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Marketing Attribution
(MTA vs. MMM)

In the hated and ever-changing marketing landscape, we are all constantly looking for new ways to optimize our marketing strategies and maximize return on investment. A key area of focus that still eludes many businesses is understanding the effectiveness of different marketing channels and touchpoints in driving customer conversions. That's where Multi-Touch Attribution (MTA) and Marketing Mix Modeling (MMM) come in, two powerful analytics techniques that help us uncover the true impact of our marketing efforts.

However, navigating the world of MTA and MMM can be intimidating for professionals without a data science background. In this article, we want to demystify these complex methodologies in a way that is easily understandable for business professionals, regardless of their technical experience or functional role.

We'll explore the pros and cons of MTA and MMM, discuss when it makes sense to leverage each method, and examine how recent developments in marketing analytics are democratizing access to these powerful tools.

Marketing Attribution (MTA vs. MMM) Marketing attribution is a crucial aspect of marketing strategy as it helps us understand the effectiveness of our marketing efforts and allocate resources accordingly. We live in a data-driven world, and marketers need to be able to quantify the impact of their campaigns and identify which channels or tactics deliver the best results. This understanding allows us to optimize our strategies, make informed decisions and improve return on investments (ROI).

There are two main techniques used to measure marketing attribution:

Multi-Touch Attribution (MTA)

Marketing Mix Modeling (MMM)

While both approaches aim to quantify the impact of marketing activities, they do so with drastically different methodologies and at different levels of granularity.

Multi-Touch Attribution MTA is a marketing attribution technique that assigns fractional value to different marketing touchpoints in the customer journey, from the first interaction to the final conversion. MTA aims to provide a more detailed understanding of the customer journey, capturing the effectiveness of individual touchpoints across various channels and providing marketers with insights into which channels or tactics deliver the best results.

Benefits of Multi-Touch Attribution:

Granular customer journey insights: MTA uses user-level data to capture the effectiveness of individual touchpoints, allowing marketers to understand which specific interactions contribute to the final conversion and identify opportunities to optimize campaigns.

Real-time optimization: MTA provides near real-time data, allowing marketers to quickly make changes to campaigns. This real-time feedback allows marketers to identify touchpoints that aren't working and shift resources to better-performing channels or tactics.

Advanced Personalization: MTA's user-level data allows for better targeting and personalization of marketing efforts. Marketers can tailor messages and offers based on customer preferences and behaviors, improving customer engagement, conversion rates and loyalty.

Marketing Mix Modeling Unlike MTA, Marketing Mix Modeling (MMM) is an attribution technique that uses aggregate historical data and statistical analysis to understand the impact of different marketing activities on sales. MMM is designed to help marketers predict how changes in marketing investments across channels will impact sales results or how saturated a channel is. MMM helps marketers make informed decisions about overall marketing strategy and budget allocation by providing a broader, more strategic view of marketing performance.

Advantages of Marketing Mix Modeling:

Comprehensive view of marketing effectiveness: MMM considers both online and offline channels (as well as earned, paid and sometimes owned media) and external factors such as seasonality, promotions and economic indicators. This holistic approach allows marketers to understand the broader context of their marketing activities and make informed decisions about resource allocation and campaign planning.

Long-term insights into marketing impact: MMM is well-suited for capturing the long-term impact of marketing campaigns and brand-building efforts. While MTA focuses on individual touchpoints and short-term interactions, MMM looks at the bigger picture and considers the cumulative effects of marketing activities over time.

Less susceptibility to biases and data collection challenges: Unlike MTA, MMM is less affected by biases and issues related to cookie tracking, ad blockers, and other data collection challenges. Because MMM uses aggregated historical data rather than user-level data, it is less susceptible to inaccuracies and data gaps.

When to use MTA or MMM? The choice between MTA and MMM largely depends on a company's specific needs and goals. MTA may be the most suitable option if a company focuses primarily on digital channels and requires real-time optimization. On the other hand, if a company wants a broader and more strategic view of its marketing performance across various channels, including offline and PR activities, MMM may be the better choice.

Combining MTA and MMM for Comprehensive Insights It is best to combine MTA and MMM to obtain comprehensive insights into marketing effectiveness. By leveraging the detailed, real-time insights provided by MTA and the broader, long-term perspective offered by MMM, you can develop a comprehensive understanding of marketing performance. This combined approach allows for more accurate decisions, better campaign planning and better resource allocation.

The importance of data quality and accuracy Regardless of whether you choose MTA, MMM, or a combination of the two, data quality and accuracy play a crucial role in the success of your marketing attribution efforts. Ensuring your data is clean, consistent, and up-to-date is essential to gaining meaningful, actionable insights from attribution models.

Data quality audits, proper data governance, and the implementation of data quality controls can help maintain data integrity and improve the overall effectiveness of your marketing attribution strategy.

As privacy regulations and tracking technologies continue to evolve, companies must adapt their marketing attribution strategies accordingly. Staying informed about changes to privacy laws, such as the GDPR and CCPA (and now the upcoming CPA), as well as developments in tracking technologies, is essential to ensuring compliance and ensuring the continued effectiveness of attribution models. By proactively addressing these challenges and seeking innovative new solutions, you can ensure your marketing attribution efforts remain relevant and impactful in an ever-changing landscape.

Democratizing Analytics Opens New Doors Historically, you could only get marketing effectiveness and optimization studies if you were willing to wait 12-16 weeks and shell out at least $150,000. The advent of open source software, the democratization of advanced analytics techniques, and investments in data science research by tech giants have made sophisticated analytics solutions accessible to the masses.

While most small and medium-sized businesses (including many advertising studios) still need more human or technological resources to run MMM, the cost and effort to run MMM in-house has decreased significantly.

Meta's Robyn Project is a solid open source toolkit that I encourage everyone to look into. Robyn is a semi-automated package for MMM that can handle model building, including hyperparameter tuning, descriptive analytics (for example, share of ad spend versus impact), and marketing spend optimization . It is currently available for R, and the team is working on a Python version. I tried it and was very happy with the results.

Example output from Robyn's model: How much does each marketing lever contribute to total sales?

Robyn's Budget Modeling Output Example: How should we change our marketing mix to optimize sales results?

As marketing analytics continues to evolve, innovative solutions like Cassandra are emerging to further democratize access to MMM. Cassandra is a cutting-edge platform designed to provide businesses of all sizes with the tools they need to effectively analyze and optimize their marketing efforts, at a fraction of the cost that most marketing consultancies charge.

To learn more about Cassandra's unique features and benefits, we are excited to present an introduction from their co-founder and CEO, who will share his insights on how this powerful platform is transforming the marketing analytics landscape.

An introduction to Cassandra: a game changer for marketing analytics By Gabriele Franco, CEO of Cassandra

Why does Cassandra exist? Before starting Cassandra, I worked at my own marketing agency,, where we managed over $12 million in advertising dollars for our clients. I realized we needed a scientific way to allocate our clients' marketing investments to get the most ROI from them. We started testing MMM and realized that most companies without a team of data scientists couldn't access scientific methodologies because they were expensive (over $100,000 per project) and extremely slow. Each project took six months to achieve results.

This realization led us to develop Cassandra, an automated marketing analytics platform that provides fast, accessible and actionable MMM insights.

How to Manage Marketing Mix Modeling Service (Old Way vs. New Way) The Old Way:

Data collection: Collect historical marketing and sales data from customers, including advertising spend, promotions, pricing, distribution and more.

Data preparation: Clean and organize data with precision and consistency, addressing outliers, missing data, and format inconsistencies.

Model selection: Choose an appropriate statistical model, such as linear regression, time series analysis, or machine learning algorithms.

Model calibration: Train the model with the trained data, adjusting parameters to minimize errors and maximize predictive accuracy.

Model validation: Compare model predictions to actual sales data, ensuring an accurate representation of the impact of marketing channels on sales.

Generate insights: Analyze model results to identify insights and recommendations, such as effective channels, budget reallocation, or strategic adjustments.

Reporting: Create a comprehensive, easily understandable report with visual aids to present the findings and recommendations of the MMM analysis.

Ongoing support: Assist customers in implementing MMM recommendations by offering periodic performance checks and model updates based on new data available.

The new way: With Cassandra, you can automate all these parts at a very affordable price and start running your first MMM in an hour.

Our goal was to create a product that even non-technical marketers could use, making it simple and accessible for businesses of all sizes. With Cassandra, users can create their own marketing mix, simulate marketing budget allocation decisions, and easily analyze various scenarios in four simple steps:

Step 1: Connect your data and respond to the form Step 2: Train your MMM in the cloud Step 3: Unlock your MMM insights Step 4: Simulate spending scenarios with your marketing budget allocator

An opportunity for brands: Cura of Sweden case study Thanks to Cassandra, brands can now optimize their marketing mix by measuring the true incremental impact of each marketing activity. They can allocate their marketing budget across various media channels, identify optimal investment levels for each channel, and determine whether they are spending too much or too little in specific areas. On average, our clients see an increase in ROI of more than 30%, helping them achieve better results and grow their businesses.

One of our successful collaborations was with Cura of Sweden, a Swedish company that develops innovative, high-quality products for sleep improvement and sleep health. Cura Sweden faced challenges in internationalizing its brand in several countries, while trying to keep marginal costs in line with its financial forecasts. They found that Multi-Touch Attribution (MTA) via Google Analytics was insufficient due to the long time between interaction and purchase, making it impossible to accurately track each investment's performance and contribution to sales.

To address these challenges, the company used Cassandra to analyze its media mix and develop a customized media plan to maximize sales. Using the budget allocator, they were able to simulate investment scenarios and improve the effectiveness of their marketing channels.

Collaboration with Cura of Sweden resulted in a significant improvement in advertising budget, total paid orders and cost per conversion. Specifically, they saw an: 86% increase in media-to-paid orders 16% reduction in cost per conversion 52% increase in marketing budget invested Cassandra identified which marketing channels provided the best ROI and optimized the their media mix accordingly, resulting in a notable improvement in sales and advertising budget allocation.

Incremental Revenue Generation Opportunities for Agencies Marketing Mix Modeling is not just an opportunity for brands to increase their ROI. Agencies can also leverage this trend to offer MMM consultancy services to their clients.

Advertising has become more scientific than ever, and it is essential for agencies to have strong analytical skills to meet client needs. Agencies that can offer marketing mix analysis services can stand out in the market and create new sources of revenue.

In summary Marketing attribution is essential for measuring the effectiveness of marketing activities and optimizing budgets. MTA and MMM are two key approaches to obtaining this information, each with their own advantages.

MTA provides a detailed understanding of the customer journey and offers real-time optimization opportunities, ideal for brands that focus primarily on digital channels.

MMM offers a broader view of marketing performance and considers both online and offline channels, and is suitable for capturing the long-term impact of marketing campaigns.

The combination of MTA and MMM can provide a comprehensive understanding of marketing performance.

Data quality and accuracy are critical to the effectiveness of marketing attribution analyses.

The democratization of analytics is making these powerful techniques more accessible to small and medium-sized businesses through innovative solutions like Cassandra.

Revenue growth opportunities for brands and agencies rely on a better understanding of marketing performance and the ability to optimize budgets.

In summary, understanding how marketing attribution works and leveraging the benefits of innovative tools and platforms can make the difference in the success of your marketing strategies and ROI optimization. With solutions like Cassandra and the growing accessibility of these methodologies, companies of all sizes can leverage marketing analytics to drive more informed decisions and more effective strategies.

Finally, as marketing analytics tools and techniques continue to evolve, it's critical to remain flexible and open to new opportunities. The key to a successful marketing attribution strategy is to adapt to changes, maintaining a focus on high-quality data and continuing to look for innovative ways to optimize your marketing efforts.

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