If you were to list all the attribution models that have been coined in marketing you could get to double digits. But many of these, if you are being kind, are ‘edge cases’. Really, there are 6 established predictive marketing attribution models and the rest are best described as ‘custom’ models (I’m looking at you J and W models 😀).
Before we get into the pros and cons of each of these 6 models, it’s worth briefly recapping why marketing professionals might even choose a marketing attribution model. Marketing attribution helps you understand which content is driving conversions and therefore driving revenue. But, in order to escape the Google Ads and Analytics myopia in marketing measurement and move towards a multi-channel (including offline and dark social touchpoints), you need a framework that accounts for a realistic conversion path. Once chosen, this framework can be a ‘mask’ to weight which channels are credited with a purchase.
The 6 recognised attribution models are: first touch, last touch, linear (even-weighted), time decay, U-shaped (position-based) and algorithmic. Each model has its advantages and disadvantages.
To illustrate how the various attribution models work, we will reference Sam the Marketing Director’s customer journey with the following touchpoints. Sam:
- Reads about your service in a trade publication
- A few weeks later they notice a LinkedIn Ad from your brand for the product they read about – they sign up for the webinar
- A week after that Sam downloads an asset from your site which they found from organic search
- One week later Sam uses a ‘refer a friend’ voucher to sign up for your product
How could this reality be accounted for with the 6 main attribution models?
#1. First touch
First touch attribution recognises the first interaction (the point of first impact where leads come into contact with your business) in a prospect’s journey. In the above example, Sam’s conversion would mainly be attributed to the media coverage from the PR team (if you can quantitatively measure that, more on that later). But then, Google Ads gives all credit for the conversion to the first-clicked ad and corresponding keyword.
While a first touch attribution model may be the right choice for businesses focused on top-of-funnel efforts, it can be overly simplistic and miss key milestones in the buyer
journey. B2B SaaS organisations often have long sales cycles, so a prospect’s first interaction with the company may not be representative of what led them to convert. For example, if Sam read the coverage a year before converting, that’s not indicative of what led to the conversion.
To account for lengthy sales cycles, you can implement a lookback window of your choice in Google Ads and Analytics (usually 90 days). This lookback window means that when a prospect converts, the credit will only be given to the first touch within the past 90 days, accounting for relevance and recency.
to the first click in a conversion path
#2. Last touch
Using the same example above, Sam’s conversion would be attributed to the refer-a-friend programme. It gives the conversion credit entirely to the final touchpoint from which a lead has converted. This is usually the standard attribution option in Google Analytics or most analytics tools. Google Ads Analytics has Last Adwords models as a standard and Meta is the same. Google Ads gives all credit for the conversion to the last-clicked ad and corresponding keyword.
Last touch works well for short buying cycles, where brands are confident that their potential customers are not going through many touchpoints before converting. If the last channel before the purchase is the one that a business wants to prioritise, the last touch attribution model can help with that. It shifts focus from the early discovery phase to conversion.
This is very different from the first touch model where the conversion is 100% attributed to the first touchpoint the contact interacts with. In first touch, when a brand has a complex customer journey, if the purchase doesn’t happen within the window pretty much dictated by the cookie, all the great marketing data the brand has gathered becomes redundant.
And so for last touch, having a short window to analyse between last touchpoint and conversion means that this method is less likely to bring errors in the process.
This is important considering Apple’s latest intelligent tracking prevention measures – where the cookie expiration period has been dramatically reduced. This can have detrimental effects on campaign performance and overall marketing data quality.
#3. Linear (Even)
The linear model attributes credit equally to all of the touchpoints that led to a lead converting. Everything from first touch, lead creation, opportunity creation, and customer closing are all treated equally. The main problem with marketing attribution is determining which touchpoints are most important in a customer conversion journey. Linear attribution has a simple answer to this; give all of the touchpoints the same level of importance.
LinkedIn offers a version of this via its ‘each campaign’ conversion attribution model. Google Ads distributes the credit for the conversion equally across all ad interactions on the path. In our example, all the four touch points would get 25% of the credit for the conversion.
Since even distribution includes a variety of touchpoints, it is more representative of the broader buyer journey. The linear model is a huge improvement from the first touch and last touch models. It gives marketers a more complete overview of everything that occurred from the beginning of the funnel to the end stage where the lead converts.
This model is easy to set up and can be used to compare results from other data models. And you don’t have to worry about which touchpoints should receive credit for a conversion.
However, it has its limitations because not every touchpoint should always be weighted equally.
#4. Time Decay
The time decay attribution model gives more significance to the touchpoints that are closer to where the conversion occurred than to the top of the funnel. It’s a multi-touch model that’s similar to the linear attribution model. It gives more credit to the middle and bottom of the funnel and represents them as being worth more because they’re the points that drove the lead to convert.
This method isn’t foolproof, though. Perhaps a lead interacted with your ad and signed up for your demo, and then later purchased your product through a link in a blog post. Should the blog content receive more credit than the ad or the demo? Most likely not.
The touchpoints closest to the point of conversion have the most weight. This model helps you optimise those points that lead to conversions directly. However, devaluing the first touch might not always be the right thing to do. Depending on the circumstance, the first touchpoint may have played an important role in the conversion.
A time decay-based attribution model places more weight on activities done closer to the point of conversion. In the above example, the webinar would receive the smallest weight and the event would receive the greatest weight. This is governed by a half-life for contributing touchpoints measured from the time of conversion, e.g. the Google Ads version uses a 7-day half-life. In other words, an ad interaction 8 days before a conversion gets half as much credit as an ad interaction 1 day before a conversion. In our example with Sam, the exact weighting is dependent on the half-life defined by the marketing team and when the touch points happened.
This model helps account for lengthy sales cycles by giving a larger percentage of credit to more recent, and therefore, often more relevant, interactions. Larger businesses tend to start with a first and last touch model, with weight split between the first and last touch. This works for teams for a while, but with long sales cycles, they realised they wanted a better way to represent the interactions that occurred between the first and last touches.
That’s when teams usually decide to shift to a time decay-based model.
#5. U-Shaped (position-based)
The U-Shaped model gives the majority of the credit to the first and last touch and then even credit to the touchpoints in between – in our example 40% of credit to both the first (article in the press) and last ad interactions (recommend a friend voucher), with the remaining 20% spread out across the other ad interactions on the path.
Unlike the first and last touch attribution models that place importance on just one aspect of the analysis, the U-shaped model gives equal importance to both values. However, there are times when the first or last touchpoint isn’t as important. When doing an analysis, you should always check if the first touch is as important as the last point.
This model isn’t suitable for long sales cycles or campaigns that have to nurture leads. It’s more for when a lead engages with your content and decides almost immediately that they want to make use of your service or product. These models are much more complex to set up and sometimes overcomplicate attribution. If you have a marketing funnel with many interaction points however it might be the best first attempt to model attribution.
#6. Algorithmic (data-driven)
The algorithmic attribution model is, according to the vendors who calculate the algorithm, the most accurate way to measure a user’s journey from prospect to conversion. But, the exact maths on how the attribution is calculated is a ‘black box’ – not publicly disclosed or reviewed by peers / third parties. In our example with Sam, we don’t really know what to expect – the weighting will change from measurement platform to measurement platform, and is largely impenetrable to those not in the engineering teams of those vendors.
The most popular example is Google Ads’ ‘data-driven attribution’ – which imposes a weighting on Google touchpoints (keywords, ads and campaigns (Search and Display Networks), website, shop visit and Google Analytics conversions from Search (including Shopping) and YouTube.
The success rate in this model is higher than others because it is uniquely created for each business. However, this process is complex and involves calculations, so it may require the skills of a data analyst and more advanced or powerful tools. These tools might not be available to smaller businesses because of their price points.
Which Is The Right Marketing Attribution Model For My Business?
So how do you answer the key question – which is the right marketing attribution model for my business?
In a nutshell, the answer is of course ‘money’ or revenue. Or in business terms ‘growth’. But even though marketing attribution is the key to unlocking this growth, it’s important to remain grounded and pragmatic about the data. For example, marketing attribution was built for measuring captured demand not demand itself. Ad platform attribution models and CRMs can’t attribute conversion credit to ‘dark social’ e.g. Slacking a LinkedIn post to your boss or hearing about a product on a podcast.
However, it’s good to remember that attribution models are simplified illustrations of complex customer journeys at best. That’s why instead of trying to build a perfect attribution model, your goal should be to build a useful revenue model and test it for your business and your audience – which is unique to your business at any given time. It’s also good to understand that you don’t need to be a large organisation to complete this revenue modelling work. There’s an emerging group of vendors and agencies specialising in this work, which can mean the board-ready report can be completed in a matter of weeks.
What’s guaranteed is that as soon as you move past the default ‘last-click’ model, a well-designed attribution model will help you understand the revenue contribution of each individual marketing campaign, message, and tactic. Ultimately, you can use this data to optimise your marketing spend and direct more investments into the messaging, channels, and tactics that are working well. On the other hand, marketing attribution will also help you stop wasting money on tactics that are not worth the time and money you’re putting in.
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