The quest to measure the effect of media has never been more intensive. Although econometric models excel at demonstrating shifts in short term response, they are not ideal for quantifying the effect on longer term objectives, which begs the question of how best to measure effectiveness throughout the funnel.

At Route, we’ve focused mainly on audience measurement for OOH. Although our data can be output at highly granular levels, it is typically accessed by specialist planning agencies and, to date, access to the data for econometricians has been a challenge. 

OOH advertising photo

The outcome is that the OOH data feeding into econometric models tends to be very high level – often total audience for the campaign, or worse, total spend on the media. The result is a single number that gets apportioned equally across the campaign duration. This is not good news for an econometric model which thrives on pattern-matching periods of variance. The outcome is often that the OOH input data does not correlate with movement in sales, resulting in disappointing attribution in the model results.

So Route has produced some best practice guidelines which encourage people to:

  1. Understand campaign objectives and establish the role of OOH within this
  2. Formulate the data request taking account of the nature of the inventory bought, the regionality, the environments, duration etc
  3. Review the data upon arrival to confirm the patterns match the campaign spend, identify any missing data, consider any unmeasured inventory
  4. Address any data gaps and create blended ROI metrics where possible
  5. Evaluate the results in context – remember lag effects, consider creative, regional distribution, split between digital and poster formats etc


In support of this, there is also a separate Frequently Asked Questions document which is set out to help people navigate through their econometric models and better understand some of the key considerations in appraising the outcomes.

Incrementality Testing

Best Practice