OOH effectiveness and the butterfly effect
Opinion
Thinkbox’s Profit Ability 2 proved that all forms of advertising pay back, especially when sustained effects are measured. Now Route Research isolates how to get the best out of your OOH media investments. CEO Euan Mackay explains.
Media measurement is experiencing something of an identity crisis as it shifts from counting “how many” to focusing on “how much”, a shift evident in recent work by Les Binet and Will Davis on effectiveness.
This may sound like a small technical change to concentrate on outcomes, but there’s a classic “butterfly effect” taking place, where a small adjustment in one area creates a typhoon somewhere else – in out-of-home advertising (OOH).
To date, Route has only really had to contend with audience measurement for OOH, i.e. counting how many people are likely to see OOH ads in Britain. That’s no mean feat, given that we measure about 390,000 ads across different advertising ‘environments’: indoors and outdoors; above ground and below; some ads which are moving physically, and others that are dynamically changing and displaying ads for different durations.
However, the wind blowing through agencies and the trade press now is driven by high-profile ROI-centric econometric models such as Thinkbox’s Profit Ability 2, and that has caused a tornado in the OOH industry.
Across most of the optimised Profit Ability 2 models, OOH underwhelms.
The uncomfortable reason for this is very likely sub-optimal OOH audience data that has made its way into the models. This, of course, is no fault of the analysts who unquestionably acted in good faith – they can only use the data made available to them.
The challenge for Route and the OOH industry is to unlock the depth and variety of data available as inputs to these models, so we give ourselves the best chance of showing up in the outcomes.
We still hear that the availability of quality OOH data for modelling purposes is a perennial challenge for econometricians.
Source: JCDecaux, Talon, Nielsen – Location Matters for MMM
The truth is that granular data does exist.
Source: Route Research 2025 – 15-minute level impacts for digital OOH Roadside frames
Route can report audiences for every frame, every 15-minute period, each day of the week, and each month of the year. So rather than a lack of quality data, this appears to be an awareness/education issue.
To quote my former boss and the late IPA research director Denise Turner, “for a medium which prides itself on being highly visible, the OOH audience data is somewhat hidden from view”. This is something we, as Route and as the entire OOH industry, need to address.
Guidelines for navigating OOH audience data
Route has made the first move on stepping beyond counting the ‘how many?’. Whilst it will never be our remit to demonstrate or conduct OOH effectiveness studies, it is our role to independently contribute to the conversation on all aspects of OOH measurement.
As a starting point, we have created Best Practice Guidelines to help analysts navigate OOH audience data more effectively when building econometric and mixed-media models.
Route has worked with Anna Sampson and John Perella to consult with various stakeholder groups across the OOH and data modelling communities to set out a framework, published this week on the Route website.
It clarifies which Route data is available to analysts and provides guidance for the industry to ensure the right balance of Route data is provided for modelling purposes.
This emerges as vital in the performance of MMM (Marketing Mix Models) through the recent award-winning work Location Matters for MMM by JCDecaux, Talon and Nielsen, which demonstrates that including highly granular Route data in an econometric model can help improve the medium’s performance and ROI by 42%.
Beyond the initial best practice guide for MMM and a supporting FAQ document, Route and an industry task force plan to create a hub of supplementary materials to demystify and decode effectiveness in OOH.
This will particularly address the measurement of upper funnel campaigns that set out to build awareness and brand consideration, and which typically do not ‘show up’ in sales-dependent econometric models – yet are of paramount importance to underpin medium-to long term brand health.
The final step will be to build a mechanism that makes econometricians more self-sufficient and enables greater access to Route data for ingestion into data models. The potential scoping of a new API interface for this purpose is also underway.
In setting out on this course, we want to spread our newly formed butterfly wings to create our own tornado, ensuring that our data has a meaningful impact on OOH accountability and on how it is represented in outcome-based measurement.
Euan Mackay is the CEO of Route