​​Battle of Models: MMM vs. MTA

Marketing performance measurement has evolved significantly in recent years, but it still revolves around one big question: which channel, campaign, or touchpoint is truly driving business results?

To answer this, two methodologies stand out above the rest: Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA). Each has its own logic, strengths… and also limitations. While MMM offers an aggregated view, ideal for strategic planning and long-term budget allocation, MTA focuses on the user journey, analyzing how each interaction contributes to conversion.

In this article, we break down both approaches, compare their applications, and help you understand which one best fits your business based on your objectives, analytical maturity, and of course the current regulatory environment.

Because it’s not about choosing “the best model”, but about building the best measurement strategy.

TABLE OF CONTENTS

• What is Marketing Mix Modeling
• What is Multi-Touch Attribution Modeling
• Differences between Marketing Mix Modeling and Multi-Touch Attribution
• Choosing the right approach for your company

Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA) are two key methodologies for analyzing marketing impact, but with very different approaches. While MMM provides a global view based on historical data and external factors, MTA focuses on the digital user journey to attribute conversions. So, which one is best for your strategy? In this model showdown, we break down their differences, pros and cons to help you make the right decision.

Marketing Mix Modeling (MMM)

Marketing Mix Modeling (MMM) is an advanced statistical technique that allows you to measure and quantify the impact of various marketing factors both online and offline on sales or any other key business KPI.

The goal is simple: understand what’s truly driving performance in order to optimize investment. It analyzes historical data and evaluates the relationship between variables such as ad spend, pricing, promotions, distribution channels, seasonality, and even competitive context.

Thanks to this analysis, MMM can answer strategic questions like:

• How much does each channel or campaign contribute to sales?
• What’s the return on my marketing actions?
• How should I reallocate budget across channels to maximize performance?
• What effect do pricing changes have on sales volume?

To work effectively, the model requires a solid dataset: historical sales, channel investment, relevant external data (weather, holidays, competition...) and a deep understanding of the business. Based on this, a statistical model is built to identify patterns, estimate impact and generate actionable insights to guide strategy and resource allocation.

 Broadly speaking, MMM works through the following steps:

1. Data collection: Gather relevant historical data such as sales, advertising spend, promotions, pricing, distribution channels, and other marketing-related factors.
2. Statistical analysis: The collected data is subjected to advanced statistical analysis using econometric techniques and mathematical models to uncover relationships and patterns between marketing inputs and sales.
3. Model construction: Based on the previous analysis, a tailored MMM model is built for each business. This model quantifies the relationship between marketing actions and business outcomes, factoring in seasonality and external influences.
4. Simulations and scenarios: The MMM model is used to simulate hypothetical scenarios and test how changes in marketing elements might affect business outcomes guiding decision-making and strategy optimization.
5. Optimization and decision-making: Based on the model results and simulations, informed decisions can be made on marketing resource allocation. This includes budget adjustments, strategy refinements and ROI maximization.
6. Continuous monitoring and updates: MMM models are iterative and ongoing. Real results are monitored, and the model is updated to reflect changes in marketing factors or business context allowing for continuous improvement in marketing decision-making.

At Adsmurai, we’re clear on this: that’s why we’ve developed our own MMM models within Adsmurai Marketing Platform (AMP). We call them MMMs (yes, plural because they adapt to each business), and we combine them with attribution tools to provide our clients with an integrated and actionable view.

In general, while MMM provides valuable insights into the effectiveness of different marketing elements and resource optimization, it requires expertise, reliable data, and careful consideration of its limitations to produce accurate and actionable results.

Multi-Touch Attribution Modeling (MTA)

The Multi-Touch Attribution (MTA) model is like the detective of digital marketing: it analyzes the entire customer journey and distributes credit across the various touchpoints that contributed to a conversion. Because, let’s be honest no one converts on the first click anymore…

Think about any online purchase journey: you see an Instagram ad, then click on an email, compare products on Google, visit the website a few times… and finally convert. Each of those interactions played a role. MTA is designed to track and evaluate them all to understand their real impact on the final decision.

There are several ways to assign this “credit” to touchpoints, and depending on the model you use, the story can change significantly. Some common examples:

• First Touch: All credit goes to the first interaction. Great if you're measuring top-of-funnel awareness.
• Last Touch: Full credit is assigned to the last interaction before conversion. Common, but often unfair to the rest of the journey.
• Linear: Equal credit is distributed across all touchpoints. Balanced, but sometimes too simplistic.
• Time Decay: More weight is given to the interactions closer in time to the conversion. More realistic for certain purchase journeys.

These models (and more advanced ones based on data or algorithms) offer different perspectives on how conversions happen. Choosing the right one depends on your business objectives, product type, and user behavior.

MTA is based on tracking and analyzing data to understand what works and what doesn’t. Done right, it helps optimize campaigns, allocate budget more effectively, and improve overall marketing performance.

The Multi-Touch Attribution (MTA) model typically involves the following steps:

1. Data Collection: Gather data on customer interactions throughout the buying journey. This includes clicks, website visits, ad views, social media interactions, and more.
2. Touchpoint Identification: Map and track each customer interaction with the brand across channels such as seen ads, received emails, online searches, website visits, social engagement, etc.
3. Value Assignment: Assign value to each touchpoint based on its influence in the conversion process. This can be done using predefined rules, statistical models, or machine learning algorithms.
4. Analysis & Insight Generation: Analyze the data to identify which touchpoints are most effective, how interactions build up over time, and which combinations drive results.
5. Optimization & Decision Making: Use these insights to inform marketing resource allocation, refine strategies, and improve customer experience.

It's important to note that the effectiveness of MTA models may vary depending on the industry, business model, and data availability. Organizations should carefully consider these pros and cons and adapt their approach based on their specific needs and capabilities.

MMM or MTA? Choosing (and combining) the right approach

When it comes to deciding between Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA), the first thing to understand is that there’s no one-size-fits-all model. The best choice depends on your needs, your data, and your business goals.

Here are the key factors to consider:

• Business goals: Looking for a strategic, high-level view of marketing impact? → MMM. Want detailed insights into specific campaigns and channels? → MTA.
  
  
• Available data: MMM works with aggregated historical data. MTA requires granular data on each user interaction. The quality and structure of your data will be crucial.
• Response time: MMM is great for medium-to-long-term analysis. MTA delivers near real-time insights to optimize on the go.
• Resources and capabilities: MMM requires technical profiles and complex statistical models. MTA relies on solid tracking implementation and analytical tools. If you lack internal resources, you can rely on experts like Adsmurai.
• Level of detail: MMM gives you a macro view of your media mix. MTA zooms into each touchpoint. What level of depth do you need?
• Industry and channels: Not all industries or channels fit one model better than the other. For example, if offline media is a big part of your strategy, MMM often performs better.
• Budget: Consider whether you can invest in tools, technology, and analysis. Both approaches come with different costs and different benefits.