Improved Radio Data Makes Media Mix Modeling More Effective.

With advertisers now having access to as-run radio deliveries vs. planned data, and with Media Monitors’ DMA expansion from 106 to 250 markets, these and other factors have improved media mix modeling performance.

“New ‘as run, actual’ radio delivery data should be considered as a ‘trend break,’” advertising consultant John Fix says in Westwood One’s blog. “Don’t let history be the best predictor of the future. Modelers, loosen up dependencies on priors to allow the model to find something with the better, improved radio data.”

Fix notes that “the radio and MMM conversation has changed significantly, as tangible steps have been taken to facilitate changing the narrative” by using as-run data, understanding that DMA-level delivery matters, and planning for adequate GRPs.

Smoothing the transition to more reliable marketing models is the partnering of broadcasters with Media Monitors, software platform Act1 and Nielsen for a methodology providing as-run radio data for buys.

“Media Mix Modeling requires weekly as-run GRPs, and the radio industry can now provide detailed, weekly data,” Fix says. “Marketers and agencies can reach out to iHeart and Westwood One to obtain as-run campaign deliveries for the entire radio campaign.”

Media Monitors’ expansion to 250 markets, Fix says, “changes the story that radio data is too sparse to be useful. Weekly level, as-run radio data will look significantly different than weekly planned data due to natural variation in delivery. This actual variation creates an improved signal for modeling and no longer looks like monthly, planned levels merely divided by the number of weeks in the month.”

Given these changes, Fix recommends that MMMs only use as-run radio data, and it should be made clear to MMM providers that this data differs significantly from historical. In fact, the methodology change should treat radio as a “new media channel.”

“Radio broadcasters believe that very few MMMs have utilized as-run, weekly data and few, if any, MMMs have used as-run delivery data at the DMA level,” Fix says. “Advertisers and modelers have very little experience using weekly radio as-run data for MMM. This change, as well as added granularity in the data, should mean that historical benchmarks for radio performance should be reconsidered.”

Like all transitions, every party involved in MMM should be made aware of the importance of new vs. historical data. “As-run radio data has rarely, if ever, been modeled by MMM providers,” Fix says. “It is appropriate for the modeler to weaken reliance on historical norms, especially if brand specific, in order to allow the model to use this ‘new’ dataset.”

Likewise, partners in any project should “acknowledge that there is potentially no prior experience with this new as-run data set,” Fix says. “Discuss the potential that this MMM model might produce a result that is different from previous models of the same brand for radio.”

With these changes to MMM, it should be noted that everyone involved brings unique knowledge to the table. 

“Specific to radio, broadcasters should be included to ensure that the data provided reflects the delivery, and they can also review the media briefing to suggest areas where breakouts (on-air read vs. recorded, duration, radio programming format) could be optimized,” Fix says. “Advertisers contribute their knowledge of the campaigns run and share the main business questions they have in order to form the data breakouts and create meaningful granularity. Modelers should be transparent with the advertiser, acknowledging the role that historical norms and prior models play as well as establishing the rules for the current model, especially when new data or new media channels are introduced.”

As a reminder to advertisers, Fix warns that “planned media weight is problematic [as] it creates a ‘smoothing effect’ which makes it difficult to correlate AM/FM radio ads with sales. Often media mix models apply ‘historical guard rails’ to constrain ROIs to conform to prior performance ranges. In essence, current ROI performance conforms to the past. This assures a constancy in ROI trending but is improper when a major change in the data quality occurs.”