All Collections
Triple Pixel
Pixel Features
Attribution Models in Triple Pixel
Attribution Models in Triple Pixel

Attribution can be a complex topic. In this article, we outline the various ways of assigning credit to ads with the Triple Pixel.

Chaim Davies avatar
Written by Chaim Davies
Updated over a week ago

What are Attribution Models, and when should I use each one?

Attribution refers to giving credit to an ad or channel for helping lead to a customer's purchase.

There are different methods for assigning credit to ads for the role they played in causing a customer to make a purchase.

Because Triple Pixel tracks every touchpoint across your customers' entire customer journey, we can report on each of these events while giving you the tools to decide which ad click deserves credit for the ultimate purchase.

An attribution window is a defined period of time during which events are tracked and attributed to an ad.

For some businesses, giving credit to an ad clicked on a full month months before the purchase event would be unreasonable, whereas for brands with a much longer average customer journey (think furniture or expensive jewelry) one month may not be long enough.

Thankfully, Triple Whale lets you decide the attribution window that works best for your brand. We offer 1 Day, 7 Day, 14 Day, 28 Day, and Lifetime (i.e. the entire customer journey, regardless of the length), so you can determine the appropriate credit for each click in your customer journeys. The default set attribution window is 28 days.

Each attribution model works differently, so it's important to understand how they work and the ideal use case for each. Let's dive in!

Last Click

The last ad clicked is often seen as the “trigger” for converting an interested buyer into a customer.

Last Click Attribution gives full credit to the last ad click tracked in the customer's journey.

Example: If a customer clicked on Facebook ad #1, then Facebook ad #2, then a Google ad, and finally made their purchase, then the ad receiving credit would be the Google ad.

When to use Last Click:
The Last Click model should be used when trying to understand which channels or ads are clicked last, prior to the order being placed.

First Click

Oftentimes, First Click channels or ads are the drivers for creating awareness of your product or brand.

First Click Attribution gives full credit to the first ad click tracked in the customer's journey.

Example: If a customer clicked on Facebook ad #1, then Facebook ad #2, then a Google ad, and finally made their purchase, then the ad receiving credit would be Facebook ad #1.

When to use First Click:
The First Click model should be used when trying to understand which channels or ads are driving the initial interest in your product/brand, as well as understanding which channels are creating interest vs assisting in interest (linear all).

Total Impact

Total Impact utilizes First-Party Pixel Data and Post-Purchase Survey Data, combined with our proprietary algorithm created using Machine Learning and Artificial Intelligence.

In this model, your store’s revenue will be distributed to each channel, campaign, adset, and ad based on a weighted model that aims to attribute revenue according to which channels provide the most impact on the purchase decision. Total Impact is similar to an MMM, where aggregate data is used to determine which channels and campaigns are responsible for the most revenue to your store over a period of time.

We know that prospecting channels such as TikTok and Facebook are likely driving Google search results and direct website visits. Now, with Total Impact, we can use a multitude of data points to determine just how much impact those prospecting ads are having on your business.

By combining this data, the model can identify which channels and campaigns had the greatest impact on customer behavior and revenue.

When to use Total Impact:

Use Total Impact when you want to visualize the weighted success of your marketing sources, channels, campaigns, adsets and ads, with your store’s revenue distributed based on which channels provide the most impact on the purchase decision. Make real-time informed decisions with confidence.

The goal of this model is to continuously learn and adapt as new data becomes available. This means that we can provide increasingly accurate insights over time, helping businesses stay ahead of the curve and make data-driven decisions.

To learn more about the Total Impact model, how it works, and when to use it - Click Here.

Triple Attribution

Triple Attribution is an incredibly powerful model when you're running an omnichannel marketing strategy. This model assigns 100% order credit to the last ad click per ad channel in the customer's journey prior to purchase.

Many brands will see BOF or conversion channels like Google Branded Search taking credit for purchases whose customer journeys clearly started earlier than that Google Search event. Using Triple Attribution ensures that ad clicks earlier in the customer journey still receive the credit they deserve.

Example: If a customer clicked on Facebook ad #1, then Facebook ad #2, then a TikTok ad, then a Google Branded Search ad, and finally made their purchase, then Facebook ad #2 would receive full credit, AND the TikTok ad would receive full credit, AND the Google ad would receive full credit. In this example, using this model results in duplicate credit.

With Triple Attribution, the final click a user made within each ad platform is deemed the most significant for that channel, and credit is assigned thereby. Therefore, the credit is duplicated.

Note: when viewing your attribution data from the Ads >All Channel page, using either of the "Triple Attribution" models may result in the bottom-line revenue appearing higher than your Shopify sales data, due to the duplicated attribution. As such, we wouldn't want to use this model on the All Channels page for understanding our total attributed revenue, because the total revenue would be duplicated.

When to use Triple Attribution:

Using Triple Attribution allows you to focus on a particular channel at any given time, and give credit to the last ad click on a particular channel -- even if customers are subsequently clicking on ads on other channels as well.

This model is best for understanding the total value of click-based revenue attributed to every channel and making day-to-day decisions on your ads.

You can also use this model to review Order Overlap between channels to better understand how many customers are interacting with multiple channels prior to placing their order.

Triple Attribution + FB Views

Triple Attribution + FB (facebook) Views is a unique attribution model for use with Facebook ad data. With this model, we combine Triple Pixel attributed orders with Facebook's own reported View-Through conversions and conversion value provided by the Facebook API.

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 + FB Views model will layer that additional 0.5 ROAS and the corresponding increase in Conversion Value on top of our our own click-through ROAS and tracked CV.

As you may know, Facebook models both click-through as well as view-through data -- meaning, conversions that are derived from an ad click as well as conversions that are derived from an ad view (known on Facebook as 1-day views).

We cannot independently verify Facebook's claimed view-through data since no one but Facebook has visibility as to the content users are viewing (but not clicking) on Facebook. However, what we can do is layer Facebook’s view-through attribution data on top of our own Triple Pixel ad=click attribution. This way, you can see the impact of Facebook's reported view-through modeled data when added to Triple Pixel's own ad-click attribution.

When to use Triple Attribution + FB Views:

This model should be used when specifically analyzing Facebook Ads, to understand the potential lift in ROI when factoring in view-through traffic.

Linear (Paid Channels Only)

Linear (Paid) assigns equal conversion value credit split evenly across every source, channel, adset, and ad that Triple Whale tracked in the customer journey prior to purchase.

Example: If a customer clicked on Facebook ad #1, then Facebook ad #2, then a TikTok ad, and finally made a purchase, then all three ads would receive partial credit, which means that the credit is distributed evenly between them.

When to use Linear (Paid):

The Linear (Paid) model should be used when you want to analyze all of your marketing channels, sources, etc. on an equal playing field and see the partial credit each one deserves.

Linear (All)

Linear (All) is when the credit (conversion value and orders) are divided equally amongst any channels or sources that had a touchpoint (click) in the customer's journey to conversion -- regardless of whether the touchpoint was a paid channel or an organic source.

Example: If a customer clicks on Facebook ad #1, then Facebook ad #2, Triple Whale is only taking into account that they clicked from Facebook, so it is one order attributed to Facebook, regardless of how many campaigns they clicked on. However, if you're only looking at the Facebook channel AND using "Triple Attribution", the orders are not de-duplicated. So, if someone clicked on multiple campaigns, their order is counted one time for every campaign they clicked on.

When to use Linear (All):

Use Linear (All) when you are viewing your channels on the "All" page within the Triple Pixel. In this view, you will now be able to see the exact number of orders and revenue we tracked through the pixel. This is a useful model to use when analyzing your entire marketing mix on an even playing field.

When on the All page, the Linear (All) model will provide the true number of orders and conversion value we tracked. It will de-duplicate the Triple Attribution model that is standard.

Did this answer your question?