What is multi-touch attribution? Exploring the models and their benefits
Adjust Team, Content & Insights, HQ, Mar 04, 2022.
Introduction
Multi-touch attribution has never been more in-demand, with marketers needing in-depth data from all corners of the mobile marketing ecosystem.
As marketers become more aware of the importance of touchpoints, they desire to understand their campaigns' impact on a single conversion. This article covers how multi-touch attribution works and compares to other models.
Part 1
First touch attribution model vs. last touch attribution
Most businesses today utilize a few common types of modeling, or even their own bespoke model if they’re working with more complicated campaigns. The two most common models are first-touch and last-touch attribution. Let’s first review these two to better understand the multi-touch attribution model.
Many attribution providers use last-touch attribution as the default means of tracking conversions. Here, the last campaign or impression that leads to an install gets all of the conversion credit. Therefore, even if a user views multiple different ads — such as a Google campaign followed by an Apple Search Ad, and finally a Facebook post — Facebook would receive 100% of the credit.
Part 2
Multi-touch attribution defined — and what makes it different
By contrast, multi-touch attribution (MTA) takes all touchpoints into account when weighing credit for an install. For instance, if a user installs an app after seeing three advertisements from three different sources such as Google Search, Facebook, and Apple Search Ads, those publishers would all be given some credit for the install.
How the credit is attributed depends on the model the advertiser utilizes.
Part 3
Exploring the benefits of multi-touch attribution
Multi-touch attribution gives mobile marketers a greater understanding of what led to an app install by providing more data on the entire chain of events that led to a conversion.
Marketers know that it’s not always just one touch that leads to an install, as users will often view many ads before they hit “download.” Therefore, knowing which ads appear to users and help lead to a successful conversion helps inform decisions, improving overall performance.
Yury Bolotkin, UA Manager at Wooga, knows this all too well. “Having the full picture in hand helps improve marketing strategies and allocate budgets towards legitimate sources. From my perspective, multi-touch attribution is an essential piece in the understanding the actual journey of a user on the way to the conversion point.”
Part 4
What are limitations in multi-touch attribution?
The crux of multi-touch attribution is that credit is given across the entire user journey, whether ad impressions, clicks, or conversions. This attribution model aims to identify ad campaigns that create an uplift of conversions for other ads while also crediting everyone involved in an install.
Theoretically, we would expect to see a higher click-through rate (CTR) for users that have previously viewed another ad. The conversion rate from landing on an app store page to clicking the download button is also likely to be driven by the app page content and the quality of the user. However, metrics for multi-touch attribution are difficult to obtain as several problems plague this attribution model.
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Installs are overrated
The first problem with multi-touch is defining the conversion event made possible by a series of ads. Most often, multi-touch solutions focus on app installs as the conversion goal. However, the user's intent to install should be weighed much less than a purchase decision, for example. It’s quite a stretch to assume that a "low intent" conversion like downloading a free app is the same as spending money in-app.
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No visibility into ad delivery
In multi-attribution, we expect the uplift effect of Ad A to be represented in a cohort of users who have seen Ad A and are now more likely to click Ad B. The issue is that third-party mobile attribution companies often only see a tiny fraction of engagements. This means the sample data is incomplete and skewed, opening the door for spoofed engagements.
Note that the introduction of Click Validation has vastly improved multi-touch capabilities. By requesting an impression before the click, it’s possible to check if the same device made a matching engagement. This leads to more accurate attribution and less budget wasted on spoofed engagements.
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Assisted installs can exaggerate numbers
Multi-touch attribution later developed the assisted install in which all touchpoints up to a user's final click receive credit. Assisted installs vastly increase attribution numbers, reducing effective CPI. Therefore, networks love this metric, especially as one install can create multiple assisted installs. But this means that metrics for assisted installs are often nothing but inflated numbers causing marketers to over-value networks that managed to send the most clicks—regardless of any actual uplift.
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Fraud is prevalent
Imagine a user with ten false clicks in the past few days and one legitimate click in the last five minutes. Instead of giving full credit to the only real click, the fraudulent activity earns a huge chunk of assisted installs. Fraudulent activity often occurs in multi-touch attribution as most don’t utilize a complex model to determine if an engagement directly increases the chances of a user converting.
Adjust is one of the few attribution providers that actively filters out click spam. Our distribution modeling distinguishes engagements that didn't affect the user.
Part 5
Why multi-touch attribution isn’t dead
With this many problems plaguing multi-touch attribution, it’s easy to wonder if it’s worth the effort. We think so. When done correctly, multi-touch attribution can provide greater insight across the entire user journey.
That’s why Adjust launched its Multi-Touch solution in 2019. Adjust Multi-Touch provides a more granular look into the direct contributions of each network and campaign, revealing which are generating conversions and uplift. These insights can inform budget reallocation and campaign optimization.
Part 6
The 5 models of multi-touch attribution
Understanding how multi-touch attribution works can be challenging as it’s an umbrella term for a variety of different models. This variety exists because marketers have to weigh credit differently depending on vertical, how they monetize, etc. As such, the split is rarely even. Therefore, different models have been developed to attribute credit depending on the needs of the business.
Let’s focus on the five most popular models:
- Linear
- Time decay
- U-shaped
- W-shaped
- Custom
The linear model
The linear model is the simplest way of applying multi-touch attribution. This model gives all interactions the same credit for the conversion. There is no difference between the assigned weights. The even totals are calculated by dividing the whole value by the total number of touchpoints along the path to purchase.
Why would a marketer choose a linear model?
For simplicity’s sake, the linear attribution model is useful for those who don’t view one touchpoint as more important than another.
However, some may consider this model too simplistic. For instance, the linear multi-touch attribution model doesn’t consider the importance of key touchpoints like first and last touch, which might arguably have more weight than the many impressions in the middle of the funnel.
The time decay model
This model gives more conversion credit to interactions that occur closer to the conversion event.
Why would a marketer select the time decay model?
Early touchpoints may not be the most important if a conversion takes some time. Once the prospect is in the pipeline, the focus is to nurture them, placing greater importance on the interactions connected to conversion.
The disadvantage to some marketers may be that this model covers the entire cycle, while they would prefer a model that gives more credit to fewer key milestones. One of these types is the U-shaped multi-touch attribution model.
The U-shaped model
This U-shaped model focuses on two critical milestones while also considering the middle touchpoints. In this scenario, the first touch is given 40% of all credit, while the last touch is also given 40%. The remaining 20% of the credit is divided across the middle touchpoints that occur between those two key stages.
This model can shed light on which partners are better suited to bringing awareness and which are better at bringing in installs.
Why would a marketer choose this model?
The U-shaped model is preferred by marketers who see the entry and exit points as the most influential.
However, if you view that middle touchpoints are just as influential as first and last touch, then you’ll be providing less credit to them than you’d like. The W-shaped model addresses this issue.
The W-shaped model
W-shaped multi-touch attribution modeling is similar to U-shaped, but the model covers additional key touchpoints evenly, distributing the rest of the credit to between-stage moments.
The first and last touchpoints, together with the middle touchpoint, known as lead-creation, each receive 30% of the credit, while the remaining middle touchpoints receive the remaining 10%.
Why would a marketer choose this model?
For some longer cycles, it makes sense to weigh in an extra milestone and even out the credit if it’s significant to the overall model. There are even possibilities to change the credit levels. For example, a marketer could make the first touch or last touchpoint weigh 25% and 35%.
However, take caution when building your model, as the more complicated your model, the more prone it is to errors.
The custom model
If a business has already created extensive touchpoint tracking, it will also likely need to adjust the weighting of its attribution model to fit its individual reporting needs.
This is why some advertisers opt for custom models in which they can set the various weighting of each touchpoint, as seen below.
Why would a marketer utilize a custom model?
Because no two companies are the same, varying by business model, vertical, or monetization strategy, a company is often rewarded by forming its own attribution model.
Again, by increasing the complexity of a model, the likelihood of errors also increases. Furthermore, such models need to be optimized extensively as new data comes in. In short, such a model would require a lot of hard work, time, and money to get right, something not many app businesses can spare.
Picking your model requires the right balance between your business needs and available resources, something we’ll address in the next section.
Part 7
6 best practices for B2B multi-touch attribution
If you’re getting started with multi-touch attribution, there are some basic requirements to consider, from data issues to project management. Here’s a quick checklist of best practices.
1. Change the way you think
Multi-touch attribution is quite a big step up from traditional performance data, so it’s important to manage expectations.
You’ll need to revise how you interpret your results, as the impact that one channel has on another is often underestimated, especially if you previously reviewed results in isolation from each other.
Therefore, it’s critical to ensure that your team knows that the parts are no longer separate. To do that, you need to teach them about multi-touch.
2. Educate key stakeholders
An early step to a successful implementation is ensuring that all stakeholders understand what multi-touch attribution is, what it will mean to the business, and what a good outcome looks like.
3. Create new, shared KPIs
Speaking of success, it’s critical to create a few KPIs to ensure you’re heading towards your goal. Since multi-touch is so broad, it will likely tie stakeholder targets together, and so your team needs to agree on a shared set of KPIs. This will allow an integrated view of performance.
4. Plan how to handle your data
Your data will now span across multiple campaigns and touchpoints and require extra handling when you’re getting set up.
Creating a blueprint that lists the types of data, the sources, collection methods, format, and so on will help clarify tasks and mitigate some early mistakes.
5. Start small
You shouldn’t rely on multi-touch from the start, but instead, slowly build campaigns, test, and make sure implementation is correct. Learn from the repeated testing of smaller campaigns.
If everything works out, you’ll also have the perfect internal case study to help convert more skeptical team members.
6. Optimize!
The essence of multi-touch is the understanding that many touchpoints drive performance together. That means there’s much more scope to tweak and improve between each and improve your conversion funnel on a microscopic level.
Your team will need to look at ways to improve the effectiveness of different elements of your campaigns, from ad sizes to copy and creative executions. The examination of your campaigns will likely be at a granular level that wasn’t perhaps quite so necessary as before. However, as you’ll discover, minor adjustments can have a big impact.
Part 8
How does Adjust Multi-Touch work?
Adjust Multi-Touch works in real-time to send each touchpoint along the customer journey to your BI system or S3 bucket. This makes it possible to map those engagements in your attribution model, providing additional insights to inform and optimize campaign performance.
Adjust’s Co-founder, Paul H. Müller, says, “Multi-touch attribution has never been more in-demand. By showing all the touchpoints that contributed to a conversion, Adjust Multi-Touch reflects the way today’s users interact with advertising. Marketers can use Multi-Touch to evaluate which channels and campaigns attract users’ attention and convert, and then invest in the experiences proven to drive growth—making it indispensable for data-driven businesses who want to identify where the value is generated on their customers’ journey.”
With this in mind, let’s look at the various benefits of Adjust Multi-Touch.
What can you learn with Adjust Multi-Touch?
Marketers will use Multi-Touch data to measure the effectiveness of each network and campaign. By seeing every tracked engagement that led to conversion, you can:
- Learn how users interact with your advertising.
- identify where value is generated throughout the entire customer journey.
- Measure which campaigns bring the most uplift.
- Gain full visibility into marketing tactics from the top to the bottom of the funnel.
- Spot trends and correlations between networks driving conversions.
- Receive raw, unfiltered, unopinionated data.
Adjust Multi-Touch also benefits from no limitations to the number of touches reported. Every click or impression that contributes to an install, session, event or reattribution is reported, even if it occurs outside of your attribution window. Additionally, the feature provides touchpoints claimed by self-attributing networks, a necessity for showing campaign performance.
Discover the true value of each network
In the Adjust Mult-Touch, you can map every engagement and effectively observe the path to conversion. This allows you to spot trends that you would otherwise have missed.
For example, you could discover a valuable correlation between two networks. If your data shows that users are more likely to install an app by clicking on an ad from network A after seeing a certain series of banners from network B, you’ve proven that network B creates uplift for network A. You could then use this insight to assign value to networks that are significantly contributing to conversions, even though they aren’t getting the last click.
In this way, Adjust Multi-Touch allows you to measure the success of partners who focus on promotion and awareness, as well as finding the best performing correlation between channel, network, and campaign. You can also use this insight to eliminate ineffective touchpoints and optimize your campaigns alongside the user journey.
To learn more about bringing multi-touch attribution to your marketing strategy, download Adjust’s Multi-Touch Guide.
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