A lot is made of attribution and rightfully so, but one must remember what attribution really is: a story of the participation of the different touch points along the customer journey. Now, as a marketer it is our job to understand when and what story tells us the truth about what really happened.
This seems like a super daunting task, but today, we're going to simplify things. First, we're going to briefly go over the most common attribution models and what they mean. Then, we'll briefly touch on why you may want to leverage different models for different clients.
Ok let’s jump in.
Attribution modeling is the process of determining how much credit to assign to different marketing channels or touchpoints in the customer journey for a particular sale or conversion. The goal of attribution modeling is to understand the effectiveness of different marketing efforts and allocate marketing resources appropriately.
Marketers use attribution modeling for many reasons. These include:
From acquisition to retention, marketers need to understand the impact of each digital marketing channel. Attribution modeling helps them do this by evaluating which channels are most effective in driving desired customer outcomes.
What's more, attribution modeling helps marketers to identify which channels are working and which are not. This allows them to allocate resources more effectively.
Attribution modeling can also be used to optimize digital ad spend. By analyzing the customer journey with different attribution models, marketers can determine which channels should receive the most budget in order to maximize ROI.
Also, attribution modeling helps to identify which channels are more likely to drive conversions, so marketers can adjust their ad spend accordingly.
Now that you know the purpose of attribution modeling, here are a few examples of how you can apply it to your eCommerce business.
If you have a fashion eCommerce store, for example, you can use attribution modeling to identify which channels are most effective in driving sales. By understanding the customer journey and attributing conversions to the right channels, you can ensure that your ad spend is allocated in the best way.
Additionally, if you have an eCommerce store with multiple products, you can use attribution modeling to identify which channels are most effective in driving conversions for each product. This allows you to customize your marketing efforts for each product, resulting in a more effective overall strategy.
There are several different methods for attribution modeling, each of which assigns credit to different touchpoints in the customer journey in a different way. Some common attribution models include:
First Click attribution is going to allocate all the credit of the purchase to the first touch point. So if you consumer had the following journey, Facebook would receive all the credit for the purchase. See the graphic below:
Now, when is First Click attribution useful. Well, a lot of times. Again, this will be contextual to the business and media buying strategy. For example, do you think the first touch point is more important than the last? If that is the case, then this would be the attribution model for you.
A use case for first click attribution is understand how efficacious your TOF efforts are. If they invited the person to the party, they should get the credit :)
Last Click is going to be the inverse of First click; all the credit will be given to the last touch point. So in the same example above of the user journey of Facebook to Google to Purchase Google would receive the credit.
Again, I find it useful to think of it in terms of “causal” intensity. What do you value more? The person who brought the person to the store? Or the sales person that closed said person?
Last Click is a great way to measure your channel health. However, be careful with it because it can be misleading sometimes.
For example, if customer journey below, the FB ads seem to be doing the majority of the lifting and the Google ad closed.
This isn’t good/bad per se, I just want you to be cognizant of what if that was a branded search ad? Would you really want your branded search taking all the credit for the previous 3 touch points?
Ok, maybe you aren’t an absolutist and you don’t think one touch point deserves 100% of the credit. I can dig it and this is where attribution models become muy interesante. Fractional attribution, the next model we will explore.
Fractional attribution is will credit every touch point in the user journey and spread credit across all the touch points from first to conversion. However, there a few types of multi-touch attribution models. The one we offer in Triple Whale is Linear, but we can briefly touch on all of them.
This model gives all the touch points on the customer journey the exact same credit for the purchase. All the touch points are assigned the same weights.
What have you done for me lately ;) This model gives more credit (weight) to touch points that happened closer to the conversion.
This is kind of like how you remember things. You remember the beginning and the end, but very little in the middle. This model gives the majority of the credit to the first and last touch points, while recognizing the touch points in between.
Same same, but different as the U-Shaped model. The W-Shaped will give you three milestone touch points, and then credit the in between touch points evenly with the remaining credit.
Triple Attribution gives credit to the last ad the customer clicked through on the particular platform (Facebook, Google, etc) currently being viewed. (This model is sometimes referred to as “Last Platform Click”.) In this model, each marketing channel’s final click-through for the particular customer receives credit for the purchase.
Example: If a customer clicked on Facebook ad #1, then Facebook ad #2, then a TikTok ad, and finally made their purchase, then Facebook ad #2 would receive credit when viewing the Facebook data, AND the TikTok ad would receive credit when viewing the TikTok data. In this model, the final click a user made within each ad platform is deemed the most significant, and credit is assigned thereby.
Triple Attribution + Views is a unique attribution model for use with Facebook ad data. Facebook tracks both click-through as well as view-through data.
We cannot independently verify Facebook’s claimed view-through data, since we are only tracking click-through events from your ads. However, what we can do is layer Facebook’s view-through attribution data on top of our Triple Pixel click-through attribution.
This way, you can see the impact of Facebook’s claimed view-through attribution when added to Triple Pixel’s own click-through attribution.
Example: If the Triple Pixel ROAS was 2.35, and Facebook’s view-through attribution claimed an additional 0.5 ROAS, then toggling to the Triple Attribution + Views model will add that additional 0.5 ROAS to our own click-through ROAS.
Google Analytics 4 Properties offers three distinct models for attribution in the Attribution Reports: cross-channel rules-based, Ads-preferred rules-based, and data-driven. Each model provides powerful insights into how conversions are generated across channels, allowing you to measure your marketing performance accurately.
This model gives 100% attribution of the conversion to the last Google Ads click, with secondary attribution to other channels in the user journey.
This model gives full credit for conversions according to the rules you set. From last-click attribution to first-click attribution, you can assign weights to different channels as desired.
This model uses machine learning algorithms supported by historical data to accurately attribute conversions to each channel. It requires large sample sizes and is the most accurate of all three models.
To find these reports, log into Google Analytics and select the “Attribution” tab. From there, you can view all of your attribution reports in one place.
With these reports, you can gain insights into how each marketing channel contributes to your overall conversions and make more informed decisions about future campaigns.
There is no right or wrong attribution model. The best attribution model is the one that helps you make the best decisions. I like to think about attribution as a story telling mechanism. What is the story that is closes to the truth?
That is the attribution model you want to use.
With that being said, you might want to compare the attribution models to ensure you are “listening” to most truthful story.
To see the comparison, just drill down into Pixel screen and click on the rubik’s cube. You can now see all the attribution models compared. What a time to be alive :)
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