
Platform lift products are not interchangeable. Before choosing one, compare the design and practical fit:
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- Control quality: How are exposed and unexposed groups formed, and how well does the design support a causal interpretation?
- Audience and coverage: Does the surveyed population match the people whose reputation perceptions matter? Check geography, representativeness, and available audience segments.
- Metric and wording: What exact question is asked, and does it measure the intended outcome—such as trust or consideration—rather than a nearby but different concept?
- Response volume and sensitivity: What response count and detectable change apply to this provider’s study? Small effects may need substantially more responses.
- Eligibility and campaign fit: Check supported markets, account access, campaign type, budget, inventory, and any exclusions before planning around a product.
- Timing: Check the study window and when results are expected relative to the campaign. Amazon Ads says Brand Lift results can be available as soon as 10 business days after submission, subject to advertiser and campaign conditions.
- Long-term context: If the question is broader than one campaign, consider continuous tracking, historical comparison, segmentation, and competitor benchmarks. The DMA guide describes these as elements of ongoing brand measurement.
Availability rules can constrain the choice. Amazon’s Brand Lift page lists supported advertiser markets and excludes Sponsored Products; Google Ads says Brand Lift is not available to every account; and Display & Video 360 documents study windows that vary by inventory type. Confirm the provider’s current rules for the specific account, market, and campaign rather than assuming a study is available.
What a lift study can—and cannot—establish
A lift study estimates change in specified survey responses among the population and during the period it measures. Its result should be described with the metric, audience, timeframe, response count, and comparison used. It does not automatically establish that the entire public’s reputation of a brand changed, nor does a positive survey result alone show increased sales, profit, or customer behavior.
Likewise, a study that does not detect lift is not proof that marketing had no effect. The effect may be smaller than the study could distinguish from noise, or the study may not have obtained enough responses. Interpret the finding according to the provider’s stated thresholds and the study design.
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