An attribution model in marketing refers to the analysis and understanding of the touchpoints in a consumer journey during their path to a conversion. If a consumer buys an item on a website after clicking on a single display ad, we can be fairly certain to award that conversion entirely to that one ad.
What if the path to purchase isn’t as straightforward? Maybe a customer clicked on a display ad, then a Facebook ad a week later, before they visited the website from an organic search listing, and then converted in-store. How can we know which event, or “touchpoint,” was the key that sealed the deal? Marketing attribution is the answer to this question.
It is more important than ever to understand what steps in the purchase journey are more or less valuable to the final conversion. Marketing attribution modeling therefore allows us to apply and optimize budgets more effectively.
What is attribution?
Marketing attribution allows us to determine what steps were taken prior to conversion. It help identify which marketing channel had the biggest impact on the customer. Being able to accurately track and understand where conversions are coming from is extremely important to the success of any campaign.
As there are an increasing number of platforms and types of media available, attribution has never been more important. Unfortunately, due to the complex nature of attribution, it makes it harder to consistently track.
Understanding the path a consumer took that lead to a purchase can be just as important as the purchase itself. Attribution plays a significant role in helping companies understand their customers and maximize their business goals. Attribution gives invaluable insight into marketing strategies which enables better decision making and allocation of budgets. Without a high-quality marketing attribution model, campaign budgets can be wasted, resulting in lost revenue.
What is an attribution model and how is it tracked?
Marketing attribution helps companies visualize how much value is in each step of a customer journey. Companies who have not put thought into their attribution model often default into a simple last click attribution model. Other models derived from data can be used to ensure more weighting is given to other touchpoints. These will more accurately represent the influence a particular touchpoint had on the final conversion.
Marketing attribution models can be put under either single-touch or multi-touch categories. Some models are more commonly used than others and some are better at giving more clarity as to what steps are delivering the best return on ad spend (ROAS). This, in turn, alerts us to the fact that we need to allocate more budget to those top performing touchpoints.
Companies need to ensure that the appropriate measurement technologies are in place to effectively track the attribution capabilities of a campaign. This is where an attribution model comes into play. Attribution models assign credit to touchpoints in the consumer journey. The types of attribution models vary in the way they measure attribution. It’s a good idea to test and learn with a couple of them to see what works best for the tracking of your marketing campaigns.
If you are still unsure of what an attribution model is, here’s a short definition from Google:
Single Touch Attribution Models
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Last-click or Last-touch attribution
As mentioned before, “last-click” is generally the default option advertisers choose. This isn’t very surprising since it is very simple to understand and considered to be the “daddy” of attribution: the origin of the species, if you will. In a nutshell, this methodology focuses on the last page or touchpoint before conversion. It blindly gives all credit to that touchpoint. At Optily, we have a good analogy we use to explain this model: it’s like blaming your last drink on the fact that you are drunk!
The problem with this model is that it does not account for any other engagements in the user journey that may have led to the conversion.
Last-click can be appropriate in some cases. For instance, when a chunk of budget has been allocated to a specific concluding channel in the consumer journey. However, the path to purchase is getting more and more complex. Customers are interacting with many different touchpoints across online and offline channels before converting. Therefore, in many cases, a “last-click” attribution model simply doesn’t cut it.
In theory, advertisers should adopt an attribution model that uses data to analyze all customer journeys. This way, the data provides insights on the role each touchpoint plays in the overall path to purchase.
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First-click or First-Touch attribution
In contrast with last-click, first-click attribution focuses exclusively on the initial engagement that the customer took on their journey. It, therefore, ignores any subsequent engagements they may have had prior to converting. This type of attribution model would suit a campaign where awareness was the main objective. However, the irony is that the value of this awareness can actually only be understood once the campaign converts!
A major drawback of this model is that it ignores other potentially important interactions after the first interaction.
Multi Touch Attribution Models
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Linear or Equal attribution
This model is arguably the most straightforward multi-touch attribution model. It takes multiple touchpoints into account and credits them equally in the contribution they made towards a conversion. It does not take into account that different aspects of the consumer’s journey carry greater weight than others. While it is an improvement on single-touch models, it fails to account for the fact that different touchpoints may have had more or less of an impact on the customers decision to convert than others.
Linear attribution models open up the full customer journey and assign credit equally across paths to purchase. Meaning touchpoints that play a discovery, nurturing, or converting goal are rewarded.
Companies who apply this model to their campaigns acknowledge that their customers interact with multiple touchpoints in their path to purchase
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Time-decay attribution model
Time decay/delay is another multi-touch attribution model. It credits all touchpoints on a user journey, but weighs credit more heavily to touchpoints that occurred closer to the time of conversion than those further back in time.
There are benefits of using this model but it also comes with some drawbacks. There will be many customer journeys where the channels they visit closer to conversion will have been more impactful than those near the beginning. As user journeys get longer and more complex, this is increasingly not the case.
Time decay involves making the assumption that channels visited near the conversion deserve more credit than early ones. It has value for companies who are focusing on quick conversions.
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Position based or U-shaped attribution
U-shaped or position-based attribution is another useful multi-touch model. This model paints a clearer picture for us than the single-touch methods explored above. This model assigns the most credit to the first and final engagements. The rest is assigned equally to any of the touchpoints that occurred sometime in the middle. 40% of the credit is assigned to the first engagement and the last, with the remaining 20% distributed equally across the middle interactions.
The U-shaped model has relevance, but it can fail to accurately credit touchpoints in the middle of the journey which may have had a bigger impact on the conversion than the advertiser might expect. If you are selling a complex product that requires extensive consumer research, a video may be invaluable to enabling the consumer to research. Tt seems nonsensical to effectively downweigh this research in favor of a more recent display click.
U-shaped attribution can be well suited for longer customer journeys. The model works on the principle that the discovery step and converting step contribute the most to a sale. What’s good is that it doesn’t entirely discount the role the nurturing steps played in between. In a customer journey with ten or more steps, a linear attribution model would dilute the value of the first and last step significantly. Companies that want to avoid this can use a position-based model.
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Fractional attribution
This model assigns credit for the conversion to a variety of sources. By determining duplicate attributions, this model removes those touchpoints that have not actually contributed. It also allows you to assign greater weight to those steps taken that played a more important role.
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Algorithmic/data-driven or Customized by Channel attribution
Data-driven attribution is another useful multi-touch type model. It uses data across touchpoints to eliminate any assumptions. It attributes credit to channels according to their performance, rather than by what position they are in. This model utilizes machine learning to analyze every touchpoint and create an attribution model based on the resultant data.
In this day and age, it’s so important to make the most of the data collected from campaigns. It helps with analyzing customers behavior through their purchase journey. It provides insights as to what channels or touchpoints should take priority over one another, in terms of budget allocation. This in turn allows us to optimize and improve the performance of our campaigns.
The algorithmic attribution model gives us a full view of the customer journey. This allows us to analyze and optimize effectively. Where budgets allow, data-driven models should be the first choice for any performance-based campaign. While this may be quite expensive initially, the spend could easily be offset by higher return on investment (ROI) resulting from the opportunity to optimize for a true view of campaign performance.
Why is understanding digital marketing attribution models so crucial?
Seeing the impact that attribution can have on a campaign’s success, it is surprising how few marketers truly understand it. Roughly 70% of professionals across both Europe and North America struggle to act on marketing insights in order to generate improvements. This indicates that attribution is not being used effectively by a huge proportion of businesses.
Without a solid multi-channel attribution modeling strategy, it’s hard to consistently achieve ever-improving ROI results on marketing spend.
A clear understanding and utilization of an effective attribution model allows companies to see the impact their campaigns are having.
Understanding attribution allows you to control the consumer journey. You can then shape it in accordance with the needs of the company and the desired outcomes.
Implementing an impactful attribution strategy allows for greater efficiency by allocating resources to channels that are performing the best.
You’re not constricted to a single dataset with marketing attribution. You can use it to establish a baseline to predict future purchases, then measure the direct impact of altering a variable. Those changes that produce positive effects can then be incorporated into the evolving model.
Conclusion
Businesses at the top level are having to alter their approach and align their outlook more with that of their marketing teams. As mobile search and media consumption become ever more important and ubiquitous, consumer journeys can be increasingly disconnected and fragmented. It is understandable, then, that senior marketing leaders are increasingly devoting more initial budget to “data-driven” attribution.
The added insight from comprehensive, and often readily available, data is enough to make savings from better ROI in channels you can be sure deserve more budget allocation. With comprehensive data and the tools to unlock it, multi-channel marketing can be more accurate and agile. This makes it perfectly suited to today’s world of increasingly complex customer journeys.
About Optily
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