How do you prove that a programmatic DOOH campaign worked? Decide before launch which question you need answered, from "did the ads run?" up to "did they make money?", then pick the measurement method that answers it, and always compare the people or places that saw the ads with similar ones that did not.
This guide sets out the methods in order of difficulty, shows what each can and cannot prove, and works through an ROI calculation that shows how easily results are overstated. If you want the background on how pDOOH impressions are counted in the first place, read what pDOOH is and how it differs from online programmatic first.
Four levels of measurement
It helps to think of measurement as a ladder. Each level answers a harder question and needs more planning.
- Delivery: Did the ads run where, when and as often as agreed?
- Response: Did people do something traceable after seeing them?
- Lift: Did more of it happen than would have happened anyway?
- Return: Was the extra activity worth more than the campaign cost?
Most campaigns should cover levels 1 and 2 as a minimum. Levels 3 and 4 are where you prove value, and they need a comparison group.
Level 1: delivery metrics
- Proof of play. A log showing each time your creative played, on which screen and when. This is the basic check that you got what you paid for.
- Impressions. In DOOH these are estimates: plays multiplied by an audience figure for that screen and time. Ask which data source the media owner uses and how often it is updated.
- Effective CPM. Total cost divided by impressions delivered, multiplied by 1,000. Useful for comparing screens, hours and venue types within a campaign. Our DOOH CPM calculator does the arithmetic.
- Delivery against plan. Impressions and spend by screen, hour and day versus what was booked, so you can spot under-delivery early.
These numbers tell you the campaign ran properly. They do not tell you whether it worked. Getting the setup right before launch matters; it is one of the problems covered in our list of programmatic DOOH campaign mistakes that waste budget.
Level 2: response metrics
- QR scans and short URLs unique to the screens.
- Offer codes redeemed in store or online.
- Branded search and direct site visits from the areas the screens cover.
- App installs or sign-ups by region.
These are cheap and easy to set up, and they give a floor: everyone who used the code saw the ad. Their weakness is that most people who see a screen will never scan or type anything, so response counts understate the effect, and some of the people who responded would have bought anyway.
Level 3: lift, the step that proves cause
Lift compares an exposed group with a similar unexposed group. The difference between them is the effect you can credit to the campaign. There are three common ways to build the comparison.
| Method | How it works | Best for | Watch out for |
|---|---|---|---|
| Matched areas (geo test) | Run the campaign in some areas or stores and not in similar ones, then compare sales or visits against each area's own baseline | Retailers and chains with sales data by location | Areas must be genuinely similar; local events can distort one side |
| Device-based footfall study | A measurement partner compares store visits by mobile devices seen near the screens with similar devices that were not | Store visits when you cannot split areas cleanly | Relies on a sample of devices that share location; results are modelled |
| Brand lift survey | People who were likely exposed and people who were not answer the same questions on awareness and consideration | Brand campaigns where sales are not the immediate goal | Survey samples can be small; exposure is usually inferred from location |
A published example of the exposed-versus-unexposed approach is UGG's programmatic DOOH campaign in London. According to Vistar Media's case study from April 2024, research by JCDecaux UK compared people known to have visited Westfield London during the campaign with those who had not, and reported a 32% rise in awareness, a 7% rise in purchase consideration and a 60% rise in awareness of the brand's link with its new clothing line. As with any vendor case study, the method is summarised rather than published in full, but the structure, an exposed group against a comparison group, is the right one.
Location-based methods have become harder in recent years. Apple's App Tracking Transparency rules, for example, require apps on iOS 14.5 and later to get the user's permission before tracking them or accessing the device's advertising identifier. That shrinks the pool of devices available to footfall studies, which is one more reason to keep a geo test or sales comparison in your toolkit. Our guide to how location intelligence platforms measure footfall goes deeper on the device-based method.
Level 4: return on investment
Return on investment compares the value of the extra outcomes with what you spent:
ROI = (incremental value − media cost) ÷ media cost
The word that matters is incremental. Counting every visit or sale from exposed people, rather than only the extra ones, is the most common way DOOH results get overstated.
Illustrative example: total versus incremental
Illustrative example. All figures are hypothetical, chosen to show the arithmetic.
- A footfall study matches 200,000 exposed devices with a similar unexposed group.
- 2.4% of the exposed group visited a store during the campaign, against 2.0% of the unexposed group.
- Each store visit is valued at ₹500, based on average spend and conversion. The media cost was ₹3,00,000.
| Counting all exposed visits | Counting incremental visits only | |
|---|---|---|
| Visits credited | 200,000 × 2.4% = 4,800 | 200,000 × (2.4% − 2.0%) = 800 |
| Value at ₹500 a visit | ₹24,00,000 | ₹4,00,000 |
| ROI | (₹24,00,000 − ₹3,00,000) ÷ ₹3,00,000 = 700% | (₹4,00,000 − ₹3,00,000) ÷ ₹3,00,000 = 33% |
Both columns use the same data. Only the right-hand one is honest, because most of the 4,800 visitors would have come anyway. It also gives you a planning target: to break even, the campaign needs ₹3,00,000 ÷ ₹500 = 600 incremental visits, which on 200,000 matched devices is a lift of 0.3 percentage points.
Two more cautions. The value of a visit is itself an estimate, so state how you calculated it. And a footfall study's device counts are a sample, so ask the provider how results are projected to the wider population and how confident they are in the difference.
Choosing a method for your budget
- Small campaign or first test: proof of play, plus an offer code or QR code, plus a simple comparison of sales in covered and uncovered areas.
- Retail or restaurant chain: a matched-store geo test using your own till data, which needs no third-party data at all.
- Brand campaign: a brand lift survey, agreed with the media owner or platform before launch.
- Larger, always-on programme: repeated geo tests or a footfall study, and in time a media mix model that estimates each channel's contribution across many campaigns.
Moving Walls covers several of these approaches, including reach and frequency, footfall and brand lift studies, in its article on measuring DOOH performance from impressions to in-store sales.
Using the data during the campaign
Because programmatic campaigns can be changed while they run, delivery and response data are worth reviewing weekly:
- Shift budget toward screens and hours with better response rates or lower effective CPM.
- Pause hours that deliver impressions but no response, if you have enough data to judge.
- Rotate creative and compare response by version.
- Keep the comparison group untouched, or you lose the ability to measure lift at the end.
That last point is easy to forget. Optimising toward the areas that respond best is sensible, but if you start running ads in your comparison areas, the test is gone.
A pre-launch measurement checklist
- Write down the one business question the campaign must answer.
- Pick the level of the ladder that answers it.
- Choose and set aside the comparison group or areas.
- Record at least two weeks of baseline data for both groups.
- Agree how proof of play and impressions will be reported, and how often.
- Set up codes, QR links or tracking URLs unique to the campaign.
- Decide in advance how you will value an outcome and what result counts as success.
For how these measurements fit into planning and buying, see our guides to how programmatic DOOH buying works and the advantages of programmatic OOH, and for campaigns that published their results, seven real programmatic DOOH campaigns worth studying.