Marketing Mix Modeling with Digital Data
4 min read

Marketing Mix Modeling with Digital Data

Marketing Mix Modeling with Digital Data
Photo by Ricardo Gomez Angel / Unsplash

Marketing Mix Modeling, a very important marketing analytics technique where many businesses have their sales occur offline, while significant marketing dollars may be spent on online marketing.

The key marketing questions that arise that many people struggle to answer are:

  • How much should you spend and allocate your marketing dollars to what ad channels?
  • What does a success looks like (or ROI) if you spend X amount to which Z ad?
  • How much offline ads or online ads are contributing to the bottom line?

These questions are important and something every marketer are obsessing to understand. The reason I’ve pointed out the business model where revenue share is split between offline and online channels is because it quite hard to answer these three questions depending on your data environment or availability.

Consumer touch points with your ads or brand could vary and come from many different sources.

People can learn about your product through social media, research on manufacturer site, go to the retail store, then shop online at a discount.

Consumers could see the ad and go to the retail store directly and pick up the product, too. There is NO one single path.  This is very complex if you are trying to tackle trying to measure the effective of media mix within this ecosystem.

I’ve actually worked on such analytics effort with a vendor specializing in marketing mix modeling, which helped me answer those three critical questions.

Before moving forward, here is a brief description of marketing mix modeling in case this is new to you.

“Marketing mix modeling is a term of art for the use of statistical analysis such as multivariate regressions on sales and marketing time series data to estimate the impact of various marketing tactics on sales and then forecast the impact of future sets of tactics. It is often used to optimize advertising mix and promotional tactics with respect to sales revenue or profit. The techniques were developed by econometricians and were first applied to consumer packaged goods since manufacturers of those goods had access to good data on sales and marketing support.” — via Wikipedia

So what I’ve learned is that in the modern era of marketing mix modeling, there is more application of digital data than years ago when companies were using only TV ads, sales, spends, econometrics models, etc. Some of the digital data that would go into modeling are:

  • Unique visitors
  • Traffic to a key section of the site. i.e. product page traffic, cart page
  • Organic search traffic like branded terms
  • Product category traffic or search trend around those product related terms
  • Digital Ad traffic from Banner Ads, Emails, Paid Search, etc.
  • Social Media brand mentions online (could be positive sentiments)

These are examples only, and what goes into the model should be consulted and worked out to make sure the stakeholders are in alignment. In most of the cases, it has many dependencies on the experience of the partner you’re working with.

What I’ve learned from the data preparation for marketing mix modeling is that it requires data that spans multiple years, and better to have granular data in daily or weekly basis. Part of the reason is because in the modeling:

  • it needs enough data to understand seasonality
  • it needs enough data and trend to recognize correlation for small data that may have an impact on sales
  • some macro trends may take only on rare occasions so having that data as one part of a timeline will allow it to be picked up as a signal
  • build a good What-If scenario analysis model

What this means is that it is important to have your data methodology clean and consistent. As we digital folks know, digital data could be short lived.

Switching vendors, site migrations where data are scattered in different databases or archived, change in tracking methodology, changes in conversion event, the emergence of new data from Social Media, etc.

If you’re not doing that now, I’d recommend you start thinking about it now and document everything you’re tracking.

Anything could happen from: switching vendors, site migrations where data are scattered in different databases or archived, change in tracking methodology, changes in conversion event, the emergence of new data from Social Media, etc.

If you’re not doing that now, I’d recommend you start thinking about it now and document everything you’re tracking.

If you’re not doing that now, I’d recommend you start thinking about it now and document everything you’re tracking.

One challenge that I had was getting social media data. Availability of social measures are pretty recent (I bet that applies to many companies), and many social measures are representing growth over time especially if companies put a lot of efforts into it in recent years. So it may or may not provide added value to the modeling’s output. So it requires some digging into narrowing down the data that shows relevant trends that give you a good signal that may impact the bottom line. For example, instead of just tweets or mentions online, narrow down to branded terms or category terms.

For example, instead of just tweets or mentions online, narrow down to branded terms or category terms.

Not sure how much effort you will have to put into building your model, but it seems like marketing mix modeling is more of an art than science because a lot of the learnings and recommendations vary by vendors or the data scientists.  

So I would recommend choosing the right partner with experience and know-how in your business.

Example MMM chart

source: https://bottomlineanalytics.com/our-thinking/1-are-you-getting-the-most-from-your-marketing-budgets-01/

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