Market Mix Modeling: Why You Are Failing at It and What You Can Do About It
While market mix models (MMM) might sound quite elementary to our peers in marketing, PR professionals have only recently discovered their value. This sudden interest in this statistical tool can be partly attributed to the need to prove our worth in the marketing mix to clients and partly to the increasing sophistication in PR measurement and analytics that our field is now realizing. As IPR’s Director of Research, Dr. Sarab Kochhar recently noted, “Virtually every aspect of business is now open to data collection and this broad availability of data has led to increasing interest in methods for extracting useful information and knowledge from data.”
And yet, I find us still struggling with statistical tools and programs such as MMM. As a Senior Manager of Predictive Analytics at a global PR agency, I conduct several training sessions with account managers and senior executives to increase their understanding of this tool. PR pros across the industry are now being asked for tangible reports and metrics to rationalize budgets while competing against other communication channels. In this post, I summarize some key points from these training sessions.
What is MMM: In simple terms, MMM is a tool that uses sales and marketing data to establish the return on investment (ROI) of each channel in the marketing mix helping organizations develop the most effective spending level for each channel. In other words, these models not only demonstrate the contribution of each communication program but are sophisticated enough to build future projections enabling budget optimization.
Key challenges: Most PR practitioners who have worked with clients on MMM would agree that most often these programs reflect poorly on PR. There are a couple of reasons for why the odds are not in our favor when relying on MMM:
- Firstly, the entire notion of ROI is something that is often very difficult to demonstrate in PR. Please don’t get me wrong- I am a data person and completely support using these financial metrics as much as possible. But, it is not always possible. Our Key Performance Indicators (KPIs) such as brand equity, reputation, trust and credibility cannot always be represented in those hard terms.
- Another related issue that works against PR is this term—working versus non-working dollars. In plain terms, all money spent on consumer-facing activities is classified as working dollars (e.g. ad placement, sponsorships), while all money spent on creating output (e.g., pitching, production and editing costs, agency fees, planning, measurement and analytics) is classified as non-working. As you can imagine, our working to non-working dollar ratio does not always match the desired 80:20 rule that applies to other marketing channels (a popular but completely misguided notion). Making matters worse, most MMM do not account for this distinction and club together PR with all other channels in the mix suggesting that PR has the lowest ROI.
As a result, we often find ourselves getting caught in the vicious cycle. We receive a lesser budget in the next fiscal cycle, so we are able to do less, and hence demonstrate less, and thus, get even lesser budget the next time. Sound familiar? If you have ever been or are in this situation, here are the top five questions you should ask when you see the output of a MMM and top five talking points to have a smart and effective discussion with the clients:
Top five questions to ask:
- What’s the ROI of PR activities?
- How has ROI changed from previous cycle?
- What if the budget was $$ and not $?
- How do sales/impressions move with weekly PR spend?
- What is the contribution of PR within mass media?
Top five talking points:
- Explain why the current MMM tends to under credit PR.
- Demonstrate time series ROI movement of each channel.
- Present a couple of scenarios with different budget distribution.
- Ask the client about desired working: non-working spend ratio for PR.
- Pitch other metrics (e.g. brand equity, reputation, trust) to measure value of PR.
While I have run several MMM for my clients and as a data analytics enthusiast, I personally am not a big advocate of the way in which they are currently being used. It is almost like judging how fast an elephant can climb a tree when that’s not really what it’s meant to do. But, if you do find yourself losing the battle in MMM, hopefully this post has given you some insights to turn the wheels around.
Rajul Jain, Ph.D., is an assistant professor at DePaul University and a senior manager at Ketchum Global Research and Analytics. Follow her on Twitter @talktorajul.