As your company’s Head of Performance Marketing, you’ve always been well-regarded for your analytical prowess and strategic acumen. You’ve always prided yourself on staying ahead of the curve, leveraging the latest tools and technologies to drive marketing success. With Multi-Touch Attribution (MTA) becoming the industry buzzword, you’ve been quick to integrate it into your strategy. And with Google’s Data-Driven Attribution (DDA) becoming an industry standard, you’ve been quick to adopt it. 

You’ve been long convinced MTA/DDA provide the insights needed to optimize your marketing efforts across different channels. However, despite your team’s best efforts, your campaigns’ results are disappointing, and your competitors are growing stronger day by day. You’re probably wondering if there’s a better way.

What is MTA in Marketing?

Multi-Touch Attribution (MTA) is a method used in digital marketing to track and assign credit to the multiple touchpoints a customer interacts with on their journey to conversion. Unlike traditional single-touch models, which give all credit to either the first or last interaction, MTA models aim to provide a more comprehensive view by distributing credit across various interactions a customer has with a brand. This can include clicks on ads, email opens, social media engagements, and website visits, among others.

Why Multi-Touch Attribution Became Widely Adopted

MTA gained popularity among Performance Marketing teams due to its promise of precision and comprehensiveness. Marketers were excited by the prospect of understanding exactly how each channel contributed to a sale. This granular insight was seen as the key to optimizing marketing budgets, ensuring every dollar spent was accounted for and effectively utilized. Additionally, MTA promised to eliminate the guesswork from marketing, providing a data-driven foundation for decision-making. With marketing becoming increasingly complex and multi-faceted, MTA appeared to be the ultimate solution for dissecting the customer journey and attributing value accurately across all touchpoints.

The Misleading Nature of Multi-Touch Attribution Models

Despite its seductive façade, MTA has several fundamental flaws that can mislead marketing strategies and lead to suboptimal performance.

The goal of perfect attribution is inherently flawed. MTA relies on deterministic identifiers to track user interactions, but these data sets are often incomplete. Privacy regulations such as Apple’s App Tracking Transparency (ATT) and Google’s Privacy Sandbox have further hampered the ability to track users across multiple platforms. This incomplete data can lead to inaccurate measurements and misguided decisions.

The complexity of setting up MTA can shift the focus from understanding customer needs to merely improving measurable metrics. This often results in marketing strategies that are data-driven but lack common sense and creativity. Marketers might end up optimizing for metrics that do not truly drive business growth, neglecting important but less measurable aspects of the customer journey.

Another significant issue is the assumptions embedded in MTA models. These models often rely on arbitrary rules to assign credit to touchpoints, which may not reflect the true value of each interaction. For example, position-based models that give most credit to the first and last interactions can overlook the critical influence of middle touchpoints.

MTA’s focus on measurable digital interactions also ignores the impact of offline channels and external factors such as seasonality, economic conditions, and word-of-mouth. This narrow focus can lead to a skewed understanding of what truly drives conversions.

Ultimately, relying solely on MTA can lead to misguided budget allocations, over-investing in channels that appear to perform well in the model but do not actually drive significant business outcomes. Marketing leaders who continue to depend exclusively on MTA are making a critical mistake, failing to adapt to the evolving landscape of data privacy and consumer behavior.

Deep Dive: 11 Flaws of Multi-Touch Attribution Models

1. Flawed Assumptions. MTA models falsely assume that marketing touchpoints exist independently of each other. In reality, each touchpoint is influenced by preceding interactions, either positively or negatively. This interconnectedness means eliminating seemingly ineffective interactions can reduce the power of highly effective interactions that came before them.

2. Complexity and Setup. Implementing and interpreting personalized MTA models (the kind of models that would provide the most insights for decision-making) is challenging due to their complexity. This process requires sophisticated data analytics and a clear understanding of customer touchpoints. The intricate setup often leads to overcomplicating the process and causing information overload, which can be counterproductive for marketers.

3. Privacy and Data Regulation. The rise of privacy regulations such as GDPR, CCPA, and Apple’s App Tracking Transparency (ATT) has significantly impacted the viability of MTA. These regulations make it difficult to track user behavior across multiple platforms, limiting the completeness and accuracy of the data needed for effective attribution.

4. Inaccurate Measurements. MTA models often rely on unproven assumptions and guesswork to assign credit to touchpoints. For instance, models like the U-shaped approach, which assumes the first and last touches are most crucial, are more heuristic than factual. This can lead to inaccurate insights and misguided marketing decisions.

5. Inability to Measure External Factors. MTA models cannot measure external factors like word-of-mouth, pricing, seasonality, competitor activities, offline promotional events, or even certain media like “dark” social channels. This limitation prevents marketers from gaining a comprehensive view of their overall marketing effectiveness, as significant channels and external factors are often overlooked.

6. Focus on Measurable Metrics. The emphasis on measurable metrics can lead marketers to focus on improving these metrics rather than on tactics that genuinely move the needle with their buyers. This focus can result in a lack of common sense and creativity in marketing strategies, as the more nuanced and qualitative aspects of marketing are neglected.

7. Incremental Revenue. MTA does not account for incremental revenue from existing customers. Not having this critical variable factored into the attribution model can lead to misallocating spend towards non-incremental ads/campaigns/channels.

8. Arbitrary Impact on Revenue. The assignment of marketing dollars to different channels based on MTA data can be arbitrary and difficult to navigate, especially when channels have few conversions. This can lead to marketing dollars being allocated to channels that do not significantly impact the conversion path, thereby wasting budget and resources.

9. Attributed ROI is Not True ROI. Attributed ROI in MTA models is not the true ROI because it only reflects the attributed value of touchpoints within the digital ecosystem. It does not account for the actual business outcomes or the broader impact of marketing efforts on brand equity and customer lifetime value.

10. Focus on Last-Touch Conversions. Whether it’s last-click or multi-touch, these models show the best results when used for immediate sales, as they overlook users who convert outside the attribution window, or on different devices, or after cookies expire. This narrow focus can miss significant portions of the customer journey and lead to incomplete insights.

11. Suitability and Lack of Customization of Pre-Built Models. Pre-built MTA models may not work well for all businesses, as their effectiveness depends on the nature of products or services, industry, and available data. The lack of customization in these models means they may not accurately reflect the unique customer journeys and marketing dynamics of different businesses.

Why Performance Marketing Teams Still Rely on MTA

Despite its flaws, many Performance Marketing teams continue to rely on MTA due to its convenience and industry acceptance. MTA provides easily accessible, daily metrics that do not require deep Marketing Analytics knowledge or the support of a Data Science team. It’s a tool that fits well within the existing frameworks and workflows of many marketing departments. Furthermore, the simplicity and immediacy of pre-built, non-customized MTA reports can be appealing in fast-paced environments where quick decision-making is crucial. This ease of use and industry familiarity keep MTA in play, even as its limitations become increasingly apparent.

Google’s Shift from Many Attribution Models to DDA

Recognizing the limitations of traditional attribution models, Google has been pushing for the adoption of Data-Driven Attribution (DDA). In 2024, Google deprecated most of its existing attribution models in favor of DDA, which leverages a proprietary Machine Learning algorithm to assign credit to touchpoints based on their actual impact on conversion outcomes as measured by Google. This shift is driven by the need to adapt to data privacy changes and provide a more accurate picture of customer journeys. DDA uses aggregated and anonymized data, which helps mitigate privacy concerns while still offering valuable insights into marketing performance.

Simply Switching to DDA Is Not the Solution

The transition to DDA has however not resolved all the common issues with MTA models we just analyzed. In fact, it has introduced new challenges. Lazy marketers and managers, who previously relied on the flawed simplicity of MTA models but at least were probably comparing multiple models one vs. the other before taking decisions, now tend to place blind trust solely in Google’s DDA model. This over-reliance on a black-box algorithm can be dangerous. Without a clear understanding of how DDA works, marketing leaders may take decisions based on incomplete or misinterpreted data. The opacity of DDA inner workings means that marketers cannot easily verify or challenge the attributions made, potentially leading to misguided strategies.

The limitations of MTA and DDA highlight the need for a more comprehensive and integrated approach to marketing measurement, adopting other methodologies like Media Mix Modeling and Incrementality Testing to achieve a holistic understanding of marketing effectiveness.

The Need for Combining MTA with MMM and Incrementality Testing

To navigate the complexities of modern marketing, it is crucial for leaders to pair MTA with Media Mix Modeling (MMM) and Incrementality Testing.

MMM provides a top-down approach, analyzing aggregate data to measure the overall impact of different marketing channels, both online and offline. This method takes into account external factors such as seasonality and market conditions, offering a more holistic view of marketing effectiveness.

Incrementality Testing, on the other hand, helps determine the actual causal impact of marketing activities. By running controlled experiments, marketers can isolate the effects of specific campaigns or channels, ensuring that the observed outcomes are genuinely driven by marketing efforts and not by external factors or pre-existing trends.

Combining these methodologies allows for a comprehensive understanding of marketing performance.
While MTA offers bottom-up, granular albeit incomplete insights that can be valuable for the short-term, MMM and Incrementality Testing provide the broader context needed to make informed decisions for the long-term.
This multi-faceted approach ensures that marketing strategies are grounded in reality, taking into account both detailed interactions and overarching market dynamics.

The Difference Between MTA and MMM

MTA and MMM serve different purposes and operate on different levels of analysis.

MTA focuses on individual touchpoints within the customer journey, attempting to assign credit to each interaction. It is useful for understanding the detailed pathways that lead to conversions and for optimizing specific channels or campaigns.

MMM, in contrast, takes a macro-level view, analyzing the impact of various marketing channels on overall sales and brand performance. It incorporates a wide range of data, including offline marketing activities and external factors. This approach helps in strategic decision-making, such as budget allocation and long-term planning.

While MTA is concerned with granular, day-to-day interactions, MMM provides the strategic big picture, making it essential to use both models in tandem to achieve a balanced and effective marketing strategy.

But even just pairing MTA with MMM is not enough.
A successful marketing strategy needs to employ both MMM and Incrementality Testing.

Simplifying the Process with BlueAlpha

For marketing leaders feeling overwhelmed by the complexity of integrating MTA, MMM, and Incrementality Testing, we offer a streamlined solution. You don’t need to become a data scientist or to hire a team of analysts to step up your game. We provide user-friendly tools and expert support to make the adoption of advanced measurement techniques simple and accessible.

By partnering with us, your team will be able to leverage cutting-edge analytics without the need for extensive technical expertise. This collaboration allows marketers to focus on strategy and creativity, confident that their measurement framework is robust and reliable.

While MTA can offer quick insights valuable for the short-term, its limitations and the evolving landscape of data privacy and consumer behavior necessitate a more comprehensive approach. By integrating your favorite MTA models with MMM and Incrementality Testing, you can achieve a balanced view of your performance, making informed decisions that drive real business outcomes.

Working with us can make this integration seamless, empowering you to optimize your marketing strategies with confidence.

It’s time for marketing leaders to embrace this holistic approach and move beyond the narrow confines of MTA, ensuring their strategies are both data-driven and contextually informed.