Opportunity to See says: "The conditions existed for this shopper to see the ad." The screen was on, the shopper was in the store, the timing aligned.
Likelihood to See goes further: "Based on available evidence, this shopper probably saw the ad." It takes the conditions and adds behavioral indicators, dwell time, proximity, transaction timing, screen placement data, to estimate a probability of actual exposure.
OTS is binary: the opportunity existed or it didn't. LTS is graduated: there's a 30% chance, a 60% chance, an 85% chance this shopper was actually exposed.
Why the distinction matters
Not all OTS is equal. A shopper who spent 45 minutes in a supermarket, passed the entrance screen three times, and transacted during peak hours had a much higher likelihood of seeing the ad than a shopper who did a 5-minute grab-and-go during a low- traffic period.
Both meet the OTS threshold. But their likelihood of actual exposure is very different.
Treating them equally in the measurement overstates exposure for the quick shopper and understates it for the browsing shopper.
LTS assigns a probability to each exposure event. That probability is calculated from:
Time in store. Longer visits mean more screen passes, more dwell near displays, more opportunity for the message to register.
Transaction proximity to screen playback. A transaction at 10:17 when the screen played at 10:14 is closer than a transaction at 10:45. Closer proximity means higher likelihood.
Screen location and shopper flow. Entrance screens have near-universal pass- through. Aisle-specific screens are only relevant if the shopper visited that aisle, and category purchase data can indicate whether they did.
Visit frequency. A shopper who visited the store three times during a weekly campaign had three LTS events. The cumulative likelihood of at least one exposure is higher.
LTS and measurement quality
When LTS replaces binary OTS in the measurement model, the quality of every downstream metric improves.
Reach becomes probabilistic. Instead of "X shoppers were in the store while the ad played" (binary OTS), you get "X shoppers had a weighted average 65% probability of exposure" (LTS). The effective reach is X × 0.65, which is more honest.
Attribution becomes weighted. When connecting exposure to purchase, LTS-weighted attribution gives more credit to high-probability exposures and less to low-probability ones. This reduces noise in the measurement and produces cleaner uplift estimates.
Optimization becomes smarter. When you know which stores, times, and screen positions produce the highest LTS, you can optimize delivery toward them. The same budget produces higher effective exposure.
The investment case
LTS requires more data infrastructure than OTS, shopper flow models, screen position mapping, temporal analysis of transaction patterns. It's not trivial to implement.
But the investment pays back through: - More credible reporting (brands trust weighted probabilities more than binary flags) - Better optimization (budget flows to high-LTS conditions) - Stronger pricing (LTS-verified impressions command higher CPMs than OTS estimates)
OTS says "it could be seen." LTS says "it was likely seen." And "likely seen" is worth money.
The bottom line
LTS moves in-store measurement from conditions to evidence. It's not proof of attention , nothing short of eye tracking provides that. But it's a significant step beyond OTS, adding behavioral indicators that produce a probability rather than a binary flag.
For RMNs seeking to differentiate their measurement quality and justify premium pricing, LTS is the next rung on the ladder. It takes the same data, transactions, screen schedules, store layouts, and extracts more insight from it.
The gap between "could have seen" and "probably saw" is where measurement credibility lives.
Related Reading
- Carrefour Retail Media Network: FMCG Advertising in Romania
- Eurocash Retail Media Network: FMCG Advertising in Poland
- Profi Retail Media Network: FMCG Advertising in Romania
- Outcome-Based Packaging: Selling a Result, Not a Channel
- Category Switching: The Hardest Retail Media Win to Prove
Ready to see how this works in practice?
Footprints AI helps brands and retailers measure what matters. See our customer success stories or get in touch to discuss your retail media strategy.
