Measuring marketing effectiveness is essential for any business investing in multiple channels.
Two popular approaches – multi-touch attribution and marketing mix modeling – help marketers understand which strategies drive results.
This article tackles the key differences between each attribution method to help you determine which one best fits your business needs.
The growing need for smarter marketing attribution
With Google’s recent update to its open-source marketing mix model, Meridian, interest in marketing mix analysis and channel modeling has surged.
While enterprise brands have long benefited from these insights, smaller businesses running multi-channel marketing can also gain value.
Two leading methodologies have emerged to tackle this challenge:
- Multi-touch attribution (MTA).
- Marketing mix modeling (MMM).
Both aim to measure marketing effectiveness but differ significantly in methodology, scope, and application.
Every business investing in marketing needs to assess whether its efforts are paying off.
SEO, email campaigns, search ads, and social media all demand time and budget.
But without the right measurement approach, it’s difficult to know which channels truly drive results.
Many marketers rely on in-platform data, but this only provides a partial view due to differing attribution models and settings.
Third-party attribution tools attempt to bridge the gap, but they often favor specific marketing channels and impose predefined attribution rules, which may not align with long-term business goals.
For businesses serious about optimizing their marketing, a customized approach is essential – one that fully leverages their own data while integrating additional insights.
This is where MTA and MMM shine.
Dig deeper: 7 must-know marketing attribution definitions to avoid getting gamed
Understanding the basics
Multi-touch attribution
Multi-touch attribution is a digital-first methodology that tracks individual customer interactions across various touchpoints in their journey to purchase.
It assigns credit to each marketing touchpoint based on its contribution to the final conversion.
Operating at a granular, user-level scale, MTA collects data from cookies, device IDs, and other digital identifiers to create a detailed picture of the customer journey.
MTA is commonly supported by marketing channels like Google Ads, which offer different attribution settings – data-driven being the most recommended.
However, first and last touch models are not considered part of MTA, as they only account for a single touchpoint.
Beyond in-platform attribution, most analytics tools also support multi-touch attribution.
For SMBs with strong tracking and high data quality, these tools can be sufficient.
However, taking attribution to the next level requires a customized MTA by:
- Using a tool that allows customization.
- Or building custom attribution reports, often in combination with a data warehouse.
A tailored MTA ensures attribution is aligned with your business and customer journey, leading to more accurate insights.
The need for a customized MTA becomes clear with the following example:
Imagine a user encounters two social touchpoints – an Instagram ad and a TikTok ad – before converting through a Google Search ad.
A standard MTA might allocate 20% credit to each social channel for awareness and 60% to Google Search, assuming search played the most crucial role due to its intent-driven nature.
- Instagram ad: 20%
- TikTok ad: 20%
- Google Search: 60%
You might conclude that increasing your Google Ads budget and investing more in search is the right move.
While this could work, it could also backfire – without a customized MTA, your decision-making may be flawed.
Let’s take a closer look at the user journey to see what might be wrong:
- Instagram ad – Cold awareness: 50%
- TikTok ad – Remarketing: 40%
- Google Search – Branded search: 10%
Instead of Google Search being the primary driver, it could be that:
- Instagram is generating initial awareness.
- TikTok is handling remarketing.
- Google is simply capturing conversions from users already familiar with your brand.
In this case, increasing Google Ads spend wouldn’t necessarily drive more sales. It would just reinforce the final step while neglecting the earlier, more influential touchpoints.
With this in mind, MTA weightings can look completely different.
Investing more in cold traffic and remarketing while minimizing spend on Google Search might be the smarter approach, as search doesn’t generate demand but rather supports the last step and defends your brand against competitors.
This example highlights why a customized MTA is essential. It allows you to tailor attribution to your specific strategy, funnel, and customer journey.
However, if data quality is poor or customization is lacking, it can lead to inaccurate insights, poor decisions, and short-term thinking.
Marketing mix modeling
Marketing mix modeling, on the other hand, takes a top-down, aggregate approach.
It analyzes historical marketing spend across channels along with external factors to assess their impact on business outcomes.
Using advanced statistical techniques, MMM identifies correlations between marketing investments and results.
An effective marketing mix model incorporates both historical and current data, making it resilient to outliers and short-term fluctuations.
Depending on the model, it also allows for the inclusion of seasonal trends, industry benchmarks, growth rates, and marketing volume.
Additionally, MMM can account for brand awareness and loyalty in base sales, as well as measure incremental sales.
MTA vs. MMM: Key differences
MTA is a valuable tool for digital marketing teams that need immediate insights and real-time tracking to optimize campaigns quickly.
Its granular data helps marketers refine conversion paths and personalize customer interactions.
However, increasing privacy restrictions and the phase-out of third-party cookies make MTA more challenging to implement effectively.
Additionally, its digital-first nature means it struggles to account for offline marketing efforts and may lead businesses to prioritize short-term conversions over long-term brand growth.
MMM, by contrast, provides a broader, privacy-friendly approach that captures both digital and offline marketing performance.
It is particularly useful for long-term budget planning, helping businesses allocate resources effectively across multiple channels.
However, its reliance on historical data and aggregate trends makes it less suited for rapid campaign adjustments.
Companies that operate across both digital and traditional marketing channels may benefit from combining MTA’s real-time insights with MMM’s strategic guidance for a more balanced approach.
Dig deeper: How to evolve your PPC measurement strategy for a privacy-first future
Open-source marketing mix models
Open-source marketing mix models are widely used for several reasons.
They are free, making them an attractive alternative to expensive enterprise tools.
Another key advantage is transparency. Since these models can be reviewed, businesses are not reliant on “black box” solutions.
Some of the most notable open-source models include:
- Meridian.
- Robyn (from Meta).
- PyMC Marketing.
To determine which model best suits your needs, it’s helpful to experiment by uploading test datasets and exploring their functionalities.
While these models share a common approach, they differ in customization depth and fine-tuning capabilities.
In my experience, Meridian is the most advanced, offering deep integration with first-party, organic, and third-party data. However, its complexity may require a steeper learning curve.
For a quicker setup, Robyn from Meta is a solid starting point.
Hybrid approach
As marketing measurement evolves, organizations increasingly adopt hybrid approaches that combine the strengths of both MTA and MMM. This unified framework aims to:
- Leverage MTA’s granular digital insights for tactical optimization.
- Use MMM for strategic planning and budget allocation.
- Cross-validate findings between both methodologies.
- Provide a more complete view of marketing effectiveness.
For digital-first companies, MTA is often the preferred starting point, offering real-time insights for rapid campaign adjustments.
In contrast, businesses investing heavily in traditional marketing tend to benefit more from MMM, as it:
- Aligns with privacy regulations.
- Accounts for external factors.
- Delivers a holistic view of marketing performance.
A hybrid approach provides the best of both worlds – combining MTA’s agility with MMM’s long-term perspective.
While managing both requires additional resources, businesses implementing this strategy gain precise, channel-specific insights and a broader strategic understanding.
This dual approach is particularly valuable for organizations balancing short-term performance optimization with sustainable, long-term growth.
Boost your marketing performance with the right attribution model
Both MTA and MMM offer valuable insights into marketing effectiveness, but they serve different purposes and have distinct advantages.
As the marketing landscape becomes more complex and privacy-focused, it’s essential to assess your measurement needs and capabilities to determine the best approach – or a combination of both.
The future of marketing measurement likely lies in hybrid solutions that blend MTA’s granular insights with MMM’s strategic perspective while adapting to evolving privacy regulations and technological changes.
By integrating these methodologies, you’ll be better equipped to optimize marketing investments and drive long-term business growth.
source https://searchengineland.com/mta-vs-mmm-which-marketing-attribution-model-is-right-for-you-452368
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