Marketers have the task of making a product or service visible to the customer, generating a lead and then ensuring that it ends up in a sale. This sounds straightforward. For a very long time, marketers would use a specific channel or campaign and then wait to see how effective it was through the number of conversions. Marketing has since grown and evolved. Now marketers use multiple channels and undertake various campaigns to generate leads and make sales. The questions are, which of these touch-points gets credited for the conversions and how is this conclusion arrived at? Such questions have led to attribution modeling.
What are Attribution Models?
This type of modeling is used when marketers want to assign credit for conversions to specific touch-points. Attribution models, therefore, give credit to various touch-points using some rules. Since a customer goes through various channels and campaigns, what convinces them to make the purchase? For example, a person visits your website from a social media advertisement today, returns through an email campaign and then finally comes a week later from direct search to buy the product. Which of these touch-points will you credit the purchase to?
Arbitrary Attribution Models
The answer isn’t as simple as some people make it out to be. Arbitrary attribution models are often single point models. These assign the credit to one touch-point, either the last or the first in a customer’s journey. The problem with this is that it automatically assumes every other channel did not play a role in convincing the customer to buy the product. Similarly, fractional models can also be considered to be arbitrary. The marketer’s intuition plays a big role in the determination of credit allocation. If, for example, you’re of the opinion that AdWord campaigns are twice as important as email campaigns, your credit allocation will always be biased towards AdWord campaigns. This is subjective.
Arbitrary models are therefore user-driven. They are easy to use because they oversimplify the situation. In the end, the results are not entirely reflective of the real life situation.
Data-Driven Attribution Models
On the other hand, data-driven attribution models are objective. They depend on data to come up with conversion attribution for every channel. They take every channel and campaign into consideration before they can assign any credit to any one of them. Algorithmic models are some of the best multi-touch data-driven attribution models. They take all the touch-points into consideration and even go a step further to analyze interaction since touch-points don’t work in isolation. These attribution models use statistics and machine learning. If you have tag and pixel data or server logs, then there is no limit on how you can use this data to track the effectiveness of every channel or campaign. The data is constantly updated to provide real time results.
Some marketers don’t like these models because they’re complex. Unlike arbitrary models which are informed by the marketer’s intuition, it’s data that informs the credit for every conversion to the various touch-points. At the end of the process, the results from the models aren’t biased.
Conversion attribution modeling in online advertising helps to measure return on interest for every touch-point. This is extremely beneficial because it’ll help you to optimize every touch-point accordingly. It determines whether or not you scrape off a campaign, reallocate money towards certain channels and reduce the resources allocated to some. You’re also able to present these results to the business owners or justify such moves to those who approve such budgets.
The ability to measure return on interest and have actionable results through this information is the measure of an attribution model’s effectiveness.
Attribution modeling in every channel or campaign, including online advertising, helps to measure the effectiveness of the touch-point in conversions. An effective attribution model is one that is also actionable. You should be able to make changes to your marketing strategy in order to optimize every touch point with the information that you get from the attribution model.