In the ever-changing landscape of marketing analytics, the comeback of Marketing Mix Modeling (MMM) has caught the attention of many marketing professionals. What’s interesting is that despite the spread of new, fast tracking tools like Multi-Touch Attribution (MTA), MMM—a method that originated decades ago—is making a strong return. But why? And why should marketers today consider using both MMM and MTA together?
- Let’s discuss the reasons behind MMM’s comeback, its strengths, MTA’s part, and why integrating both can give marketers a more complete view of their marketing performance.
The Return of “Old School” Marketing Mix Modeling (MMM)
Marketing Mix Modeling (MMM) is a top-down statistical analysis used to measure the impact of marketing tactics (such as TV, radio, print, digital) on overall business performance, usually sales. It was shelved with the increased use of MTA, which promised more granular, user-level insights in real time. But in recent years, MMM has staged a comeback for several key reasons:
1. Privacy Concerns and Lack of Personal Data
With privacy regulations such as GDPR, CCPA, and the inability to use third-party cookies by major browsers, data collection has become more difficult and restricted. The loss of granular, user-level data has made MTA less effective for many marketers. MMM, however, doesn’t rely on individual user data but on aggregated data, making it an invaluable tool in a privacy-first world. It analyzes broad trends without needing user-level tracking, which aligns with today’s stricter privacy policies.
2. Data Silos
A significant challenge for MTA is that platforms like Google, Facebook, and Amazon control user data but restrict third-party access to it. This lack of sharing makes cross-channel attribution difficult. MMM, which uses aggregated data and doesn’t depend on user-level inputs from these platforms is less affected by this issue. It provides marketers with a clearer view of their entire marketing mix by aggregating data across channels, even when some platforms don’t share granular data.
3. Comprehensive Marketing Insights
While MTA provides valuable insight into user behavior across digital interactions, it often overlooks the bigger picture. MMM, on the other hand, is good at offering a high-level view of how marketing investments, both online and offline, drive overall business outcomes. This includes media channels that are harder to track with MTA, such as television, radio, and print advertising. As traditional media becomes more important for some brands, MMM’s ability to measure these channels makes it especially valuable.
4. Long-Term Effects and Brand Building
One of the downfalls of MTA is its focus on short-term metrics like clicks, impressions, and conversions. Brand building requires looking at long-term effects, which MTA does not assess. MMM, on the other hand, is good at providing insights into long-term impacts, making it an important tool for measuring the effect of brand campaigns that play out over months or even years.
Why Marketers Should Leverage Both MMM and MTA
While MMM is making a comeback, MTA is still important. In fact, the best results often come when the two metrics are used together. Here’s why marketers should consider both metrics:
- Granular vs. Bigger Insights
MTA is great for providing granular, user-level insights on digital insights. It helps marketers understand the specific path that leads users to conversions, giving them a better understanding of how specific tactics, such as social media ads, paid search, or display ads, impact individual user journeys.
On the flip side, MMM gives a big picture perspective, showing how different marketing channels contribute to overall sales or business outcomes. By combining MTA’s detailed data on specific actions with MMM’s broad analysis of overall marketing mix, marketers can gain a comprehensive understanding of their campaigns effectiveness.
- Short-Term vs. Long-Term Measurement
While MTA focuses on short-term conversions and immediate user actions, MMM allows for the measurement of long-term brand building and business outcomes. Using both metrics enables marketers to understand how their short-term decisions fit into broader, long-term strategic goals. This approach ensures that they’re not sacrificing long-term brand health for short-term gains, or vice versa.
- Adapting to Industry and Business Needs
Different businesses have different marketing needs. Retailers with a heavy digital presence may rely more on MTA to understand their e-commerce flows. However, businesses that rely on both offline and online marketing, such as automotive or consumer goods, may need MMM to evaluate broader campaigns. By leveraging both, marketers can customize their approach to better fit their industry, business model, and consumer behavior.
In closing, the comeback of MMM is not a return to the past but a path forward in how marketers should think about their media mix and performance measurement. With privacy becoming more of a priority and the limitations it brings to MTA, MMM offers a valuable perspective to marketing strategies. By using both MMM and MTA, marketers can maximize their impact and make data-driven decisions to help with both the quick decisions in digital marketing and the long-term goal of brand building.
Whether you’re planning new campaigns or assessing cross channel marketing performance, integrating MTA and MMM is crucial in today’s marketing. If you’re considering either or both, eSage Group can help prepare your data and integrate marketing analytics solutions into your Marketing technology stack and deliver the powerful insights your team needs.
About eSage Group
- 5-star Customer Satisfaction Rating
- An amazing list of long-term, satisfied, Fortune 500 Clients
- Strong technical depth in all major Cloud platforms and Mar-tech tools to provide an Enterprise level integrated solution
- Fast Growing Managed Services Offering providing top-tier on-going support to our Clients
- Experienced team of:
- Data Engineers
- Data Scientists
- Project Managers
- Data Visualization Experts
- Artificial Intelligence Engineers