Use this page to decide whether a campaign can be measured with a lift study before you design one. It takes about ten minutes and needs no data you do not already have on the media plan.
For how the resulting study is sized, see Sample size and precision. For how lift is computed, see Lift measurement methodology.
What you need
- The audience definition the campaign is targeting
- Planned impressions and average frequency, or planned reach against that audience
- The size of the audience, if you are working from impressions
The target is 500 exposed respondents and 500 matched control, derived here. The whole calculation works backwards from that number.
Step 1: Find the qualifying rate
Look at the audience definition and decide whether a sample provider can target it or whether the survey has to screen for it.
Targetable attributes are ones the provider selects on directly: age, gender, region, household income, profiled demographics. "Women aged 18 to 34 in New York" is a targeting instruction, so nearly everyone entering the survey qualifies.
Screened attributes are ones the provider cannot select on, so every respondent is asked and most are turned away. "Marketing decision-makers at SaaS companies" is a screening instruction, and may cost thirty entrants or more for each qualifying respondent.
Take a planning figure from the table, then confirm it with feasibility against the real definition:
| Audience type | Qualifying rate |
|---|---|
| Targetable demographics and geography | 80–95% |
| Category buyer or recent purchaser | 30–60% |
| In-market intender | 10–20% |
| Niche professional or B2B role | 1–5% |
Incidence varies by market and by provider, so treat these as a starting point rather than an answer.
Step 2: Find the reach rate
Reach is the share of the qualifying audience the campaign reached.
If the media plan states reach against the same audience the survey will screen to, use that figure and go to step 3.
Otherwise, derive it. Divide impressions by average frequency to get unique people, then divide by the size of the audience:
Ten million impressions at an average frequency of three reaches 3.3 million people. Against a 20 million person target that is 17%. Against the US adult population it is 1.3%.
Check which audience you are dividing by. The reach rate that belongs in this calculation is reach within the audience the survey will sample, not reach against the general population.
Step 3: Calculate the entrants
Multiply the two rates and divide into 500:
Or read it off:
| Audience type | Reach 3% | Reach 10% | Reach 30% |
|---|---|---|---|
| Targetable demographics and geography | 18,500 | 5,600 | 1,850 |
| Category buyer or recent purchaser | 37,000 | 11,100 | 3,700 |
| In-market intender | 111,000 | 33,300 | 11,100 |
| Niche professional or B2B role | 556,000 | 167,000 | 55,600 |
Then judge the number against the study budget. A targetable audience at 10% reach needs 5,600 entrants and is routine. A niche B2B audience at 10% reach needs 167,000 and is not.
Step 4: If the number is too large
Work through these in order. The first two cost nothing.
Align the survey's screening to the campaign's targeting. A campaign reaching 4% of a national population may reach 25% of the segment it targeted. Screening the survey to that segment raises the reach rate without changing anything else.
Check the frequency. Impressions served to someone already reached add cost without adding a measurable person. If frequency is high, reach is lower than the impression count suggests, and a frequency cap on the remaining flight improves measurability.
Recruit from inside the ad unit. An in-ad survey produces exposed respondents directly instead of screening for them. Note in the reporting that exposed and control then come from different frames.
Work from a first-party list where the client can identify the audience in their own CRM. This turns an incidence problem into a response-rate problem.
Measure the portfolio instead of the campaign. See the next section. On screened audiences this is usually the largest single saving available.
Lower the target. 300 exposed instead of 500 detects an 11-point difference instead of 8.9. If the question is directional, that may be enough.
If none of these bring the number into range, the honest conclusion is that this campaign is not measurable this way, and it is better reached at the planning stage than after fielding starts.
Measuring several campaigns at once
Exposure to one campaign does not consume the respondent. Each respondent is exposed or unexposed independently for every campaign in the study, so one fielded sample produces a separate exposed cell for each. Sizing for one campaign sizes for all of them.
Ten campaigns each reaching 5% of a shared audience:
| Approach | Respondents fielded |
|---|---|
| Ten separate studies at 500 exposed each | 100,000 |
| One study covering all ten | 10,000 |
The saving scales with the number of campaigns, and it is largest on screened audiences because the screening cost is paid once instead of once per campaign. The 167,000 entrants in the B2B row above buy one campaign measured separately, or ten measured together.
A second effect: with ten campaigns at 5% reach, around 40% of respondents are exposed to at least one, against 5% for any single campaign.
| Campaigns in the study | Share of respondents exposed to at least one |
|---|---|
| 1 | 5% |
| 3 | 14% |
| 5 | 23% |
| 10 | 40% |
| 20 | 64% |
Check three things before assuming the saving:
Every respondent is asked about every campaign. The full saving applies while the questionnaire covers all campaigns for all respondents, which is practical up to roughly five to ten. Beyond that, sub-sample the items to keep the interview manageable and expect each campaign's base to fall in proportion. A respondent seeing five of fifty brands gives each brand a tenth of the sample. See Reaching low-incidence audiences for the questionnaire mechanics.
Cross-exposure is handled where campaigns compete. A respondent unexposed to campaign A but exposed to competitor B is not a clean control for A if both move the same outcome. Record cross-exposure and use it as a control variable.
The audiences overlap. Campaigns targeting different populations do not share a sample frame, and combining them means screening for the union, which erodes the saving.
One further benefit worth planning for: a combined study measures every campaign against the same respondents in the same window on the same instrument, so cross-campaign comparison no longer depends on studies fielded at different times against different samples.
What the calculation leaves out
Match rate. Not every reached respondent joins to an exposure record. Retention windows, IP churn on mobile networks and the quality pipeline remove records before the join. Treat the output as a floor and carry a buffer.
Accumulation during fielding. Reach at the point a respondent enters the survey is lower than end-of-campaign reach. Use mid-flight reach rather than the planned final figure.
Variation in qualifying rates. The step 1 table is a planning aid. Run feasibility against the real definition, market and provider before committing. See MCP and OpenAPI integration.