Multi Touch Attribution (MTA) vs Marketing Mix Modeling (MMM)

Marketing Mix Modeling (MMM) and Multi Touch Attribution (MTA) are two powerful and sophisticated tools used to understand the customer journey and determine which channels generate the most conversions and the highest return on investment (ROI).

Both approaches can provide valuable insights, but they differ significantly in their approach and level of precision.

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Marketing Mix Modeling

Marketing Mix Modeling is a statistical technique used to determine the impact of different marketing activities on sales. This approach looks at the combination of various marketing inputs, such as advertising, promotions, and pricing, and how they influence customer behavior.

Marketing Mix Modeling provides a more holistic view of the marketing landscape and helps businesses understand the overall impact of their marketing efforts.

The MMM approach takes into account the entire market mix and provides a comprehensive view of the relationship between sales, revenue, costs, competition, and other variables. MMM excels at providing illustrative data that reveals how every marketing initiative affects the bottom line.

With the extensive privacy changes, such as third-party cookies going away and IOS changes, many companies have turned to MMM modeling to evaluate their marketing performance.

Robyn Facebook MMM

Even media publishers such as Facebook have jumped in on MMM, with the release of an experimental, ML-powered and semi-automated Marketing Mix Modeling (MMM) open source package, called Robyn.

According to Facebook:

Robyn aims to reduce human bias in the modeling process, esp. by automating modelers decisions like adstocking, saturation, trend & seasonality as well as model validation. Moreover, the budget allocator & calibration enable actionability and causality of the results.

For marketers with 90% of their budget in Facebook, Robyn may be a great solution.

Multi Touch Attribution

MTA provides real-time insights into consumer behavior by tracing the customer journey across a digital landscape and revealing touchpoints of engagement. With its real-time analysis, MTA is perfectly positioned to allow for the granular tracking and analysis of consumer behavior. However, MTA is limited by the complexity of the algorithms used to track the data, as well as the difficulty in measuring consumer behavior across walled gardens and offline.

With the introduction of new privacy features in iOS 14 and later, Apple has made changes to the way data is collected and shared, which has impacted the ability of advertisers to use MTA effectively. Specifically, the changes have affected the ability of advertisers to track users across multiple apps and websites, and to access certain types of data about their users.

Here are some of the main issues with MTA post-iOS and privacy:

• Limited data availability: With the new privacy features, Apple now requires apps to ask users for permission to track them across other apps and websites. This means that advertisers have less access to the data they need to perform MTA effectively.
• Incomplete data: Even when users do consent to tracking, they may not be tracked across all channels and touchpoints, as some data sources may be restricted by the new privacy features.
• Inaccurate data: With less data available, there is a higher risk of attribution errors and inaccurate measurement, which can lead to poor decision-making and ineffective marketing strategies.
• Limited measurement options: With the new privacy features, many of the traditional methods of MTA are no longer available. This has led to the development of new measurement approaches, such as Provalytics.
• Increased complexity: Advertisers now need to navigate a more complex privacy landscape, which requires new tools and processes to ensure compliance and protect user privacy.

While MTA is still an important tool for measuring marketing effectiveness, the changes brought about by iOS and privacy regulations have made it more challenging to use effectively.

Advertisers will need to adapt their measurement strategies to account for these changes, and find new ways to accurately and reliably measure the impact of their campaigns.