This article originates from a presentation at the Revenue Marketing Summit in Las Vegas in 2023 when Siara was the Head of Growth Marketing at Autodesk. She has since moved roles.
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Navigating the complex maze of media mix modeling in today's multifaceted market can be a daunting endeavor.
My journey at Autodesk, where the landscape consists of a bifurcated business model pivoting on distributors and online sales, has been a testament to the vitality of leveraging data and optimizing media mixes to steer toward palpable results.
I’m Siara Nazir, and my forte has been analyzing and unearthing the granularities of data to ensure our conversion strategies, whether year-over-year or quarter-over-quarter, not only resonate with the macro-perspective but also trickle down effectively to the specifics, ensuring a robust strategy that withstands the undulating tides of the global market.
In this article, I’ll delve into:
- The origins and evolution of media mix modeling
- Diving deep into media mix modeling and its potency
- Global use cases: Tailoring media mix modeling to unique market dynamics
- Navigating diverse media landscapes: Our Japanese market strategy
- Analyzing success and challenges through test results in global markets
- Key outcomes, surprises, and learnings
- Navigating the realm of media mix modeling: A 5-step approach
- Key considerations and potential hurdles
The origins and evolution of media mix modeling
The origins of media mix modeling can be traced back to the 1970s, starting with a certain statistician professor at MIT.
Despite becoming a topic of more common discussion only in the past decade or so, this strategy has been embedded in the marketing world for quite a substantial period. The MIT professor crafted a mixed model, aiding marketers in deciphering viable investment options.
As we catapulted into the 1990s, media mix modeling began gaining momentum when various companies formulated offerings that facilitated other businesses in accelerating their marketing ventures.
It was a significant era – the burgeoning of the internet altered the media landscape, shifting focus from traditional mass media (such as television, print, and radio) to a novel medium, enriched with behavioral marketing and new segmentation available to marketers.
Companies like MarketShare emerged, delving deep into experimenting with media mix modeling - companies with which I've had the pleasure of working.
Fast-forwarding to today’s Martech landscape, media mix modeling has nestled itself snugly within the framework of data analytics, media, and, of course, modeling.
The red box outlined in our current martech landscape highlights where media mix modeling resides – not isolated in its own segment but rolled into the myriad offerings companies now have surrounding data analytics and media. This conflation into a cohesive section was non-existent just a few years ago.
Peering into the future, I foresee artificial intelligence (AI) and machine learning (ML) not only amplifying the number of companies in this sector but likely birthing a new sector concentrated around predictive modeling. This, I believe, represents the forthcoming evolution where marketing is headed.
Diving deep into media mix modeling and its potency
Let's demystify what media mix modeling truly is. At its core, it's an econometric model deeply rooted in statistical methodologies, and to truly understand its prowess, it’s essential to distinguish it from conventional attribution methods like last-touch or multi-touch attribution.
While the latter are principally predicated upon a customer action – such as viewing a banner ad or clicking a link, and accordingly, credit is assigned in the attribution system – media mix modeling operates a tad differently.
It thrives on correlation: the association of variables with another variable.
Consider this analogy: if it rains, people are likely to carry umbrellas. The presence of rain correlates highly with the emergence of umbrellas.
So, how does this translate into a powerful marketing strategy? Media mix modeling allows us to scrutinize non-action-oriented variables like brand equity.
Taking companies like LifeLock or Stitch Fix as examples, ponder the worth of the brand and the equity it hauls in. A media mix model has the capability to quantify this, establishing what we term a 'baseline'.
Upon establishing this baseline, the enthralling journey of experimentation commences. This involves exploring queries such as:
- What happens to the incremental revenue above the baseline if the display or social inputs are escalated?
- Is it effective?
This realm of experimentation is not merely intriguing but is a hotbed for innovation and strategic fine-tuning in marketing endeavors.
Visually conceptualizing it: you have all of your channels - and it’s noteworthy that many media mix models can incorporate offline data too.
If your product is retailing at Staples or other outlets, that data can be synergized along with your online and television data. The data funnel through, purchases are executed, and the model regurgitates what it perceives as the attribution.
Therein lies the playground where marketers delve in, optimizing their mix, and that's essentially the back-end mechanism of how it all intricately works.
Global use cases: Tailoring media mix modeling to unique market dynamics
Embarking upon our journey with media mix modeling, we initiated experimentation both with a small company and in-house, mindful of the budgetary constraints that many entities navigate.
What transpired was enlightening: our in-house experimental findings closely mirrored what the external media mix modeling produced, bolstering our confidence in our strategic maneuvers. Yet, like all ventures, it was, and always is, a cycle of testing and learning, with myriad factors influencing outcomes.
Creating the media mix commenced with a critical step: aligning it closely with the nuances of the market or country in focus. Tailoring our mix to resonate with the demographic and socioeconomic dynamics elevated our conversions by an impressive 23%.
This success was birthed from our investment in understanding the customer first, and establishing our goals clearly – be it to amplify awareness, drive conversions, or propel trials – before crafting the mix.
For instance, during our experimentation in Germany, we discerned that customers harbored a penchant for data and extended web page content, a stark contrast to the US market, where brevity and concise choices are paramount to maintaining customer engagement.
