Marketing Mix Modeling (MMM) – a topic the OOH industry will be hearing more and more about.

In Poland, we are increasingly focusing on OOH and DOOH audience measurement. We discuss methodology, data sources, and common standards for the entire industry. This is an important direction, because without reliable data, it is difficult to talk to advertisers about media effectiveness today.

However, during this year's World Out of Home Organization congress in London, another topic repeatedly came up that remains relatively little known in Poland. We are talking about Marketing Mix Modeling, or MMM for short.

One of the most interesting presentations on this topic was given by Henry Innis, co-founder and CEO of Mutinex. I must admit, I went to this presentation more out of curiosity than conviction that it would be one of the most important points of the program. After it ended, I had a completely different opinion.

Right at the beginning, Henry put forward a thesis that immediately caught the attention of the room. "The biggest problem with OOH is not that it is poorly measured. The problem is that it too often measures itself."

This is quite a provocative thesis, but the longer I listened to his presentation, the more I came to the conclusion that it does not apply exclusively to outdoor advertising. It actually applies to all media.

For years, each advertising channel built its own way of talking about its effectiveness. Television had its metrics. Digital had its own. OOH had its own (if it had any at all). Everyone measured something slightly different, and everyone could prove they were effective.

The problem is that an advertiser doesn't buy a billboard, a Google campaign, or a TV spot. An advertiser buys sales growth, market share growth, or brand awareness growth.

And it was around this that Henry Innis's entire presentation was built.

From reach to business impact.

Innis recounted the history of media measurement evolution over the past two decades.

First, reach and contact frequency dominated. Later, attribution models and the famous last click appeared, which for many years was something of a holy grail for digital marketing.

Over time, however, we began to see the limitations of this approach.

Because how do you attribute a sale to a customer who saw an ad on a DOOH screen in the morning, then heard about the brand in a podcast, watched a TV commercial in the evening, and two days later typed the product name into Google and made a purchase?

It is increasingly difficult to answer such questions using simple attribution models.

This is precisely why Marketing Mix Modeling is becoming increasingly important.

What exactly is MMM?

Simply put, MMM attempts to answer the question of what impact all marketing activities carried out by a brand have on business results.

The model simultaneously considers many variables: television, digital, OOH, radio, price promotions, seasonality, weather, competitor actions, and a number of other factors.

It then analyzes which of these actually influenced sales or other business indicators.

This is an important difference.

MMM does not attempt to track a single user. It is not interested in a specific click or a single conversion. It looks at marketing from the perspective of the entire company and the entire market.

This approach becomes particularly relevant today, as more and more user data disappears from the advertising ecosystem, and the effectiveness of traditional attribution models is increasingly questioned.

"Who owns the truth just changed hands"

However, the most interesting part of Henry Innis's presentation was not the technology.

It was the observation regarding the shift in power dynamics in the advertising market.

For years, media owners themselves defined how their results were evaluated. Each channel had its own metrics, methodologies, and narrative regarding effectiveness.

Today, advertisers are increasingly building their own effectiveness evaluation models.

As Innis said:

"Who owns the truth just changed hands."

This one sentence very well describes the direction in which the entire advertising market is heading.

Increasingly, media effectiveness is no longer decided by the media themselves, but by independent models analyzing the impact of all channels on a common business outcome.

What does this mean for the OOH industry?

For outdoor advertising, this is both a challenge and a huge opportunity.

In recent years, the industry has done tremendous work in audience measurement. New Audience Measurement (AUM) standards are being developed, new data sources are emerging, and digital media provide increasingly detailed information about actual ad exposure.

This is of great importance.

At the same time, in the world of MMM, merely measuring the audience ceases to be an end in itself.

The audience becomes one element of a larger puzzle.

Advertisers want to know not only how many people saw an ad, but also what impact it had on sales, brand awareness, or market share growth.

This is precisely why the work being done by WOO on global Audience Measurement standards is so important today. The better the audience data, the easier it is to include OOH and DOOH in models evaluating the effectiveness of the entire media mix.

A topic worth watching.

I have the impression that in Poland, we are only just beginning the conversation about MMM.

Meanwhile, in many developed markets, it is becoming one of the most important tools used by major advertisers for budget decisions.

This does not, of course, mean the end of audience research. Quite the opposite.

Audience Measurement and Marketing Mix Modeling are not competing approaches. They are successive stages of the same journey.

First, the audience must be reliably measured.

Then, one can begin to answer a much more difficult question:

What real impact did the advertising have on the client's business results?

Looking at the direction in which the global advertising market is developing, I have the impression that one of the most important industry discussions of the coming years will revolve around this very question.