Why solving the attribution problem is the holy grail of marketing…

“Attribution modelling describes various methods marketers use to properly break up and assign conversion credits to various different channels in case users take multiple website visits, via multiple channels, to arrive at the ultimate conversion behaviour.” Sourced.

Thanks, Webster’s. 

In the ’50s and ’60s, creative decisions in advertising were made with a small set of measures to guide decisions. Ad campaigns were chosen based upon the potential number of eyes or ears that might see or hear an ad, so measurements were determined by things like viewer ratings or total newspaper subscriptions. Many campaigns came down to creative directors making gut-based decisions on what they assumed the target customer wanted/was looking for. Everything was put in print, billboards, or on TV or radio.

How did you know if a campaign was working? 

Generally, you noted if your sales increased and people said nice things about you. However, concrete results were elusive. 

But, although those days of 10 am whiskey-fuelled, tobacco hotboxed boardroom meetings are long gone, a lingering problem remains- attribution. Tools and tracking methods like cookies, URL tags and website analytics allow marketers to track a potential customer from discovery to sale, but even still, and despite these methods allowing marketers to create, track and map with near surgical precision, they cannot account for the full customer journey. 

The attribution problem is often referred to as the central problem in impact evaluation. The central question is to what extent changes in outcomes of interest can be attributed to a particular intervention. Attribution refers to both isolating and estimating accurately the particular contribution of an intervention and ensuring that causality runs from the intervention to the outcome. 


Ideal attribution…what does it look like?

Marketers drill down into insights from attribution tools in multiple ways. Some marketers work with web analytics platforms, some establish their own internal attribution models and some choose to focus on a handful of key metrics. 

That’s impossible to do, of course, unless marketers can consistently track the customer throughout their entire purchase journey, from building brand awareness all the way down to the literal conversion point, Marketers would also need to know what considerations customers take into account along the way and which of those considerations ultimately led to a conversion. 

Offline attribution. 

Say for instance, on one balmy summer’s day, perhaps even feeling the effects of one too many glasses of that golden Chablis your housemate kept pouring you the night prior, you decide that you are in need of a soft drink. You walk into the convenience store to sort that sorry situation of a mouth out, and you opt for a classic cure: a crisp, syrupy Coca-Cola with the heavenly viscosity of hot road tar- and you crack that sucker before getting to the counter (because by god you need it) and walk out the door a saved human.  

For the consumer, the journey ends here. But, for the marketer, this is where the trouble begins…what lead this hypothetical person to make that decision?

Some liken the customer path to something akin to a random walk – each subsequent user interaction occurs independently of the previous one. “There are so many conscious and subconscious, trackable and non-trackable, significant and insignificant factors that may influence user behaviour, that it is futile to expect a well-trodden and visible path to purchase. “

This inherent causal ambiguity was epitomised by the ubiquitous methods of advertising’s golden age, in which the engineering of a businesses ‘brand identity’, or recognition was a keystone attribution model- people know Coca-Cola and know what to expect- so that’s the purchase they made. 

The Sao Paulo Experiment

In 2006, the Mayor of Brazil’s biggest city, and South America’s largest commercial market did something radical: he put in effect, a blanket ban on outdoor advertising. In spite of a whole host of legal issues and powerful lobbying against the move, he announced that nearly without exception, outdoor advertising would have to be removed within months, stating: “We decided that we should start combating pollution with the most conspicuous sector – visual pollution.” 

In the face of these changes, marketing and advertising creatives were forced quickly to find new ways to spend money that had been earmarked for outdoor advertising, especially since the law came into effect almost immediately. This dramatic shift in Brazil’s regulations is thought of as a catalyst ingredient, ushering the country’s robust consumer market into the digital age- as, in the face of the ban, this was seen as the most economically viable option. 

We could argue the pros and cons of this move, but why is all of this relevant? 

Well, it is important to note that although methods such as billboard and bus bench advertising lack the ability to track, they clearly do have a profound effect on the purchaser. The Sao Paulo experiment is, if nothing else, an example of the silent power of brand awareness. 

New World, New Metrics. 

Marketers use a variety of metrics to measure the success of their campaigns, and those metrics are often the fundamental building blocks of their attribution models.

Sales metrics such as conversion rates and return on ad spend (ROAS) are generally considered to be the most effective attribution methods when evaluating a campaign’s success.

Other tracking metrics such as unique visitors, click-through rates and average time spent on a page are considered secondary tracking methods. 

It’s not hard to see why decent tracking metrics can be appealing: Brands and agencies want to know that their ads are actually being viewed. But marketers should be wary of relying on those metrics too heavily since they don’t necessarily indicate consumer purchases or even engagement with the brand. This can lead to over-inflated ROI statistics, and marketers should be aware of an inherent bias towards touting the efficacy of such tracking techniques. 

So, What do marketers see?

We all know what a purchase experience looks/feels like to the customer. But what does that same journey look like to the marketer? Here are a few examples of what touchpoint mapping looks like, ranging from simple to more complex. 

Simple touchpoint i.e.

To slightly more complex, i.e.

To far more convoluted models, i.e

Even if one could track and optimise the user journey for each step in a buyer journey such as this one (as some customer experience platforms are trying to achieve), it’s worth questioning how reliable and reusable this data is. Can marketers really expect future users to navigate a similar path? What is it about this user that required over 50 times more interaction than the other? Is the future of attribution a complex, AI-driven series of “if/then”s that adapt to each step the user takes? Or is it a return to more simple, one-size-fits-all, approaches that satisfy most needs, most of the time?

It is important to note that the above paths are pictorial representations of what touchpoints look like along a purchasing path. It’s also an important note that it isn’t so much the particular touchpoints that matter, rather how influential each touchpoint was on encouraging the user to move further along the purchase funnel. These examples, if nothing else, show just how wide-ranging scale, from simple – to staggeringly complex, these paths can get, which further complicates the mission to faithfully & correctly attribute models, campaigns and behaviour. 

Common attribution models.

It’s important to remember attribution models, broadly speaking, are lenses through which we can attempt to better understand user behaviour, and depending on the market, your product, the type of advertisement and the platform, you’re going to get different results. Let’s take a look at some of the most commonly used marketing attribution models: 

Last-click attribution. 

All the credit goes to the customer’s last touchpoint before converting. This doesn’t take into consideration any other engagements the user may with the company’s marketing efforts leading up to that last engagement.

Pros: Regarded by many as the most accurate attribution model. 

Cons: Weighted heavily towards the final click. Who’s to say other touchpoints weren’t just as influential?

First-click attribution. 

The other one-touch model, first-click attribution, gives 100 per cent of the credit to the first action the customer took on their conversion journey. 

Pros: Very useful in discovering which channels are more effective at allowing customers to find you. 

Cons: Opposite to last-click: & pays no mind to the other potential avenues customers may have gone through to reach a purchase decision and assumes the first click is the most crucial. 

Linear attribution. 

This multi-touch attribution model gives equal credit to each touchpoint along the user’s path.

Pros: Equal distribution of credit to every touchpoint along the user’s journey. 

Cons: May not fairly attribute credit to more/less influential touchpoints along the purchasing path.  

Time decay attribution. 

The closer in time to the event, the more credit a touchpoint receives.

Pros: De-values older campaigns in favour of newer ones. 

Cons: Better suited to long-campaigns. 

U-shaped attribution. 

The first and last engagement get the most credit and the rest is assigned equally to the touchpoints that occurred in between. In Google Analytics, the first and last engagements are each given 40 per cent of the credit and the other 20 per cent is distributed equally across the middle interactions.

See below.

Source: Google analytics. 

The bottom line…

As digital marketers working with small to medium-sized accounts, we usually operate within a closed system of Google Analytics attribution models. And it is of heavy importance to admit, both to ourselves and to clients, that these models simply cannot account for existing brand awareness and other offline factors. We can and should always be aware of these factors, but also attempt to understand them as best we can- and the simple fact is that users and customers follow a near-infinite variety of unique paths to purchase. 

So, what do we as marketers stand to gain from our attempts to track every step of a customers journey?

The answer is understanding

The greater the understanding of the buyer’s process, the better we can attempt to game it and in doing so, win more market share and consumer trust.

But, will we ever reach the singularity of complete attribution? Probably not any time soon, folks, but a marketer can dream. 

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