Maximizing OOH effectiveness through accurate MMM and attribution evaluation

By: Karin Baatsch-Deboulet (EVP, operations, Kinetic) & Mark Costa (chief digital officer, JCDecaux North America)

The only constant in life is change.”- Heraclitus

The pandemic produced constant changes in the advertising marketplace, and most of us found ourselves addressing challenges we had never encountered. With many Covid restrictions now lifted, consumers are returning to more normalized behaviors, but with a renewed appreciation for the outdoors. 42% of adult consumers report noticing Out of Home (OOH) ads more than pre-pandemic, and 45% report contextual OOH ads generate a higher level of interest. The greatest increases in these two metrics occur with adults 25–44, households with incomes of $100,000 plus, and in cities with populations of 1,000,000 or greater. Despite the challenges Covid produced, it actually enhanced the basic value proposition offered by OOH – a highly effective broadcast medium that occupies the real-world environment where consumers live, work, shop and play.

The opening quote from the ancient Greek philosopher, Heraclitus, best reflects the massive transformation the OOH industry has been going through, whether: (1) it’s the digitalization of advertising panels, (2) the adoption of data-driven platforms, products and capabilities that facilitate how OOH media can be planned, bought and measured (in many ways at parity with other mainstream digital media), or (3) the rise of programmatic digital Out of Home (DOOH) facilitating the inclusion of DOOH into programmatic omnichannel strategies.

The contextualization of messaging and addressability is a key attribute of DOOH. However, digitization has also had a remarkably important impact on printed OOH, too. Consumer data available through mobile devices has shifted planning from radius based to audience based, and can be applied across all OOH inventory, whether printed or digital. Even more importantly, the data ultimately becomes the fuel for performance measurement against all advertising channels.

Assessing the accuracy of OOH performance within MMM and attribution models

Digitization has further transformed OOH to a data driven, transparent and accountable medium: brands can plan by audiences across all formats, optimize daily for programmatic buys through improved industry impression measurement, and leverage anonymized mobile exposure IDs to facilitate measurement of performance outcomes such as footfall analysis or brand awareness studies (through statistical test and control methodologies). Digitization has put the performance measurement of OOH on par with other channels and in the hands of brands.

But while buy side planning and activation practices are now fully incorporating new data currencies, media mix modeling (MMM) and attribution practices with legacy models are not always reflecting and utilizing the latest data advancements, posing a risk to underrepresent OOH’s effectiveness and misrepresent the value of the overall media plan. The OOH industry has defined best practices that provide a framework for brands and their partners to engage the correct way to accurately incorporate OOH as a media contributor in MMM and attribution solutions.

Why is this important and what are the implications for OOH?

Inaccurate representation of OOH in MMM ultimately skews the overall marketing mix assessment and the way brands go about distributing their budget and the share attributed to OOH. This scenario also underscores the importance of getting OOH measured accurately in attribution models, to ensure the results will help drive the validation of the investment that brands demand.

With research studies showing increased effectiveness when OOH is properly evaluated, there is a huge opportunity for brands to better understand OOH impact as part of their media mix, and develop plans that deliver optimal results.

OAAA’s Best Practices for Marketing Mix & Attribution Models for Out of Home Media are a complete set of materials based on key MMM and attribution research learnings, and include specifications of data granularity that should be provided as inputs for the various MMM and attribution models.

What are the key advantages to adopting the best practices?

The comprehensive advantages of adopting the best practices pertain to the need for and benefit of data precision: (1) accurate and precise data inputs enable accurate and precise model outputs (results); (2) more granular inputs enable more granular models and more actionable insights. The best practices also reinforce alignment by ensuring OOH campaigns are designed to support and deliver on the brand KPI(s) which OOH is intended to influence. Proper evaluation of OOH cannot be accomplished if the KPI and OOH campaign design and execution are not aligned.

It is important for the OOH industry, brands, modelers, and data providers to collectively adopt these guidelines. Beyond optimizing effectiveness results by providing the framework to have the OOH channel evaluated more accurately, it is also enabling brands to understand OOH impact on overall media plans. This will provide brands the opportunity to consider OOH in many more media and marketing plans.

The only constant in life is change

Bringing Heraclitus back into the conversation, the data landscape is going through immense change today, and there will be even greater upheaval in the coming months. It appears the ad industry is going to witness the death of the cookie, and the industry is working through the impact of iOS changes and availability of anonymized mobile IDs. This change will impact MMM and attribution platforms as they will need to evolve techniques to accommodate the gaps created in mainstream digital media data.

As these platforms evolve, so will the OOH industry and its continued evaluation of MMM and attribution best practices. The current best practices are a starting point intended to help brands maximize their overall marketing investments when considering OOH media.

Article originally appeared in The Drum on February 8, 2022.