Today’s media landscape requires consistent and accurate insights into campaign performance across all marketing channels. With the introduction of digital media into the marketing landscape, it is growing increasingly difficult to properly gain insights into campaign effectiveness and properly attribute the performances of each channel.
In the past, marketers used traditional marketing mix models, gathering high-level, aggregate data over a long period of time to showcase where along the marketing mix channels were most effective. For example, if marketers noticed that consumers made more purchases following a magazine ad over the span of several months, they would then focus their marketing efforts on this medium, running similar ads.
Today, however, marketers have realized that in order to truly measure marketing campaign ROI, an omni-channel measurement capability is needed. This new approach helps address the addition of new points of contact, while giving marketers the capability to better understand consumer interactions with their brand across all media mix channels, and how these channels impact one another. This is a much more complex process, calling on marketers to track exposure to ads across digital and traditional displays.
So, the question is, where does marketing mix modeling (MMM) fit into today’s practices of marketing analytics, and how can it still provide marketers and enterprises with valuable insights for better campaign optimization? To answer this question, we’ll dive into the history of marketing mix modeling, and highlight several pros and cons that provide insight into its place within the world of marketing analytics and where it’s headed.
A Look into the History of MMM
Marketing mix modeling has been around for decades, preceding digital marketing and the mainstream internet as we know it. Originally, MMM was designed to guide marketers’ investments by providing insights into the channels and strategies that were delivering the best results. This was accomplished through the practice of long-term, aggregate data collection using regression analysis to determine key areas of opportunity.
When leveraging MMM, marketers typically look at offline media channels like TV, radio, and traditional print advertising across a long period of collection. This provides insights that can be used to plan their marketing spend according to the times and media channels where it’s most consumed. Additionally, MMM considers external factors that may influence long-term media trends, including insights into consumer behavior, brand authority, weather, natural disaster, holidays, competition, etc. to further assist in providing actionable insights.
The resulting metrics provided from marketing mix modeling gave marketers a “top-down” view into the effectiveness of their efforts, providing a broad overview of each channel that generated ROI along the model’s timeline. Marketing mix modeling was created at a time where the marketing landscape was simplified. At its peak, MMM provided enough insight into marketing spend and media mix placement to give marketers opportunities to optimize their efforts. However, the days of relying on MMM alone for campaign spending optimization are long gone.
The Cons of Marketing Mix Modeling
Although historically MMM was used to great effect for optimizing marketing efforts, it simply isn’t capable of providing the person-level, granular insights that modern marketers rely on. Marketing is constantly evolving, and because it changes at such a rapid pace, long-term data collection can no longer be relied on as before. This was made evident throughout the COVID-19 pandemic, in which marketers have had to quickly adapt to changes in consumer behavior.. Additionally, the introduction of digital marketing added further complications to the practicality of MMM by creating a fragmented media mix of traditional and digital marketing. This makes it difficult to create insights that measure combined digital and traditional marketing ROI.
Limitations of Marketing Mix Modeling
Today’s marketers need insights into a variety of elements across their marketing ecosystem, including:
- Person-level, behavioral data
- Insights into cross-channel marketing impact (how one channel impacts the media mix or vice versa)
- The impact brand authority has on marketing spend and campaign optimization
- Proper marketing attribution on individual media effectiveness
- The impact of creative messaging across channels
- Key times to send marketing messages
As you might expect, the complexity of these metrics—considered standard by today’s marketers—require a variety of marketing optimization analytics to be accurately measured. As a result, marketers are now shifting toward a unified measurement methodology. This approach combines a variety of data measurement techniques into one cohesive insight model that gives marketers visibility into marketing campaign optimization across media with a centralized view.
The Pros of Marketing Mix Modeling
While MMM lacks the granular insights needed to identify individual opportunities for marketing campaign optimization, it does take into account a variety of external factors. This makes it an effective tool that provides the starting point for high-level, overall marketing budget planning.
Given the fact that marketing mix modeling includes both internal and external factors into its aggregate data collection, it provides a much-needed holistic approach to general marketing trends. In this sense, MMM gives marketers a 360º view into their prospective markets. This is especially important for CMOs, who are responsible for allocating the appropriate budgets for their company’s marketing efforts.
While marketing mix modeling may be outdated in its traditional methodology, it provides marketers with a “report card” of sorts, highlighting major marketing trends they can use to better plan their overall strategy. In order for enterprises to get the most out of a marketing mix model, here are a few recommended MMM best practices to ensure you get the insights you’re looking for without damaging your bottom line:
- Don’t allocate too much time and budget
- Keep MMM generation in-house
- Conduct modeling one to two times a year
- Use MMM for basic diagnostics of your target market to better shape your budget preparation
Marketing mix modeling in and of itself is a mixed bag of pros and cons. Given its long data collection timeframe, inability to provide specific insights for personalized marketing, and its “top-down” level of insights, marketers can’t rely on MMM alone for campaign optimization insights. However, with a marketing performance measurement solution, marketers and enterprise organizations can leverage MMM as an effective way to gain a holistic, 360º overview of market trends, behavior, and media consumption. This can help to better plan campaign budgeting, marketing spend, and campaign optimization.