It’s rare for a customer to make a purchase after seeing a singular ad. We won’t say it doesn’t happen, but it’s far more likely for a customer to become familiar with your brand through multiple advertising channels before making a purchase.
Most advertising platforms will try to take credit for a purchase made on their platform, even if they aren’t the first or last touchpoint before a sale happens. These are single-touch models that give 100% of the credit to just one interaction. Realistically, a consumer is often engaging 6 to 20 times before making a purchasing decision.
In this article, we’ll outline what multi-touch attribution is, the difference between multi-touch and single-touch attribution, the types of multi-touch attribution marketing models, and the steps for implementing multi-touch attribution for your brand.
Multi-touch attribution (MTA) is a marketing measurement methodology that assigns credit to multiple touchpoints along the customer journey, which provides a more comprehensive view of which channels and campaigns contribute to conversions, and by how much.
Unlike single-touch models that give 100% of the credit to only one interaction, multi-touch attribution recognizes that customers rarely make purchase decisions based on a single exposure to a brand. Instead, it acknowledges that multiple interactions – across various channels and over time – collectively can and will influence purchasing decisions.
Let’s say a consumer first discovers your product through an Instagram ad (first touch), then later receives a promotional email, searches for product reviews on Google, clicks on a Google Ad, then finally makes a purchase after clicking on a Facebook retargeted ad (last touch). With single attribution, the credit might go to either Instagram or Facebook (first or last touch), but with multi-touch attribution (MTA), the credit would be distributed amongst all of the touchpoints the customer engaged with rather than just one.
Often used interchangeably, multi-touch and multi-channel attribution have distinct meanings:
The key difference: multi-touch attribution is interested in individual touchpoints and their impact, whereas multi-channel attribution focuses on the effectiveness of different channels when combined.
Single-touch attribution (that assigns 100% of the conversion credit to one touchpoint) might be simpler to implement, but it significantly oversimplifies the customer journey and can lead to misallocated marketing budgets.
Here's a breakdown of the advantages and disadvantages of multi-touch attribution vs. first-touch attribution.
First-touch attribution gives all of the credit to the initial interaction that brought a customer to your brand. This model highlights which channels are the most effective at generating awareness for your brand.
The advantages of first-touch attribution are that it’s:
As for the disadvantages, first-touch attribution:
Multi-touch attribution, on the other hand, considers the first touch as only one part of the conversion path, and will assign it partial credit depending on the model used.
Last-touch attribution assigns full credit for the purchase to the final interaction before conversion, and it is the default for many analytics platforms. This model highlights which channels are most effective at driving conversions.
Last-touch attribution is also easy to implement and understand, and works well for short, simple purchase paths, and has a clear focus on conversion-driving channels. However, it completely ignores the influence of touchpoints earlier in the customer journey, tends to overvalue bottom-of-funnel activities, and can lead to under-investment in awareness and consideration stages.
Marketing mix modeling and multi-touch attribution both aim to measure marketing effectiveness, but they differ significantly in the approach to holistic analysis.
Multi-touch attribution
Marketing mix modeling
It’s possible for marketing teams to use both approaches in complementary ways: MTA marketing for tactical optimization of channels, and marketing attribution models for strategic planning and budget allocation.
Not all multi-touch attribution models are created equal, and they will evaluate how multiple touches interact in different ways. Here are the most common approaches:
The linear attribution model distributes credit equally across all touchpoints in the customer journey. Each interaction–from first touch to conversion–receives the same percentage of credit. It’s as if each interaction is a piece of the puzzle, and without any of them, the picture is incomplete. It’s also referred to as an even-weighted model, since each touchpoint is given equal credit.
The linear attribution model is simple, easy to understand, and doesn’t require any fancy algorithms. But the model’s downfall is that it doesn’t account for the fact that some interactions may have a bigger impact on customers than others.
The time decay model gives more credit to conversions that occur closer to conversion, based on the assumption that more recent interactions will have a stronger influence on the purchase decision. Kind of like how the last song you heard is more likely to be stuck in your head versus the one you heard last week.
The challenge with the time decay model is that it requires a more advanced level of data tracking and analysis, and it may overlook earlier touchpoints that planted the seed for later conversions.
Position-based models (also called “bathtub models”) assign different weights to touchpoints based on their position in the customer journey.
U-shaped attribution
The U-shaped model gives 40% credit to the first and last touchpoints, and the remaining 20% is distributed among the middle interactions. This model is best for businesses that want to emphasize both acquisition and conversion activities.
W-shaped attribution
The W-shaped model gives 30% each to three key milestones: first touch, lead creation, and conversion–and the remaining 10% is distributed amongst other touchpoints. This model is best for B2B companies with defined lead generation stages.
Z-shaped attribution
The Z-shaped model is similar to the W-shaped model, except it adds a fourth key milestone with “opportunity creation”, giving 22.5% each to first touch, lead creation, opportunity creation, and conversion, and 10% distributed among remaining touchpoints. This model is best for complex B2B sales with longer sales cycles and multiple stages before conversion. This model is also called the full path attribution model.
Using machine learning and statistical techniques, the algorithmic attribution model can dynamically assign credit based on the actual impact each touchpoint has on conversion probability. Some types of algorithmic attribution include:
Similar to algorithmic attribution, data-driven attribution uses algorithms and machine learning to analyze patterns across thousands of customer journeys. Unlike rule-based models, it adjusts the credit distribution based on what the data shows actually influences conversions. The data analysis is what determines the value of each touchpoint in the customer journey.
With a custom attribution model, a brand can define their own rules for credit distribution based on their unique business needs or customer journey. It is best for organizations with specialized marketing approaches that require unique considerations for an attribution tool.
There are several benefits to implementing multi-touch attribution tools over simpler attribution models:
Even with all of the benefits, there are still several challenges to MTA:
Implementing an effective multi-touch attribution system requires careful planning and execution. Here’s a step by step approach to setting it up:
Before implementing any attribution model, you must know what constitutes a conversion for your business. Some options include:
When you have a clear conversion goal, you can ensure you’re measuring what truly matters for your business.
Map out all possible customer touchpoints across your marketing ecosystem, including:
Make sure you have the technical infrastructure necessary to track users across touchpoints. This will require you to:
Select the appropriate attribution model for your brand based on your:
For many businesses, starting with a simpler model like linear attribution would make sense, then you can switch to more complex models as your data capabilities mature.
There are several options for how to implement MTA for your brand:
Compare results against existing data and available benchmarks:
The most important step is to use your attribution data to improve overall marketing performance:
EcoBio Boutique is an Italian cosmetic brand dedicated to combining science, technology, and eco-sustainability to enhance the natural beauty of every woman. While the brand was well-known in Italy, founders Giacomo and Simone wanted to expand to new markets, and needed the most accurate attribution and customer data possible to best serve their new audience.
With Triple Whale, EcoBio Boutique was able to gain a better picture of the full customer journey, and which customer touchpoints were most important for conversions. They discovered that Facebook is the primary first-touchpoint for customers most likely to convert, and were then able to optimize ad spend on Facebook. As a result, they also were able to adjust their advertising on other platforms like Pinterest by dedicating the channel to lower-funnel campaigns.
EcoBio Boutique was able to decrease their year-over-year blended cost per acquisition by 33%, obtain a 55% increase in year-over-year blended ROAS, and achieve a 311% boost in year-over-year profit using Triple Whale’s multi-touch attribution tools.
To get the most from your multi-touch attribution efforts, follow this advice:
Several key trends are evolving the attribution landscape, including AI and machine learning, privacy-first attribution, unified measurement approaches, and omnichannel attribution.
As machine learning algorithms continue to evolve, they are increasingly capable of identifying patterns and relationships between touchpoints that humans might miss. More sophisticated attribution models will adapt to changing customer behaviors in real-time.
The new frontier of artificial intelligence is agentic AI, an AI tool that is capable of autonomous decision-making. Agentic AI can process data, make decisions, learn from the interactions it participates in, and can proactively work to achieve goals. With agentic AI and a multi-touch attribution system, you could set up specific goals for measuring attribution, and an agent could monitor it for you while you sleep. In fact, we have agents for that.
The deprecation of third-party cookies and deeper privacy regulations have caused a giant shift towards tracking methods that preserve privacy. These include:
Many hybrid approaches between multi-touch attribution and marketing mix modeling are possible, and there are many benefits to combining approaches to leverage the strengths of both methodologies. Unified measurement frameworks combine the granular insights of MTA with the comprehensive channel coverage of MTA.
Triple Whale’s Measurement Agent compares MMM, Triple Attribution, and in-platform data to get the most holistic understanding of channel and campaign performance. These types of comparisons used to take hours of analysis, but now it can all be done instantly with the help of agentic AI.
The boundary between online and offline experiences continues to blur, attribution solutions will evolve to connect touchpoints across physical and digital environments. This is made possible by integrating location data, using QR codes in physical locations, using online/offline loyalty programs, and integrating the physical point-of-sale software with the online experience.
Multi-touch attribution represents a significant advancement over simpler attribution models, offering a more nuanced understanding of how your marketing efforts contribute to conversions. There’s no one-size-fits-all approach, however, and the right model for your brand depends on your business model, customer journey complexity, and available resources.
Understanding the strengths and limitations of different attribution models will help you select an approach that provides the insights you need to optimize your marketing performance and drive better business results. Focus on extracting actionable insights rather than pursuing perfect attribution and you’ll be well on your way to maximizing the return on your marketing investments.
Ready to gain clarity on where your customers are coming from? Triple Whale’s multi-touch attribution solutions will provide a single source of truth for your brand. Book a demo to learn more!
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