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Attribution Models for Marketers: The Definitive Guide

We would like to think that a visitor finds your blog or clicks an ad, and immediately converts into a sale or lead.

Unfortunately, that is rarely the case.

Today, people will visit your site several times prior to converting. They'll find your blog post, return directly a week later, and click a retargeting ad the next day. Then, they will finally convert!

So the question is: which marketing channel gets the credit? Was your blog responsible for the sale? Or was it your Facebook Ad?

When trying to show clients the value of each marketing channel, it can be difficult. With multiple touchpoints in the customer journey, each channel plays its part.

These are the questions at the heart of marketing attribution models.

What are marketing attribution models?

Attribution modeling is a framework for analyzing which touchpoints, or marketing channels, receive credit for a conversion. Each attribution model distributes the value of a conversion across each touchpoint differently.

A model comparison tool allows you to analyze how each model distributes the value of a conversion. There are six common attribution models:

  • First Interaction

  • Last Interaction

  • Last Non-Direct Click

  • Linear

  • Time-Decay

  • Position-Based

By analyzing each attribution model, you can get a better idea of the ROI for each marketing channel.

There isn't necessarily a "best" attribution model. You may choose one as your primary attribution model for reporting and analysis. Different factors, like business goals or buying cycles, can make one model better than another.

Attribution modeling is an analysis tool, and you don't need to limit yourself to one and stick with it. Compare performance under each model to understand the importance of multiple touchpoints in the customer's journey.

Let's look at an example.

Currently, your default reporting is based on a last interaction model (more on each type below.)

You look at your analytics and only see a few leads from organic blog traffic. You're evaluating if you should keep investing in the blog. How do you prove the value of the blog?

You navigate to Google Analytics MCF Model Comparison Tool and view your conversions under the first interaction model.

Under a First Interaction model, the conversion value of your organic traffic shoots up! You realize that your blog visitors comeback to your site after clicking a retargeting ad, and then they sign up or purchase.

Under the Last Interaction model, the retargeting ad received all credit for those leads.

By comparing multiple attribution models, it's easier to understand how two (or more) marketing channels work together to generate conversions, so you can assign a conversion value to each channel.

Attribution models are useful, but they're also one of the more complicated topics in marketing.

In our complete guide to attribution models, we'll walk through the primary attribution models and when to use them. Let's get started on each type of attribution model.

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What are the different types of attribution models?

1. Last Interaction Attribution

google analytics attribution models

Last Interaction Attribution is also referred to as "last-click" or "last-touch."  As the name implies, this model gives 100% of the credit to the last interaction your business had with a lead before they convert.

For example, a visitor finds your website through organic search. A week later they see a Facebook Ad and click the ad. Later that day, they go to your website directly and make a purchase.

The direct traffic, in this instance, gets all of the credit for that purchase. 100% of the value is assigned to that last touchpoint.

This is the default attribution model in most platforms, including Google Analytics. If you are looking at standard conversion reports in Google Analytics, you're seeing each goal attributed to the last interaction your customer had with your business.

Pros & Cons of Last Interaction Attribution

Last Interaction attribution is the simplest to implement and evaluate.

It is also often the most accurate. Digital marketing today is scattered. People may access from multiple devices, clear cookies, or use multiple browsers. This makes it difficult to track their entire journey.

However, you can always be certain of their last interaction prior to converting.

The downside is that this model ignores everything that happens before the final interaction. Many of the interactions and touchpoints prior to that last-click will be just as important.

This model may be a good fit for you if you have a short buying cycle. If there aren't many touchpoints prior to converting, only tracking the last one will give you a good idea of your strongest channels.

You will also find this model especially helpful if your sales funnel is wide at the top, but narrow at the bottom.

2. First Interaction Attribution

google analytics attribution models

First Interaction is similar to Last Interaction, in that it gives 100% of the credit to one single click/interaction. First Interaction (also called "First-Click") gives all of the credit for a conversion to your business' first interaction with the customer.

For instance, if a customer first finds your business on Pinterest, then Pinterest gets all of the credit for any sale that happens after that interaction.

It doesn't matter if the customer found you on Pinterest, then clicked a display ad a week later, and then went to your site directly. Pinterest, in this example, gets the full credit.

Pros & Cons of First Interaction Attribution

The main appeal of using First Interaction attribution is how simple and straightforward it is. However, this model ignores the effects of any potentially important marketing channels that occur at a later point, such as retargeting ads.

This model is also helpful if your industry has a short buying cycle. If there is a tendency to convert customers immediately, then their first touchpoint is especially important. Or, if your main business goal is bringing in new top-of-the-funnel customers, First Interaction is a great model for evaluating each channel.

3. Last Non-Direct Click

google analytics attribution models

The Last Non-Direct Click Model is a bit more helpful than a standard last-click model. 100% of the value is still assigned to a single interaction. But, with last non-direct click, it eliminates any "direct" interactions that occur right before the conversion.

Direct Traffic is when anyone goes directly to your site by manually entering your url or clicking a bookmarked link, which means this visitor already knows about your company.

How did they learn about your company? What prompted them to go to your website directly? By eliminating direct traffic in a last-click model, you can better assign value to the marketing channel that led to the conversion.

Pros & Cons of the Last Non-Direct Click Model

As mentioned above, eliminating direct clicks makes this a more insightful model than last interaction. However, it still assigns 100% of the value to one interaction. If your customer had 4 touchpoints prior to that last non-direct click, it's completely ignored.

4. Linear Attribution

google analytics attribution models

With a Linear attribution model, you split credit for a conversion equally between all the interactions the customer had with your business.

For instance, a customer finds you on Instagram, signs up for your email list and later clicks an email link. The next week they go to your site directly and make a $120 purchase.

There are 3 touchpoints in this situation. Each touchpoint gets equal credit of 33%, or a $40 conversion value attributed to the channel when the purchase was made.

Pros & Cons of Linear Attribution

Linear attribution gives you a more balanced look at your whole marketing strategy than a single-event attribution model does.

However, this means it also assigns equal importance to everything.  Some marketing strategies are more effective than others, and this model will not highlight the most effective strategies.

If you want a nuanced attribution model that's straightforward and easy to explain to clients, linear attribution might be a good choice for you. It's also a great way to demonstrate how each channel does have value.

5. Time Decay Attribution

time decay attribution

Time Decay attribution is similar to Linear attribution—it spreads out the value across multiple events. But unlike, Linear attribution, the Time Decay model also takes into consideration when each touchpoint occurred.

Interactions that occur closer to the time of purchase have more value attributed to them. The first interaction gets less credit, while the last interaction will get the most.

*Pros & Cons of Time Decay Attribution

If relationship-building is a big factor in a business' success, using Time Decay attribution can be a helpful way to conceptualize that.

Keep in mind  that this model minimizes the effect of top-of-the-funnel marketing techniques. You may want to use a Time Decay attribution model when you're dealing with a particularly long sales cycle, such as for expensive B2B purchases.

6. Position-based Attribution

position-based attribution marketing model

The Position-based attribution model (also called U-shaped attribution) splits the credit for a sale between a prospect's first interaction with your brand and the moment they convert to a lead.

40% of the credit is given to each of these points, with the remaining 20% spread out between any other interactions that happened in the middle.

For example, if a prospect first makes contact with your business through a Google search, looks at your Facebook page, and later signs up for your email newsletter, the first and third touches each receive 40% of the credit, and the Facebook visit receives the remaining 20%.

Pros & Cons of Position-Based Attribition

Position-based attribution is a strong model for many business types that have multiple touchpoints prior to a conversion. It gives at least some credit to every interaction. But, it gives a stronger weight to your two most important interactions: the first time a customer found you and the interaction that prompted a conversion.

Bonus: Custom Attribution Models

google analytics attribution models

Do you know a particular weight or valuation you want each touchpoint to have? Do you have a very specific funnel that you want to evaluate?

You can create custom attribution models in Google Analytics. A custom attribution model lets businesses give a custom amount of weight to whatever touchpoints they think are most important.

Pros & Cons:

A custom attribution model provides the most nuanced look at what's getting you sales. However, it can be difficult to create, and this type of model requires a lot of data. Businesses that have a long buying cycle and plenty of data on hand are the best candidates for using a custom attribution model.

Where to Find Attribution Model Reports in Google Analytics?

Google Analytics uses last interaction attribution by default. However, you can compare different attribution models in your account. You'll find this tool under "Attribution" on the left-hand side of your account.

google ads reporting

By comparing each model, you can see the value each channel delivers under different attribution models. You can use their default channel groupings, or if you are customizing your links with UTM codes, click "Source" to see the value assigned to each source you are tracking.

In the above example account, direct traffic was attributed with 2,234 conversions with last interaction attribution. Under first interaction, that drops to 1,408—a 40% decrease!

By looking at both models, we can understand the value of the other marketing channels that led to the direct traffic and conversions.

google analytics dashboard template

Marketing Attribution Model Infographic

Attribution models are complex to use and understand. To help simplify it, we created an infographic that you can save and share below:

google analytics attribution models

Written by

Peter Foy

Peter Foy is a content marketer with a focus on SaaS companies. Based in Toronto, when he’s not writing he’s usually studying data science and machine learning.

Read more posts by Peter Foy ›

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