Marketers Dusting Off MMM With AI, ML Integrations

What’s old is new again, with a twist -- at least according to the Out of Home Advertising Association of America (OAAA), which commissioned a survey with Benchmarketing, a division of Omnicom Media Group, to analyze media mix modeling.

Advancements in artificial intelligence (AI), machine learning (ML), automation and data analytics continue to push MMM back into the tool chest of marketers, advertisers and agencies to measure and evaluate marketing performance.

Anna Bager, president and CEO of OAAA, thinks the data confirms that as marketers look to optimize media plans for next year, OOH should receive a higher allocation of the overall budget.

Nothing is certain, but advanced technology has pushed MMM back into the hands of the industry as a tool to measure the impact of a brand’s advertising across a variety of marketing channels.

This study relied on Vivvix and SMI as key data sources and examined historical ad-spending trends from 2017 through 2002 as part of the analysis. Hundreds of Omnicom U.S. brand MMMs of sales vs media activity by channel were used to produce anonymized and aggregated results.



Industries used to explore ad trends and spending included automotive, CPG food, and retail grocery. The data offers guidelines for allocating channel spend levels to optimize improvements in sales and brand metric scores.

In automotive, when looking at brand consideration in the automotive category, the analysis showed the greatest improvement in effectiveness when OOH budget allocation is optimized from 1% up to a high range of 14%. Automotive had the strongest improvement in driving brand awareness when the mix is optimized and OOH is used at a range of up to 19%.

The CPG food category sees the greatest improvement in brand purchase intent effectiveness when OOH budget allocation is optimized from 1% up to a high range of 15%, seeing a return on ad spend improvement of +24%.

The CPG food category also sees the highest improvement in increasing consumer brand spend once the media mix is optimized, seeing a return on ad spend improvement of +27% to +33%, depending on the size of the brand.

In retail grocery, the study’s brand index model showed the greatest improvement in effectiveness when OOH budget allocation is optimized from 8% up to a high range of 21%.

The analysis, which incorporates U.S. brand case studies from the Omnicom network, reached the same conclusions as previous studies on the value improvement generated by including OOH at significant levels in the media mix, showing a growth in performance.

The retail industry introduced media mix modeling (MMM) in the 1950s. It became popular in the 1960s and 1970s. Marketers used MMM to combine first-party data with external factors to forecast returns, optimize budgets, and simulate business scenarios. 

The findings released Tuesday by the OAAA found out of home’s share of media plan allocations leads to increased effectiveness, with measurable impact on sales, brand awareness, consideration, and purchase intent.

The ability to factor in variables such as budget spending and economic forces, marketers and advertisers also played a part.

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