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Glossary Out-of-Home

Likelihood to See (LTS)

A metric measuring the probability that a target audience will see an out-of-home advertisement based on location, timing, and foot traffic patterns.

Also known as: LTS score visibility rate OOH likelihood probability to see ad exposure likelihood

What is Likelihood to See?

Likelihood to See (LTS) is a predictive metric used in out-of-home (OOH) advertising to estimate the probability that a member of your target audience will actually view an advertisement placed in a specific location. Rather than assuming everyone passes a billboard or transit ad, LTS uses data-driven models to calculate realistic exposure based on audience movement patterns, dwell times, and site-specific variables.

Why LTS Matters

OOH advertising spend requires significant capital investment, making accuracy in audience delivery critical. LTS allows media planners to:

  • Validate site selection before committing budget to premium locations
  • Compare venues objectively using standardised metrics rather than footfall claims alone
  • Optimise campaign reach by identifying sites with highest exposure probability for specific demographics
  • Justify investment to clients with evidence-based predictions

In the competitive UK market, where prime billboard space commands premium rates, LTS helps agencies move beyond vanity metrics like "daily impressions" to meaningful audience delivery.

How LTS is Calculated

LTS combines multiple data sources:

  • Foot traffic data: Actual movement patterns from mobile data, footfall counters, or transport authority records
  • Dwell time: How long people typically spend in view of the ad
  • Audience targeting criteria: Age, demographics, and behaviour filters matched against passing audiences
  • Visibility factors: Sightlines, angles, obstructions, and competition from other ads
  • Temporal patterns: Rush hour peaks, weekend variations, seasonal changes

The output is typically expressed as a percentage (e.g., "65% LTS") representing the proportion of your target audience likely to see the ad during the campaign period.

When to Use LTS

LTS is particularly valuable for:

  • Geo-targeted campaigns promoting local events, retail locations, or regional services
  • High-value placements where budget justification is essential
  • Audience-specific buys targeting commuters, shoppers, or specific age groups
  • Campaign planning to estimate total reach before media buying
  • Benchmarking comparing OOH performance against digital channels

UK media agencies increasingly use LTS as standard practice, particularly when working with major advertisers or premium transit placements on London Underground, National Rail, or motorway sites.

LTS vs. Other OOH Metrics

While Opportunities to See (OTS) measures raw, unfiltered passing audiences, LTS applies realistic filtering for actual target audience visibility. This makes LTS more conservative but more commercially honest – a key distinction when pitching campaigns to data-conscious clients.

Best Practice

When evaluating LTS data, verify the methodology behind calculations. Reputable sources like JCDecaux, Clear Channel, and transport authorities publish transparent LTS models. Cross-reference with independent footfall data where possible.

Frequently Asked Questions

How is LTS different from OTS (Opportunities to See)?
OTS measures total people passing a location regardless of relevance to your target audience. LTS applies demographic and behavioural filters to estimate only how many people matching your audience criteria will likely see the ad, making it a more accurate reflection of actual campaign delivery.
What's a good LTS score?
Scores above 50% are generally considered strong for OOH placements. However, 'good' depends on context – premium high-street locations targeting broad audiences might achieve 70%+, while niche demographic targeting might see 30-40% LTS but deliver highly relevant exposure.
Can LTS predict individual ad views?
No. LTS is a statistical probability applied to population groups, not individuals. It predicts the likelihood that members of your target audience will see the ad on average, but cannot guarantee any specific person will view it.
How often should LTS data be refreshed?
LTS models should be updated annually or when significant environmental changes occur (new transport routes, major roadworks, venue renovations). Pre-campaign verification against recent footfall data ensures accuracy.

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