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Reinventing Marketing Mix Modeling in the Age of Personalization

Last updated: November 30, 2020

reinventing-marketing-mix-modeling-in-the-age-of-personalization

reinventing-marketing-mix-modeling-in-the-age-of-personalization

Think of all the different ways you interact with media on a daily basis—from your phone to your computer or television, to a billboard— you engage with a large number of touchpoints across online and offline channels.  This increase in media consumption has made it crucial for marketers to personalize their outreach and efforts.

With access to such a wide media mix, modern consumers are used to ignoring or engaging with brand messages based on whether or not they are relevant to their needs. The messages that are tailored to individuals are engaged with, while generic messages are left to wither on the vine.

To better gather the insights needed for personalized campaigns, marketers using marketing mix modeling (MMM) need to leverage the high-level, aggregate insights that it provides to optimize their modern, person-level measurements and attributions.

For effective use of marketing mix modeling today, marketers need to understand the insights it provides as well as how they can use those insights to optimize additional marketing efforts.

What is Marketing Mix Modeling?

Traditional marketing mix modeling is the practice of using long-term, aggregate data collection and regression analysis to highlight unique trends across marketing efforts and identify key areas of opportunity to capitalize on those trends. MMM was originally relied on in a time when marketers didn’t have nearly as much choice in the channels and touchpoints they used engage consumers. The fewer options marketers had to reach consumers meant  greater insights could be derived from simpler, long-term data collection.

  • For example: Before the digital age, marketers were limited to print and broadcast marketing. Using long-term insights from their marketing mix models, they could recognize that print engagement rose in the summer, and TV and Radio engagement rose in the winter.

With this information, marketers could optimize their efforts for increased ROI—focusing marketing spend and campaigns on the channels that drove the highest impact at certain times. The problem with relying on MMM for optimization efforts now is that there are simply too many ways a consumer can engage with marketers for long-term insights to be actionable.

Modern Marketing Mix Modeling in a Unified Measurement World

Modern marketers understand that marketing mix modeling comes with a number of pros and cons. While the traditional utilization of MMM is outdated (i.e. today’s consumer trends change too fast for long-term, aggregate measurements to keep up), marketers can still use MMM to identify broad marketing trends and spending patterns.

Marketers who want to get the most out of all their marketing measurements—including marketing mix modeling— must combine them into a single unified marketing measurement strategy. With unified marketing measurement, marketers can incorporate the unique insights provided by each analysis method with additional metrics. This provides a single, comprehensive view into marketing effectiveness and ROI opportunity.

Take marketing mix modeling and multi-touch attribution (MTA) for example. MMM provides broad, long-term data that can highlight trends like seasonality, brand impact, holiday impact, etc. that rapid measurements can’t. On the other hand, MTA provides the unique impacts of each touchpoint along a channel, scoring them on their ability to impact conversion accordingly. Neither method provides enough insights to be solely relied on, but used together, marketers gain a wider view of their efforts that lead to better marketing campaign decisions.

Using Marketing Mix Modeling for Strategic Planning

For marketers aiming to distribute engagements that are tailored to consumers at the person-level, they have to wade through a large collection of big data. For every person-level insight gained, marketers have attributed each touchpoint across print, digital, and broadcast channels, recognized which creative messaging resonates with the consumer, as well as what time they prefer to be reached, and what channels they prefer to be reached through.

As you can imagine, it’s a lengthy process. For marketers looking to truly optimize their efforts across the marketing mix, it helps to have a solid foundation upon which they can begin narrowing the marketing measurements down. Enter marketing mix modeling. With the long-term insights that MMM delivers, marketers can see overarching themes, trends, and shifts that show them the best places to begin their marketing measurement search.

Think of MMM as a decoder that lets marketers make sense of the scrambled marketing landscape before diving deeper to find the specific insights that indicate personalized consumer behavior. By understanding the overall marketing landscape first,, marketers can identify where and when they should focus their additional measurement efforts like MTA.

Final Thoughts

Today, marketers rely on a wide variety of marketing measurements as part of a unified measurement strategy for insights into the personalized marketing campaigns modern consumers expect. In this effort, marketers break away from traditional use of marketing mix modeling, and leverage the long-term, high-level insights it provides to optimize the rest of their marketing measurements—giving marketers the personalized data they need faster.

Written by Marketing Evolution