Mindshare's New Data Chief: Media Mix Modeling Is not Dead

A Q&A with Former Carat Data Exec Rolf Olsen

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Rolf Olsen
Rolf Olsen

Mindshare has a new data chief and he's not afraid to combine traditional methods with cutting-edge approaches to make the right media decisions.

"I've always worked in some capacity around data," said Rolf Olsen, who started with Mindshare on March 9 following a 10-year stint with Aegis Media Limited, during which time he helped build Carat's analytics group. "I really like to understand what the ultimate impact of something is," he said.

In the time Mr. Olsen has been navigating data for agency clients, a lot has changed. Particularly in the past decade, traditional media mix modeling has given way to algorithmic analytics based on an onslaught of digital and social media data. But, says Mr. Olsen, media mix modeling -- which traditionally involved macro-level data to measure and predict the effect of advertising on sales -- still has a place for media buyers and sellers and can serve as a complement to today's digital approaches.

Of course, digital media and the variety of digital channels available to reach consumers has altered how agencies operate as a whole. "Historically agencies didn't necessarily look at data as a real asset," said Mr. Olsen, noting that "digital has really been the massive driver" of that.

Now, Mr. Olsen is charged with managing Mindshare's hub for data dissemination. Deemed The Loop, the physical space serves as a central zone where staff monitor data streams from an array of analytics sources and feed information to colleagues to help optimize and measure client campaigns. The initiative launched in February 2014 under Mindshare North America's former Chief Data Officer Bob Ivins, and Mr. Olsen aims to continue its development.

Ad Age: How will your work developing data and social analytics practices at Carat inform what you'll do at Mindshare?

Mr. Olsen: In the early part of my career, analytics was very much an add-on and was very much treated like that by agencies and clients alike. However, if you integrate a data and analytics framework with the strengths of a media agency, then you significantly increase your ability to deliver a tangible impact for your clients. And, you help make the planners and buyers better at what they do as they have a feedback loop. That's one of the things that impresses me so much about The Loop at Mindshare -- that we have a mechanism that acts on data and analytics in real-time in order to inform media investment.

Ad Age: You've talked about how media mix modeling has evolved in the last decade, particularly as digital has taken over marketing. How can MMM stay relevant?

Mr. Olsen: MMM has been around for a really long time, and has been "mainstream" for more than 10 years. The original focus of MMM was merely to show a total contribution from marketing, which then evolved to a focus on individual channels. The challenge then became that the last 10 years has seen an explosion of marketing touch points, putting significant pressure on the value perception of MMM.

That being said I do not believe that MMM is dead. The solution lies in model integration, applying a top-down, bottom-up modelling approach, using MMM as top down and using digital attribution modeling as the bottom-up element. Together that allows you the ability to mine the rich digital data, enabling a more frequent digital optimization focus, with the holistic optimization focus of MMM. The key take away again, is application, specifically recognizing where in the planning cycle you are and how the analytics is supporting planning and optimization.

Ad Age: What are the organizational obstacles in the typical large media agency when it comes to truly employing data and analytics to the fullest extent?

Mr. Olsen: You really need a successful marriage of operations, technology, analytics, and finance to pull it off! Historically agencies didn't really look at data as an asset, but in today's marketing environment it's vital in order to create both efficiency and effectiveness in the operating model -- and ultimately, the ability to create data-centric solutions that deliver meaningful client impact. As such, the key obstacle is how you create an environment where all parties can ultimately collaborate and have an equal voice.

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