If you want to gain more knowledge about which channel can generate the most credit conversions, attribution modelling will help you with that. To offer you a quick overview, this tool consists of a set of rules that control how credit for sales and conversions is distributed across customer touchpoints. Moreover, attribution modelling will give you a better idea of how each interaction contributes to a conversion.

How Attribution Modelling Works:

Attribution models utilize statistical research at the user level to give value to marketing initiatives. This is in contrast to models that employ aggregate data, such as marketing mix modelling. In addition to that, attribution modelling is typically referred to as the bottom-up technique of assessing marketing effectiveness. On the other hand, businesses use marketing mix modelling to assess the performance of marketing initiatives such as television, radio, print advertisements, and point-of-sale promotions. Moreover, it avoids real-time analysis in favour of yearly, biennial, or quarterly analysis utilizing aggregated past data, unlike attribution modelling.

Because of this person-centric approach, attribution models are more commonly used in digital marketing than in offline platforms like print advertising. In the long run, this will benefit you by making the marketing world a little less complicated and more manageable.

Furthermore, attribution modelling ultimately focuses on online sales and advertising. In addition to that, the data used here is evaluated often in real-time.

The Different Types of Attribution Models:

When it comes to choosing the best attribution model for your business, it is imperative that you hold the data needed to fulfil your objectives. With that, now that we have briefly touched on the definition of attribution and how it works, let’s dive into the different models.

Last Interaction Attribution: This model is also known as “last-click” or “last-touch.” The approach provides 100 per cent credit to the last encounter your company acquired before a customer converted. Moreover, the last interaction attribution is the standard-setting on your Google Ads campaign. Therefore, if you have never changed the attribution settings of your campaign, then chances are this is what you are using. The last interaction attribution is also known as the easiest to implement and assess among all the models.

To give you an example, say you see an ad on Google for a pair of shoes, but you haven’t decided on making a purchase yet. After a couple of days, you remember about the ad and visit the website directly to view the product details. The next day, you see the ad on Facebook, and this is where you decide to make a purchase. In this case, 100 per cent of the credit will be given to Facebook.

First Interaction Attribution: As the name suggests, the first interaction attribution focuses on the first point of contact of the conversion process.

For instance, you receive a click for your paid search channel ad today. However, your customer did not push through with the purchasing process. You then retarget the advertisement, and this time, the customer decides to make a purchase. Rather than the retargeting ad receiving the entire 100 per cent of the credit, all the credit will go to the paid search channel advertisement.

Last Non-Direct Attribution: Meanwhile, this model excludes direct traffic and gives the last channel the client browsed through before converting 100 per cent of the conversion value. Moreover, the last non-direct attribution offers a clear distinction between direct and non-direct traffic.

Linear: Linear attribution tracks each touchpoint a customer interacts with on their way to making a purchase. Furthermore, this model will allocate weight to each touchpoint in regards to generating the conversion. Typically, the distribution is equally divided by the number of interactions a user has before converting. For example, your customer went through paid search, email newsletters, social media, and other direct channels. In this case, the credit will be distributed to all four channels, meaning 25 per cent each.

While this is a simple approach, it does not acknowledge the fact that some marketing techniques are more impactful and effective than others.

Time Decay: The time decay model also gives varying weights to each touchpoint along the route to purchase. However, the difference is that this approach provides the touchpoints that were used closer to the conversion more weight than those that were used earlier in the process. Therefore, if your campaigns are frequently long and complicated, the time decay model is ideal.

Position-based: This model also involves the distribution of weights. Specifically, the first and final encounters each receive 40 per cent credit, with the remaining 20 per cent credit spread evenly across the intermediary interactions. Because of the established percentages, this approach is also known as the “U-Shaped Attribution.” It generates a complete picture of the process as it understands that the first and final clicks are usually the most significant in the customer conversion journey.

Data-driven: Lastly, the data-drive approach is a relatively new model Google Ads has introduced. It employs Google’s machine learning technology to credit the conversion process’s most influential terms. Moreover, this modelling allows you to take advantage of machine learning. Specifically, it is based on account performance and intelligent learning of which terms were most significant in the conversion journey, with conversion credit assigned appropriately. This is great for high-volume spenders who are likely to click and convert at the same time.

Overall, these models are all functional and give useful data that you can utilize to enhance your conversion rate. You should select the strategy that best matches your business objectives.