MMM-Based Media Planning

Kelly Sanderson
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Syndicated, panel-based data is often used as the foundation of channel selection when building a media campaign. It’s easy to see why, syndicated data offers planners rich insights into behaviours, media preferences and psychographic graphic makeup of the audience they are looking to reach with their messaging. What media do they spend the most time with? Are they more likely to be found on Reddit or Pinterest? How important is our ‘Made in Canada’ messaging? All these insights are important as a first step in the planning process, but when used in isolation and without a full understanding of the individual brand’s customer, they can sometimes lead media buyers down the wrong path.

What is syndicated data?

Syndicated data is data that refers to the general population and can’t be traced back to a specific individual. When working with panel-based data that has been gathered through balanced household surveys and/or panels. Participants are recruited online or via telephone and then are asked to answer questions on their awareness of certain brands, what media channels they spend time with, what products they use etc. The data is then aggregated and modelled back to the general population.

Panel-based data can tell advertisers a lot about Canadian's media and product usage and how those behaviours are trending over time, but there are a few drawbacks:

  1. It is not proprietary to the advertiser, so all brands in that category have access to that data as well.
  2. The format and questions are standardized. This is necessary for trending and data stability, but it means that buyers have little control over what information is collected and how questions are asked. 
  3. The data is based on a sample of the population, and how it is gathered may skew the results. For example, if survey recruitment and completion are done online only, it may miss portions of the population who don’t spend a lot online and skew towards people who just like filling out online surveys.

Supplementing with first-party data

To supplement syndicated data and get deeper insights into their specific brand, advertisers and agencies often look to first-party data. First-party data is any data that is gathered and owned by the brand and/or agency. Some of the most common sources of first-party data are:

  1. Brand/Campaign Awareness Studies – To better understand how the brand is perceived by the target audience or to gauge the reach of a media campaign, advertisers will sometimes commission a custom brand study. The study is often done by a third party and is conducted either online, by phone or both.
  2. CRM Platforms – CRM platforms can give advertisers rich insights into who their existing customers are, allowing them to create look-alike audiences to reach new ones. Data found in CRM platforms can include:
    • Customer location - Where do their sales/customers live? Do customers in certain areas spend more or convert more frequently than others?
    • Customer demographics – Based on their purchase patterns, advertisers can often infer some things about their customers' demographic makeup. Do they buy mostly male or female clothes/shows? Are they buying toys and/or kid’s clothes? If so, what age group are the kids in? etc.
  3. Search activity – Apart from driving users to their website, online search activity can also show advertisers where demand is highest for their category, product and/or brand.

The intelligence gathered from these first-party datasets can help brands better understand their current and prospective customers, but none of them is able to tell brands what marketing activities lead to that search or sale, and historically, none of them is able to integrate into other software for enhanced media planning easily.

At Arima we’ve developed a data science platform that can easily overlay all these first-party and syndicated data sets onto our privacy-first synthetic society to create the ultimate target customer profile. But that’s only half of it. We also allow users to seamlessly incorporate ROAS data from their marketing mix models (MMM) into the campaign planning process.

Leveraging Marketing Mix Modelling (MMM) Data


If an advertiser has an up-to-date, geographically relevant MMM (no working with Global’s hand-me-downs!), they can combine the ROAS data found within syndicated data and proprietary first-party data to create a truly tailored media campaign.

Let’s look at how the process could work in the Arima platform.

To get started, users first define the audience segment they are looking to reach by combining audience variables from multiple data sources; this can be data from the Arima Synthetic society, industry-recognized syndicated data sources and any of the first-party data we mentioned above.


Once the audience segment is defined, advertisers can build custom media plans by:

From there, multiple campaign scenarios can be evaluated to see which combination results in the optimal reach/frequency against the established audience segment and campaign objective.


Having brand-specific ROAS data from an up-to-date MMM act as a factor in the media planning process has major benefits to the media planner.

Instead of based media selection solely on which channels syndicated data tells them the audience uses or based on which digital channels they see working in their MTA analysis, MMM data is proprietary to the advertiser and is cross-channel.

For example, a syndicated data source may tell an advertiser that their target audience over indexes in time spent listening to terrestrial radio, is an average TV viewer and light Facebook user. Based on this alone, a media planning platform may recommend the advertiser break out their budget to 60% Radio, 30% TV, and 10% Facebook. But if previous MMM ROAS data is factored into the planning process and it shows that, for this specific brand, radio has historically had a ROAS of $35, TV $45 and Facebook $50, it would change the media recommendation to increase spending allocations to Facebook where ROAS is higher.


This is a very, very simple example, but it illustrates the power MMM data can have when developing media campaigns.

Bringing it all together


By consolidating all available data sources into one platform, advertisers and agencies can get a true 360-degree view of their existing and prospective customer base and insight into what media platforms and tactics work not only for their industry/category but for their brand specifically. This creates a solid foundation for media campaign development, analysis and continuous optimization, which would otherwise be unavailable.



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