How AI Is Making Market Mix Modeling More Relevant Than Ever

We all know what market mix modeling (or media mix modeling, or MMM) does: measure the impact and effectiveness of marketing activities. It spans different channels and types of marketing; when performed correctly, it can determine what’s working and where to allocate spends for maximum effectiveness.

Even so, MMM isn’t something that many marketers get excited about. Maybe because it’s been around for a while. Or maybe because traditional market mix modeling is relatively slow, expensive, and often geared towards non-digital activities. But that’s not the case anymore; today’s MMM uses near-constant access to a stream of incoming data to produce current, detailed, and highly customizable insights. And, as the name suggests, it’s more than capable of dealing with modern digital marketing activities and channels.

Call it what you will – digital, next-gen, optimized, or AI-led – modern market mix modeling is more relevant than ever before.

Traditional vs. Modern Market Mix Modeling

Old MMM was a bit like summing up a long list of numbers with a calculator: you could do it, but it took time (and maybe some human error). As technology improved, MMM became more like cutting and pasting numbers into a spreadsheet: better, but still slow. Today’s digital MMM – with data automatically being processed and uploaded, and different analyses available with just a few clicks – is much more in keeping with how we expect business intelligence tools to work in the 2020s. Just look at the comparison between traditional and digital MMM:

1. Older data vs. real-time data. Traditional MMM relied on data generated some months (or even years) prior to analysis. While this may not be historical data per se, it certainly does not represent the current state of the audience, market, etc. In contrast, modern market mix modeling is performed on data that’s collected in near real time; this is thanks to improvements in data processing and loading, as well as to interconnected platforms that can support multiple data analysis uses.
2. Forward view vs. backward view. The natural consequence of using older data is that your analyses are always looking backward, over what’s already happened. While this is great for post-campaign examinations, it doesn’t help much with mid-campaign adjustments. Because modern market mix modeling consumes current data, it’s much easier to monitor actions’ effectiveness and make any needed tweaks. Thus, strategy and decision-making can shift from what worked in the past to what’s working now.
3. In-house, on-demand vs. partner providers. Much of analytics modeling (not just MMM) traditionally requires the services of experts. Whether these were found in the company’s IT or data science team or through hiring an analytics partner, the result was often the same: it took time to customize the model and deliver the results. Now, though, intelligent dashboards and AI-powered platforms make market mix and other models available instantly and relatively inexpensively.

This is not to say that analyzing historical data, doing periodical reviews of past performance, or hiring an outside analytics partner has become unnecessary; on the contrary, these can all be helpful actions and sound investments. But it’s worth highlighting that this is not the only option available; now, we can do faster, higher-quality routine modeling using self-service AI tools while leaving the more complex models and strategy to the experts – in-house or third-party.

Benefits of Digital MMM

To wrap up, let’s consider the benefits of digital, next-gen market mix modeling:

• Modern MMM has the ability to easily incorporate digital marketing channels and methods.
• More data is available and it can be processed faster than ever before, allowing for flexible insights (from big-picture to fine-grained) and data-backed decision making.
• Because of AI-powered data processing, it’s no longer as expensive or slow to create custom models.
• Cloud computing makes it easier for modeling tools to scale across companies and new use cases.
• Thanks to machine learning and advanced modeling techniques, modern MMM tools continuously improve their performance, generating detailed and accurate results.

As businesses look for ways to use data to gain a competitive advantage, we expect to see AI-powered market mix modeling become ever more important.

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