You may or may not be familiar with the idea of an attribution model. It is just one of many ways you can analyze data from your marketing efforts to make improvements going forward. Attribution models can be a very useful tool to provide insights about your digital campaigns, and pinpoint which of your digital efforts are working most effectively. For those who aren’t familiar, or are wondering if these models are something they should be using, we’re breaking down the basics of attribution models below.
What Attribution Models Are
Attribution models are an observational method used to, as the name suggests, attribute the sales or conversions your company is making to specific touchpoints on a consumer’s journey.
When someone purchases your product, it usually doesn’t just happen out of nowhere. They have taken a journey, be it short or long, that led them to your website to make a purchase. For instance, a consumer could see an advertisement for your product on Facebook, a paid ad on Google, organic social media posts, or some combination of all of the above before they convert. We would call each of these digital interactions with your products a “touchpoint”.
An attribution model, then, is a way to assign a value to each of these touchpoints, and determine which one (or ones) should be assigned credit for helping to drive the conversion. A well-designed model can help you answer the question “Which of my digital marketing efforts are leading to sales?” Answering this question is hugely helpful in determining where to focus your budget and efforts in the future.
The Different Types of Attribution Models
There are two major types of attribution models: rule-based and statistical model based. Different models will work better for different businesses; thus, when making use of attribution models, it is important that you choose the type that aligns best with your business.
In rule-based models, you can choose for yourself how the credit is assigned to different touchpoints. For instance, you can use a last click model, which means that 100% of the credit for a conversion will be given to the last touchpoint the consumer clicked on before buying. Or you can use an even credit model, so that every single touchpoint a consumer interacted with before making a purchase is given equal credit in driving that conversion.
Statistical models, instead of using a particular rule to assign credit, use historical data and an algorithm to assign fractional credit to each touchpoint along a consumer’s journey. The algorithm is always learning and taking in new information, using industry data and current trends in the field to determine how much credit each touchpoint should be earning. If a statistical model is gathering its data from a reliable source, you can count on it being a pretty accurate model. If you are unsure which rule to use for your model, choosing a statistical model instead is a smart choice.
Data These Models Can Provide
No matter which type of model you choose, all attribution models output a variety of very useful data for companies to analyze and learn from. Obviously, based on the rule-based or statistical model chosen, the output will include information about which touchpoints have been credited with the most conversions.
There is also more nuanced and fine-grained data these models can provide as well. Outputs from your attribution model can show you the average number of touchpoints consumers went through before making a purchase. Regardless of which type of model you used, you can also view the most common pathways through which your customers interacted with you. Even if some touchpoints did not receive credit based on your model, you can still see these touchpoints in the pathways people took to get to purchasing. For instance, even if you chose a last click model, you can see which previous touchpoints consumers also interacted with before that last click.
Cautions
While attribution models can be extremely useful and insightful, they are not the right choice for every business and every situation. There are a few things to keep in mind when it comes to attribution models.
First, since they use observational methods, that means they are not experimental, so you cannot determine a cause-and-effect relationship. The relationships you see based on attribution models are just correlations. You would need to run an experiment to see if the touchpoints highlighted by your attribution model are rally CAUSING additional sales.
Additionally, as we mentioned above, there are many different attribution models to choose from. Using the incorrect one for your business can lead to misleading results. Be careful when selecting which model to use, and critically analyze your results to ensure they make sense and seem to be including all relevant touchpoints. Overall, using an attribution model to help your business see which digital touchpoints seem to be driving conversions can be very helpful in shaping marketing decisions. When you are running multiple digital campaigns, or have multiple digital marketing efforts working in tandem, attribution models can help you separate them out and see which efforts are working best. We hope this article provides you with a bit of insight into the power of attribution models.
