What is Multi-Touch Attribution?

12/07/2021

What is Multi-Touch Attribution (MTA)?

 

Multi-touch attribution (MTA) is a marketing measurement approach that attempts to track users across devices and the ads they’ve seen in order to determine how the ads contribute to the path to purchase. This marketing attribution approach requires user identity data and because of this, it can only be used to track addressable media (mainly digital advertising) and cannot measure traditional media such as print, TV, radio and OOH. Also, it cannot measure conversions where the consumer’s identity is not known (in-store cash transactions for example).

 

 

How Does Multi-Touch Attribution Work?

 

To make MTA work, a brand must align ad impressions with consumer and device identity data in order to know “who saw which ad” and whether a conversion occurred. Traditionally this was accomplished using cookie data but this method has all but been shut down as a result of browser cookie blocking. Now, a brand must use 3rd party identity graphs along with their own first party data to attempt to stitch this picture together. Complicating this are major technology industry changes from Apple’s iOS 14.5 update that has blocked a major portion of mobile device tracking and Google’s coming FLoC initiative which will stop all consumer level targeting and tracking for Google-owned ad networks and Chrome.

 

Once a consumer’s identity has been matched to an ad, a device, and a purchase transaction, the multi-touch attribution solution must then compare conversion performance of those customers who’ve seen the ad with those consumers who have not in order to determine whether the ad has generated any true lift. Compounding this complexity is that the MTA solution must also compare sequences of ads seen, keep the identity straight across multiple devices and the order of the ads seen to be able to measure the magic combination of ads that leads to the highest conversion lift. Doing this with any level of accuracy requires evaluating billions of comparisons as even a small set of campaigns across channels generates an enormous number of potential sequences and combinations. And this is fraught with accuracy problems when even a small number of conversion identities is mistakenly mis-matched with ads.

 

Finally, building a marketing attribution solution requires an enormous amount of data, server-to-server integrations with third parties, the exchange of PII, as well as user-ad level impression data, which is now increasingly difficult to obtain from the walled gardens. The result is typically a lengthy deployment cycle, added costs with 3rd party identity data graphs, brittle solutions that are difficult to maintain (or change), and measurement without known accuracy.

 

 

Different Types of Multi-Touch Attribution

 

 

Path to purchase

 

 

Many Multi-Touch Attribution schemes are available in the market, from very simple unsophisticated approaches to extremely complex deployments requiring heavy resources and technology. Below is an overview of the most common approaches, from simplest to most complex:

 
 

Last-Touch Attribution

 

 

Last-Touch Attribution

 

Last-Touch Attribution is the simplest form of marketing attribution. It assigns full credit for the conversion to the last ad clicked. This is the default approach of free attribution tools within Google Analytics or other web analytics solutions.

 

The Pros of Last-Touch Attribution:

  1. Last-touch is simple and easy to understand
  2. It doesn’t require any PII
  3. It is easily measured for free and doesn’t require complex software

 

The Cons of Last-Touch Attribution:

  1. It almost always over-values lower funnel ads such as paid search
  2. It almost always under-values all other media, especially media with low-click rates such as video, display, non-brand search, as well as any media that doesn’t have direct engagement (e.g. clicks)
  3. It makes a poor assumption that only clicks drive purchases, and that other media without clicks does not
  4. It can lead to extremely poor marketing investment decisions that will harm the brand in the long run

 

 

First-Touch Attribution

 

 

First-Touch Attribution

 

First-Touch Attribution is also a simple form of marketing attribution. It assigns full credit for the conversion to the first ad clicked. This is a common attribution option in free attribution tools within Google Analytics or other web analytics solutions.

 

The Pros of First-Touch Attribution:

  1. It gives more credit to earlier funnel media, and avoids giving all of the credit to lower funnel media
  2. It doesn’t require any PII
  3. It is easily measured for free and doesn’t require complex software

 

The Cons of First-Touch Attribution:

  1. It almost always over-values upper funnel ads that have been seen within short windows of time (1-2 weeks before conversion)
  2. Cookie windows and cookie expiration dramatically shorten the measurement window (and potential effects) of longer-term awareness building ads
  3. It relies on clicks thereby ignoring the awareness building effects of ad impressions
  4. It can lead to extremely poor marketing investment decisions that will harm the brand in the long run

 

 

Fractional (or Rules-Based) Attribution

 

 

Fractional or rules-based attribution

 

Rules-Based Attribution attempts to assign weights, factors or “rules” to give credit to different points in the funnel. While it is noble in its attempt to overcome the weaknesses of more simplistic attribution approaches such as last-touch, the weights or rules can be overly simplistic, or even just flat-out incorrect, which creates more problems than it solves.

 

The Pros of Rules-Based Attribution:

  1. Rules-Based Attribution tries to address the weaknesses of Last-Touch Attribution by giving more credit to ads that don’t usually get credit
  2. It can lead to more investment in upper-funnel media
  3. It doesn’t require any PII
  4. It is easily measured for free and doesn’t require complex software

 

The Cons of Rules-Based Attribution:

  1. Most of the weights are arbitrary and are not rooted in any data or evidence
  2. The weights are too simplistic and assign the same credit for channels that likely have significant differences among specific campaigns or ads within them
  3. It is usually highly inaccurate and will lead to extremely poor marketing investment decisions that will harm the brand in the long run

 

 

“U” and “W” Shaped Attribution

 

 

"W" shaped attribution

 

These Attribution methods also attempt to assign weights, factors or “rules” to give credit to different points in the funnel- usually by assigning more credit to the first and last ads in the sequence. In the “W”, more credit is given to the “mid-funnel” with the assumption that it contributes more too.

 

The Pros of “U” and “W” Shaped Attribution:

  1. These approaches try to address the weaknesses of Last-Touch Attribution by giving more credit to ads that don’t usually get credit
  2. They can lead to more investment in upper-funnel media by assuming that the first ad “seen” introduces the brand to the consumer
  3. They don’t require any PII
  4. They are easily measured for free and don’t require complex software

 

The Cons of “U” and “W” Shaped Attribution:

  1. Most of the weights are arbitrary and are not rooted in any data or evidence
  2. The weights are too simplistic and assign the same credit for channels that likely have significant differences among specific campaigns or ads within them
  3. First-touch ads are usually cut off from consideration due to cookie expiration, especially as browsers, ad platforms and mobile operating systems have moved to reduce cookies and cookie lifetimes.
  4. They are usually highly inaccurate and will lead to extremely poor marketing investment decisions that will harm the brand in the long run
 
 

Algorithmic Attribution

 

 

Algorithmic attribution

 

Algorithmic Attribution is a more complex approach that sometimes uses predictive models and correlations to calculate credit across ad sequences. These computations are more sophisticated than simple rules-based methods and usually require an expensive 3rd party solution to provide marketing measurement. Also, these approaches frequently move past cookie-based tracking to more complex identity-based tracking schemes.

 

The Pros of Algorithmic Attribution:

  1. These approaches try to address the weaknesses of more simplistic rules-based methods
  2. A model or algorithm assigns credit as opposed to simplistic methods
  3. These methods attempt to compare ad sequences from customers who’ve seen the ads versus those that haven’t

 

The Cons of Algorithmic Attribution:

  1. They require the use of PII and the sharing of customer data with external vendors and systems
  2. They suffer accuracy problems with poor customer match rates and identity mis-matches
  3. They require expensive software
  4. They ignore traditional media (unless you buy an even more expensive Marketing Mix Modeling solution add-on)
  5. They ignore non-marketing factors that drive conversions like promotions & discounts, the economy, the effects of the pandemic, weather, competitive factors and many other elements
  6. They are suffering major new gaps due to Apple iOS 14.5, Google’s upcoming FLoC initiative and other consumer data privacy moves in the market
  7. They are not future-proof due to the wave of privacy regulations and other tech industry changes

 

 

Multi-Touch Attribution & Data Deprecation: Issues with MTA

 

Much has been written about the consumer data privacy changes being made in the industry today with Apple’s major change to user tracking in iOS 14.5 and Google’s upcoming FLoC initiative. Most of the industry discussion has been focused on the fact that these changes are making 1:1 ad targeting and personalization much more difficult, and these are truly significant disruptions to marketers who have relied on these techniques to boost ad performance. But lost in this conversation is that fact that these privacy changes also disrupt Multi-Touch Attribution, make it far less accurate, and limit the future viability of MTA solutions.

 

Consider Apple’s iOS 14.5 App Tracking Transparency (ATT) changes which opt-out all iOS device users from tracking by default. To-date only about 15-20% of users have opted into tracking, leaving a mammoth hole in about 80% of iOS campaign impressions, or 40-50% of the entire mobile advertising market in the US. If your MTA solution required the ability to track users for channels such as Facebook (or any other mobile-heavy ad network such as Snap, Twitter, TikTok, YouTube or Pinterest), your brand’s ability to measure campaigns in these channels is now experiencing significant data loss and as a result, major new accuracy problems.

 

Google will be next. In 2022, it will continue its rollout of FLoC- or Federated Learning of Cohorts- which will simultaneously kill cookies in Chrome and stop all individual ad targeting and tracking within the Google advertising ecosystem. FLoC will create another massive data hole in the digital advertising market and its effects won’t be limited to targeting and personalization. Instead, it will create another set of major problems for all MTA solutions.

 

Lastly, there is a significant wave of regulatory movement in the consumer data privacy space. Several US states have enacted privacy laws allowing consumers the right to be “forgotten”, the right not to be tracked, the right for their data not to be sold, and many other new restrictions and consequences for brands that do not comply. The implications for multi-touch attribution solutions are clear: the use of PII and consumer tracking will now carry significant new compliance risks for marketers.

 

 

Summarizing MTA

 

Multi-Touch Attribution Summary:

 

Pros Cons Future-Proof Prognosis

Good qualitative view of purchase paths and Consumer Experience (CX):

  • Where do prospective buyers drop off or get stuck?
  • What ads drive clicks?

Pro Tip: these CX and purchase path measures can probably be found in your web analytics platform and don’t require additional investments in new measurement software.

Multi-Touch Attribution doesn’t measure traditional ads:

  • Major traditional media campaigns that cannot be tracked usually have an impact on sales
  • Ignoring the effects of this media will grossly overstate the value of digital campaigns.

Multi-Touch Attribution models are inaccurate (and getting worse by the day):

  • Tracking data loss creates significant accuracy problems
  • Apple’s iOS 14.5 and 15 releases have created huge data gaps
  • Google FLoC (2022) will create even larger digital data gaps causing bigger headaches for MTA

Marketers Can’t Control Ad Sequences:

  • Even if a brand knew that ads seen during a certain sequence generated conversion lift, how will they ensure these ads are seen at a certain point in the path? The short answer: they can’t. It’s not possible.

Multi-Touch Attribution measures don’t account for non-marketing factors that drive sales, such as:

  • Sales, promotions and discounts
  • Seasonality
  • Economic conditions
  • Weather
  • Competitive factors
  • And many more elements… 

Poor

 
 

When to Use Multi-Touch Attribution

 

Given the issues around data loss, consumer tracking and privacy changes, and related model accuracy issues, what is the right role for Multi-Touch Attribution moving forward? Unfortunately, marketers who seek to understand a sequence of ads, and which sequence of ads that convert at higher rates, will be disappointed. It turns out that this is extremely difficult, complex, expensive and not all that accurate- and certainly getting more difficult and less accurate as time moves on. But for many marketers, understanding the path to purchase remains an important priority. While controlling the sequence of ads that your customers see is practically impossible, there are many qualitative reasons to examine the purchase path. Luckily,  every web analytics platform provides the ability to track and visualize your customers’ purchase paths. This allows brands to understand critical information that can be used to improve performance such as:

 

For these Consumer Experience (CX) measures, purchase path analysis is an essential tool and measurement approach. And luckily, this doesn’t require multi-touch attribution to do so.

 

 

 

How to Future-Proof Marketing Attribution?

 

Smart brands care about properly measuring the incremental contributions of their marketing investments regardless of whether these are digital or traditional channels, and smart brands want to understand the total impact of their marketing across all conversion points- not just digital ones. But knowing that marketing attribution has become more difficult, less accurate and now has new regulatory risks, how should brands think about future-proofing their measurement? Read OptiMine’s Future-Proofing Guide for a more detailed plan of action. To get started quickly, keep these simple but effective tips in mind:

 

 

 

4 Ways to Future-Proof Your Marketing Measurement Book Cover

 

 

 

Comparing OptiMine with MTA and Traditional MMM

 

What options are available to marketers in today’s measurement landscape, and where does OptiMine fit in? Consider your alternatives, specific needs and the options available in the market today:

 

Comparing OptiMine with MTA and MMM graphic

 

 

 

 


 

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