Understand your Brand Lift measurement data

If you’ve already set up Brand Lift measurement, you can add helpful reporting columns to see how much your ads have influenced your audience’s positive feelings towards your brand or product. Generally, more responses are required in order to accurately detect smaller amounts of lift, because if there are few responses to Brand Lift surveys, it can be hard to determine accurately what influence your ads had on those responses. In this article you’ll learn what these metrics mean and how to add them to your reports.

View the following guidelines about how many responses are required to detect your lift.

  • For high-performing campaigns, you can expect to detect lift once you receive about 2,000 responses per lift metric.
  • At the recommended budget minimum, you can expect to detect lift once you receive 5,600 responses per lift metric.
  • If your campaign has not shown any lift after reaching 16,800 responses per metric, you may not be able to detect your lift.
Note: In general, you may start seeing lift results once your surveys reach 2,000 responses. Learn more about your Brand Lift measurement data

Lifted users

This column shows you the estimated number of users in a sample survey whose perception of your brand changed as a result of your ads. It shows the difference in positive responses to your brand or product surveys between the group of users who saw your ad and the group who didn’t. For example, your ads could result in a lift in consideration (or awareness, or ad recall) with regard to your brand or  product after seeing your ads. 

The “lifted users” metric doesn’t necessarily measure unique users, in that a user may become positively influenced or “lifted” more than once during the course of your campaign.

Note: Remarketing to lifted users is not supported.

Cost-per-lifted user

The average cost for a lifted user who is now considering your brand after seeing your ads. Cost-per-lifted user is measured by dividing the total cost of your campaign by the number of lifted users. You can use this metric to understand the cost to change someone’s mind about your brand in terms of brand consideration, ad recall, or brand awareness.

Absolute brand lift

This metric shows the difference in positive responses to brand or product surveys between the group of people who saw your ads (the exposed group) and the group withheld from seeing your ads (the baseline group). This metric is calculated by subtracting the positive response rate of the baseline group from the exposed group. Absolute brand lift measures how much your ads influenced your audience’s positive feelings towards your brand or product. For example, an increase from 20% to 40% in the positive survey responses between the two surveyed groups represents an absolute lift of 20%. 

Absolute brand lift and campaign performance

Absolute lift doesn’t necessarily reflect your overall brand lift performance. It is better to focus on a metric like cost-per-lifted user as the primary success metric of your campaign, because it factors in both reach and cost. See the following table:

Campaign

Cost Cost per 1,000 impressions (CPM) Reach Absolute lift Lifted users Cost-per-lifted user
Campaign 1 $100 $15 6,666 10% 667 $0.15
Campaign 2 $100 $5 20,000 5% 1,000 $0.10
Difference n/a 66%  200%  50%  60%  33% 

If you consider absolute lift only, Campaign 1 appears to perform better than Campaign 2. However, at the same cost, Campaign 2 drove 50% more lifted users, at a 66% lower CPM, and with a 33% more efficient cost-per-lifted user. 

Headroom lift

The impact your ads had on increasing positive feelings towards your brand or product compared to the positive growth potential your brand or product could have gotten. This metric is calculated by dividing absolute lift by 1 minus the positive response rate of the baseline group. For example, an increase from 20% to 40% in the positive survey responses between the exposed group and the baseline groups represents a headroom lift of 25%.

Relative brand lift

The difference in positive responses to brand or product surveys between users who saw your ads, versus users who were withheld from seeing your ads. This difference is then divided by the number of positive responses from the group of users who didn’t see your ads. The result measures how much your ads influenced your audience’s positive perception of your brand. For example, an increase from 20% to 40% in the positive survey responses between the two surveyed groups represents a relative lift of 100%.

Since survey responses can’t be collected for the entire exposed and the baseline groups, this data is extrapolated from the responses that have been collected, which gives you an estimated number within a certain range. Usually, the confidence interval is 90%, so you can expect that in 90% of the cases, the true lift number will be within that range (if you were to have reached everyone).

Baseline positive response rate

How often users who were withheld from seeing your ads responded positively to your brand. Use this metric to better understand how positive responses to your brand were influenced by general media exposure and other factors, not by seeing the ads in your campaigns. 

Exposed survey responses

The number of survey responses from people who saw your ads.

Note: If you see a low number in this column, that indicates that there aren’t enough survey responses yet. Continue running your campaigns and check back soon. 

Baseline survey responses

The number of survey responses from people who were withheld from seeing your ads.

Note: If you see  a low number in this column, that indicates that there aren’t enough survey responses yet. Continue running your campaigns and check back soon. 

Positive response rate 

Out of all the people who responded to the survey, this is the percentage of people who responded with a positive answer in regards to your product or brand. 

Confidence interval

This is the estimated range in which your relative brand lift and absolute lift estimates fall. For example, you may see your relative lift is 38.41%, the point estimate. In brackets you will see the confidence interval from at least 30.5% to at most 45.0%.

Modify your measurement data table columns

  1. Sign in to your Google Ads account.
  2. In the page menu along the left, click Labs, then select Lift Measurement
  3. Select the advertised object you’d like to view measurement data for. 
  4. Click columns icon Columns, and click Modify columns.
  5. Click Brand lift to open the panel, then select the columns you want to add or remove. 
  6. Click Apply
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