Multi touch attribution: Meaning, Criticisms & Real-World Uses

What Is Multi-Touch Attribution?

Multi-touch attribution (MTA) is a marketing analytics methodology that evaluates the impact of various marketing channels and customer interactions, known as touchpoints, on a consumer's customer journey leading to a desired outcome, typically a conversion. This approach, falling under the broader category of marketing analytics, moves beyond crediting a single touchpoint and instead distributes credit across multiple interactions. By doing so, multi-touch attribution aims to provide a more holistic understanding of how different elements of a digital marketing strategy contribute to results. It allows businesses to assess the collective effectiveness of their advertising campaigns and optimize future marketing investments to achieve a higher return on investment (ROI).

History and Origin

The concept of attributing marketing effectiveness has evolved significantly, initially rooted in more traditional methods like marketing mix models (MMMs) which emerged in the 1950s and gained popularity by the 1980s. These models provided a broad understanding of how various marketing elements influenced sales. However, with the advent of the internet and the proliferation of digital channels in the late 1990s and early 2000s, the customer journey became increasingly complex, spanning multiple online interactions¹⁰. The limitations of single-touch models, which assigned all credit for a conversion to just one interaction, became apparent⁹.

In the mid-2000s, multi-touch attribution models emerged as a response to this complexity, seeking to assign value to each touchpoint along the customer journey⁸. This development was crucial as marketers needed a more nuanced view of how different digital efforts contributed to conversions. The Digital Marketing Institute notes that while early attribution models were slow and less suited for the real-time nature of online marketing, the evolution towards multi-touch attribution allowed for a more comprehensive assessment of digital interactions⁷.

Key Takeaways

Multi-touch attribution (MTA) provides a comprehensive view of the customer journey by assigning credit to all touchpoints that contribute to a conversion.
Unlike single-touch models, MTA acknowledges that multiple interactions influence a consumer's decision-making process.
Implementing MTA enables marketers to optimize their budget allocation by identifying the true value of each marketing channel.
Various multi-touch attribution models exist, each distributing credit differently, from equal weighting to data-driven approaches.
The effectiveness of multi-touch attribution relies heavily on robust data analytics and the ability to integrate data across diverse platforms.

Multi-Touch Attribution Models and Calculation Principles

Multi-touch attribution does not rely on a single, universal formula but rather encompasses a variety of models, each with its own method for distributing credit among touchpoints. These models range from rule-based systems to sophisticated algorithmic models often employing machine learning. Understanding these different approaches is key to interpreting how multi-touch attribution works in practice.

Common multi-touch attribution models include:

Linear Attribution: This model distributes credit equally across all touchpoints in the customer journey. If there are (n) touchpoints leading to a conversion, each touchpoint receives (1/n) of the credit.
First-Touch Attribution: Assigns 100% of the credit to the very first interaction a customer had with a brand.
Last-Touch Attribution: Assigns 100% of the credit to the final interaction immediately preceding the conversion.
Time Decay Attribution: This model gives more credit to touchpoints that occurred closer in time to the conversion. Older interactions receive less credit.
U-Shaped Attribution: Credits the first and last-click attribution touchpoints more heavily (e.g., 40% each), with the remaining 20% distributed evenly among the middle touchpoints.
W-Shaped Attribution: Similar to U-shaped, but also gives significant credit to a "middle" touchpoint, such as the point of lead creation.
Data-Driven or Algorithmic Models: These are often the most complex and accurate, using statistical modeling or machine learning to assign credit based on the actual observed data and the probability of conversion. They may use concepts from game theory, such as Shapley values, or causal inference to determine the true contribution of each touchpoint⁶,⁵.

These models essentially represent different hypotheses about which interactions are most influential. The choice of model impacts how success is measured and how budget allocation decisions are made.

Interpreting Multi-Touch Attribution

Interpreting multi-touch attribution insights involves understanding that the assigned credit reflects a specific model's view of a touchpoint's influence. It moves beyond simplistic metrics by revealing the interplay of various marketing channels throughout the customer journey. For instance, a channel that consistently receives little credit under a last-touch model might be revealed as crucial for initial awareness under a first-touch or linear model.

Analysts use MTA data to gain a deeper understanding of how different marketing activities contribute to a conversion. This enables marketers to shift focus from solely optimizing for the final interaction to nurturing customers through a series of relevant engagements. The insights derived from multi-touch attribution help to identify which channels are effective at different stages of the funnel, from building initial awareness to driving the final purchase.

Hypothetical Example

Consider an online retailer, "GearUp," selling sporting goods. A customer, Sarah, is looking for a new bicycle. Her journey to purchase might look like this:

Day 1: Sarah sees a GearUp ad on a social media platform. (Social Media Touchpoint)
Day 3: Sarah performs a Google search for "best road bikes" and clicks on a GearUp organic search result. She browses the site but doesn't buy. (Organic Search Touchpoint)
Day 5: Sarah receives an email from GearUp promoting a sale on bicycles, having previously signed up for their newsletter. She clicks the email, adds a bike to her cart, but abandons it. (Email Marketing Touchpoint)
Day 7: Sarah searches directly for "GearUp bikes" and clicks on a paid search ad. She completes the purchase. (Paid Search Touchpoint)

In a traditional last-click attribution model, the Paid Search touchpoint would receive 100% of the credit for the sale. However, multi-touch attribution provides a more nuanced view:

Linear Model: Each touchpoint (Social Media, Organic Search, Email Marketing, Paid Search) would receive 25% of the credit for the conversion.
Time Decay Model: Paid Search would receive the most credit, followed by Email Marketing, then Organic Search, and finally Social Media, reflecting their proximity to the purchase.
U-Shaped Model: Social Media (first touch) and Paid Search (last touch) would receive a significant portion of the credit, with Organic Search and Email Marketing sharing the remainder.

This multi-touch attribution analysis allows GearUp to recognize that while the paid search ad closed the deal, the social media ad sparked initial interest, organic search provided information, and email marketing brought Sarah back into the sales funnel. This comprehensive view helps GearUp optimize future advertising campaigns more effectively.

Practical Applications

Multi-touch attribution is a critical tool for marketers seeking to optimize their strategies and maximize the effectiveness of their spending. Its practical applications span several key areas within digital marketing:

Optimizing Marketing Spend: By understanding the true contribution of each channel, businesses can make informed decisions about where to allocate their budget allocation. Instead of over-investing in channels that merely close sales, they can also support those that initiate the customer journey or nurture leads. The American Marketing Association highlights that MTA models empower managers to understand the direction and size of their advertising effects, leading to more informed decisions for marketing dollar allocation⁴.
Enhancing Customer Journey Understanding: MTA provides a detailed map of how customers interact with a brand across various touchpoints. This granular insight helps in tailoring content and messaging to specific stages of the funnel.
Improving Campaign Performance: Marketers can identify underperforming channels that are not contributing to conversions as expected, or conversely, channels that are highly effective but perhaps undervalued in a single-touch model. This leads to more targeted and efficient advertising campaigns.
Personalization: With a clearer picture of touchpoint effectiveness, businesses can create more personalized experiences, delivering the right message at the right time based on the customer's previous interactions.

These applications enable organizations to gain a competitive edge by making data analytics-driven decisions that move beyond anecdotal evidence or last-click biases.

Limitations and Criticisms

While multi-touch attribution offers significant advantages over single-touch models, it is not without its limitations and criticisms. A primary challenge lies in the complexity of data collection and integration. Tracking every marketing channel and customer interaction across diverse platforms, often managed by different entities, can be incredibly challenging due to disparate databases, data formats, and reporting standards³.

Furthermore, the increasing focus on consumer privacy and regulations like GDPR and CCPA, along with browser changes like cookie deprecation, significantly impact the ability to accurately track users across the customer journey. As third-party cookies diminish, traditional multi-touch attribution methods that rely heavily on cookie-based tracking face significant hurdles, potentially rendering them less effective². This shift necessitates new, privacy-centric measurement approaches.

Another criticism revolves around the inherent assumptions of the models themselves. Rule-based models (like linear or time decay) apply predefined logic that might not accurately reflect actual customer behavior. Even algorithmic models, while more sophisticated, depend on the quality and completeness of the input data analytics. Issues such as data silos, missing data points, and the difficulty in connecting online and offline touchpoints can lead to incomplete or misleading attribution insights. Some researchers also note challenges in providing enough interpretation for why certain data-driven methods work, leading to a "black box" problem¹.

Moreover, multi-touch attribution primarily focuses on quantifiable digital interactions, often struggling to account for the impact of offline marketing efforts, brand perception, or external factors that influence a conversion. This can lead to an incomplete picture of overall marketing effectiveness.

Multi-Touch Attribution vs. Last-Click Attribution

The key distinction between multi-touch attribution and last-click attribution lies in how credit is assigned for a conversion. Last-click attribution is a simple, single-touch model that gives 100% of the credit for a conversion to the very last touchpoint a customer interacted with before making a purchase or completing a desired action. For example, if a customer clicks a paid search ad and immediately buys, the paid search ad gets all the credit.

In contrast, multi-touch attribution acknowledges the complex nature of the customer journey, where consumers often interact with multiple marketing channels before converting. Instead of attributing success to just one touchpoint, multi-touch attribution distributes credit across all or several of the interactions that led to the conversion, based on various defined models (e.g., linear, time decay, U-shaped, or data-driven). While last-click attribution is easy to implement and understand, it often undervalues channels that initiate interest or nurture leads early in the funnel. Multi-touch attribution, though more complex, aims to provide a more accurate and holistic view of each channel's contribution to the overall return on investment (ROI).

FAQs

What is a marketing "touchpoint"?

A marketing touchpoint is any interaction a potential customer has with a brand or its marketing efforts. This can include seeing an ad, visiting a website, opening an email, engaging on social media, or even speaking with a salesperson. Multi-touch attribution analyzes sequences of these touchpoints.

Why is multi-touch attribution important?

Multi-touch attribution is important because it provides a more accurate understanding of which marketing channels genuinely contribute to a conversion. It moves beyond simplistic views, allowing businesses to optimize their budget allocation and improve the effectiveness of their advertising campaigns by recognizing the value of all interactions in the customer journey.

What are the main types of multi-touch attribution models?

The main types of multi-touch attribution models include linear (equal credit to all touchpoints), time decay (more credit to recent touchpoints), U-shaped (more credit to first and last touchpoints), W-shaped (credit to first, last, and mid-journey touchpoints), and data-driven or algorithmic models that use advanced data analytics to assign credit based on observed performance.

Can multi-touch attribution track offline interactions?

Traditionally, multi-touch attribution models excel at tracking digital interactions. Integrating offline data (like in-store visits or phone calls) into MTA models presents a significant challenge due to difficulties in connecting disparate data sources and identifying individual customer journeys across online and offline environments. Some advanced solutions attempt to bridge this gap, but it remains a complex area.

How does multi-touch attribution help with budget allocation?

Multi-touch attribution helps with budget allocation by providing insights into which channels are most effective at different stages of the customer journey. This allows marketers to strategically invest in channels that are initiating interest, nurturing leads, or driving final conversions, leading to a more efficient deployment of marketing resources and a higher overall return on investment (ROI).