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<aside> š£ How do you measure your efficacy?
In a world that has become even more patently digital over the past couple of years, itās crucial to have a comprehensive understanding of your customersā journey. You need to know who they are, what they want, and what motivates their affinity for your product. Attribution modeling is a way of assigning credit to different touch points along that journey. Making sense of it is critical for optimizing marketing efforts and delivering the one-of-a-kind experiences thatāll make you standout.
Recently, weāve observed that the easy legibility of single-touch has become popular with those looking to glean a quick and dirty picture of what works. And while sometimes what you want may be quick and dirty (which in marketing just means probably not as comprehensive), the difference is one that can impact how you evaluate the efficacy of your campaigns and how you plan future spends.
Thatās not to say single-touch is bad ā itās actually quite good for figuring certain things out. (Weāll get into which!) The reality is that multi-touch attribution is your best bet for surveying a landscape being deeply transformed by technologies evolving as we speak.
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What is single-touch attribution?
Single-touch attribution assigns credit for a conversion to a singular touchpoint the customer interacted with. For instance, if a customer clicked on a Facebook ad and then made a purchase, the credit for that sale would go to Facebook alone. The appeal of single-touch attribution is obviously its simplicity. Itās both easy to understand and implement.
The problem is that single-touch can also lead to undervaluing the importance of other touch points and factors in the customer journey. Consider this, one of your customers might see an Instagram ad, then visit the brandās website and sign up for a newsletter, and finally make a purchase after receiving an email. Single-touch would give all the credit to one of those steps, ignoring the others at the cost of your grasp on the full picture. You can see how replicated across instances, single-touch can really add up to fundamentally misunderstanding your campaign efficacy and audience interest.
Beneath the larger single-touch umbrella, there are two main subcategories. The easy enough to grasp, first-touch and last-touch. The former attributes conversions to the first interaction a consumer had, while the latter measures conversions at the last touch point. When your business is running a direct response campaign thatās focused on getting customers to take a specific action, single-touch is the best way to glean which channels would be the most effective at driving conversions.
Itās important to note that the efficacy of single-touch attribution can differ depending on the type of channel being measured. For example, Google is a demand capture channel. Someone is typing in a keyword and weāre capturing that demand. Those consumers are likely very deep in the funnel and have a high likelihood of converting right away. Last-click modeling will always show favorable results for these channels.
Facebook, on the other hand, is a demand creation channel. Weāre interrupting the feed to move the user āin-marketā and consider buying. Once they arrive on site theyāre likely somewhere in the middle of the funnel. Last-click modeling will typically underrepresent the results for these channels since there is likely another touchpoint needed before converting.
Recently, a client weāre familiar with was trying to evaluate the last-click performance of Facebook ads that severely underrepresented the total value being created. They were only considering the last-click revenue from Facebook divided by total spend on the channel, resulting in a negative ROI.
One way to combat this line of thinking with brands is to run a holdout test, essentially stopping spend on a channel and measuring impact on overall revenue to prove out that the total value generated from the channel is much higher than what GA reports on a last-click basis. Consider this mostly with paid social and more upper funnel channels.
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<aside> š§© What is multi-touch attribution?
On the other hand, multi-touch attribution assigns credit for a conversion to multiple touch points in the customer journey. Thisāll give you a more holistic view of all the steps that led to a purchase, providing insight into which diverse factors are most important to driving conversions. Back to our Facebook example, if a customer sees an ad there, visits your site three times, sees an IG ad in the meantime, and receives an email all before making a purchase, multi-touch will give you insights into all those stages.
The benefit of multi-touch is its comprehensiveness and the way it assigns credit to a range of relevant inputs. This adds up to a more holistic view of your customer journey which is ultimately the best way to really see through the eyes of your consumer. Multi-touch isnāt its own model though. On Google for instance, there isnāt a multi-touch option you can simply toggle. You need to choose your desired inputs and thatās when you begin to see the way your options start to branch and fork which is what makes multi-touch more complicated.
There are two main multi-touch types. Thereās rule-based multi-touch and thereās algorithmic attribution. Rule-based simply means that credit is assigned based on predetermined rules. Those rules can take one of three main shapes: thereās linear, time decay, and position-based attributions.
This approach while full of pros, has one distinct con: itās difficult to measure the impact of each step. Because credit is being spread out that also means that depending on where you look, two sources may be taking credit for the same purchase. If I see an ad on IG, and then later go search and click on a Google ad, both are going to take credit. That can be deceiving.
And last but not least is algorithmic multi-touch attribution. Algorithmic is the most sophisticated of the varieties because it assigns credit for conversions across multiple touch points based on machine learning and statistical modeling. Pairing the increased intelligence with a denser web of information delivers you the most complete picture of how your customer found your product, how the channel youāre leveraging is performing, and what you might want to consider in order to optimize your results.
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<aside> āļø What should you do?
Since most companies within most industries (except for maybe those with a very short path to purchase) understand the value of a holistic understanding of the customer journey, multi-touch algorithmic will take its place as the most popular option. And with marketing channels and technology becoming more reliant than ever on machine learning, it seems like more accurate algorithmic attribution is becoming easier to implement where in the past it's been much more difficult and required a significant amount of analysis and investment.
Over time, we believe that most everything will shift to algorithmic attribution. For internal platforms, youāre already seeing this with data driven attribution from Google. From a third-party attribution perspective (like GA 4, Triple Whale, Northbeam) as things like Safari's ITP (Intelligent Tracking Protection) continue to make click/UTM-based user identity resolution difficult for stitching together multiple sessions, these platforms will have to start using machine learning to model the user journey hence making rule-based multi-touch and single-touch (really anything reliant on UTMs) more difficult.
While both single-touch and multi-touch attribution have their pluses and minuses, multi-touch algorithmic modeling makes it easier to see through the eyes of your customer. At Part and Sum, we recommend algorithmic attribution as the best way to optimize your marketing efforts in a landscape of evolving technology in order to create customer experiences that are memorable and drive growth.
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