Billboard attribution for D2C brands, with real CPAs.

What billboard attribution proves for a D2C brand

The word doing the work in every figure above is incremental. A store visit that would have happened anyway is not attribution, it is coincidence. A Blindspot lift study measures the increase in visits, web sessions or purchases among people who saw your screens against a matched group who did not, then divides the media spend by that increase. That is a harder, more honest number than a last-click, because it strips out the customers you would have won regardless. It is also the number a growth marketer should hold DOOH to, and the one Blindspot reports.

This matters for direct-to-consumer brands in particular. D2C growth has leaned on the feeds for a decade, and the feeds have become crowded, expensive and increasingly hard to measure as tracking degrades. Out-of-home reaches the same people away from the scroll, at a cost per proven action that, in the campaigns below, has run a fraction of a feed acquisition. The rest of this guide shows the measured numbers, explains precisely how each one is produced, and gives you the CAC math to slot it into a plan.

Efficiency here is not a claim but a measured number: a budget of any size buys proven outcomes rather than filler impressions, in the campaigns below as low as $0.82 per incremental store visit.

Cost per outcome, against the feeds

Measured Blindspot D2C figures, each against a matched control group, next to the typical paid-social benchmark. The benchmark column is the range D2C brands commonly report as a customer-acquisition cost on paid social. Read every Blindspot figure as an incremental action over control.

Blindspot figures are measured campaign results against matched control groups; your own numbers depend on category, price point and creative. The $15 to $40 range is the customer-acquisition cost D2C brands commonly report on paid social, quoted as a benchmark, not a Blindspot figure. See the public results in the case studies, and how a plan is built in book a billboard.

How DOOH attribution is measured

Every number on this page comes from the same four-part method. None of it is modelled guesswork, and each part answers a question a skeptical growth marketer would ask.

Verified plays: did the ad actually run? Every ad appearance on every screen is logged with a geo-tagged, time-stamped proof-of-play. You know the screen, the location, the exact second it played and that it played. This is the foundation, because attribution built on impressions nobody can confirm is attribution built on sand. A single worldwide tourism campaign on Blindspot logged 2,146,892 verified plays, each one auditable.

Foot-traffic lift: did it drive store visits? A panel of mobile devices seen near your screens during the campaign is compared with a matched control group of similar devices that were not exposed. The difference in store visits between the two groups is the incremental lift. Divide the media spend by that lift and you get the cost per incremental store visit, $0.82 in the D2C campaigns measured here.

Web lift: did it drive site visits? The same exposed-versus-control logic applies online. Site visits from audiences exposed to the screens are compared with a control group, and the increase is the web lift. In the campaigns measured, that produced a cost of $0.80 per incremental web visit, close to the store-visit figure because the mechanism is identical, only the outcome changes.

Sales impact: did it drive purchases? The hardest and most valuable read. Purchases from the exposed group are compared with the control group to isolate the online sales the campaign actually caused. That is how Adore Me arrived at $5.75 per incremental online purchase. It is a real acquisition cost, measured the honest way, and it is directly comparable to a blended CAC from any other channel.

The through-line is that Blindspot measures what happened, then attributes only the increase over a control. Proof-of-play removes the doubt about delivery; the lift studies remove the doubt about causation. Together they turn out-of-home from a brand-awareness line item you take on faith into a performance line item you can put in a spreadsheet next to everything else.

The brands, and their numbers

These are not projections. Each is a published result from a Blindspot campaign, measured against a control group, and each is a brand a D2C marketer will recognize.

Adore Me: $5.75 per incremental online purchase. The lingerie D2C brand ran out-of-home as a performance channel and measured the online purchases it caused against a control group. The result is the cleanest possible answer to the question a D2C team actually asks: what does a sale cost here? Against a $15 to $40 feed CAC, $5.75 per purchase is not a rounding error, it is a different order of magnitude.

Intimissimi: +53% store visits. The apparel retailer measured a 53% increase in store visits from audiences exposed to its Blindspot screens against control. For a brand with physical stores, that is the foot-traffic lift mechanism in action, and it is the same measurement that yields the $0.82 per incremental store visit figure.

Pepsi: +168% in-store sales. A 168% lift in in-store sales, measured against control, shows the method holds at the scale of a global brand and on the outcome that matters most, sales at the shelf. The mechanism is identical to a D2C store-visit study, pointed at sales.

UiPath: +104% web traffic. The software company more than doubled its web traffic from exposed audiences against control, the web-lift mechanism producing the $0.80 per incremental web visit figure. It also shows the approach is not limited to retail: a company whose conversion is a signup or a demo reads the web lift the same way a D2C brand reads a purchase.

Read together, these results answer the four things a growth marketer weighs before moving budget: it drives store visits (Intimissimi), it drives sales at the shelf (Pepsi), it drives web traffic (UiPath), and it drives online purchases at a real acquisition cost (Adore Me). The full write-ups sit in the case studies.

Fitting DOOH into a performance stack

For a growth marketer, the question is not whether out-of-home can be measured, it is where it sits in a plan alongside paid social, search and the rest. Three principles make that decision straightforward.

Compare incrementality to incrementality, not to last-click. The trap is to hold DOOH to a lift standard while giving the feeds credit for every last-click they claim. Do that and you compare an honest number against an inflated one. The fair comparison is a blended, incrementality-adjusted CAC across every channel. Measured that way, the $0.82, $0.80 and $5.75 figures above are already the incremental numbers, so they slot in directly, no discount needed.

Run the CAC math on the freed budget. Say a D2C brand is acquiring at a $25 blended CAC on the feeds and shifts a test budget to out-of-home that measures $5.75 per incremental online purchase. On a $10,000 test, the difference between a $25 CAC and a $5.75 CAC is the difference between roughly 400 acquisitions and roughly 1,700. Even if a real plan lands well above the best-case figure, the headroom is large, and every play is logged so you can see whether it held. The point is not that DOOH always wins, it is that you can now measure whether it did and reallocate on evidence.

Buy the hours your audience is out, not the whole day. Because Blindspot schedules each screen down to the hour, you concentrate a test into commuter peaks, evenings and weekends rather than paying for empty overnight hours. That is the same control that lets a brand cut waste from a traditional flight, and it is why a small D2C test budget can buy a meaningful number of useful plays. Set the schedule, read the lift, and treat the campaign like any other performance line: measured, adjusted, repeated.

The operational barrier used to be as real as the measurement one: booking out-of-home meant a sales call, a four-week flight and an agency in the middle. On Blindspot there are no minimums and no sales calls, a campaign can be live in 48 hours across 3M+ screens in 50+ countries, and Blinky, the free AI planner, will build a first plan from a one-line brief so a growth team can test out-of-home the way it tests anything else.