Digital Marketing Attribution Models, An Introduction


Attribution models help us determine which marketing strategies contribute to sales and conversions. That’s the simple definition, but this area of digital marketing has become more complex and sophisticated in recent years. 

Advanced marketing attribution requires the normalization and aggregation of consumer data across various channels. When executed properly, this ensures each consumer interaction is weighted properly. 

What does this mean in the real world? Well, imagine you’re promoting the same product using an email marketing campaign and a display ad. The same consumer is exposed to both marketing strategies, but only converts after seeing the display ad’s special promotion. If this is part of a pattern, you may want to divert more resources to the display ad. You might also decide to tweak your email marketing strategy. 

To effectively attribute the success of all your various marketing strategies and channels, you need advanced analytic programs that efficiently extrapolate Big Data into usable insights. This is what attribution models do. 

In essence, digital marketing attribution is all about determining which messages and channels have the greatest impact on the consumer’s decision to convert. 

An effective attribution model should deliver the following insights:

  • Which touchpoints persuaded the consumer to convert
  • The specific messages a consumer was exposed to
  • Which channels the converting consumer used
  • Which messages achieve the best results within specific demographics
  • How external, real-world factors influence conversion decisions

What Are the Different Types of Attribution Models?

There are several types of attribution models. Which you choose depends on a range of factors.

Last-Click Attribution

Last interaction attribution gives full credit to the last touchpoint the consumer interacted with before converting or making a purchase. This is a crude way to determine attribution, as it only tells you a small part of the consumer’s journey. The path to that final interaction might have included Facebook and Twitter. But if the consumer’s last touchpoint was Google, that’s where the credit is attributed.

The Pros and Cons of Last-Click Attribution

Last-click attribution is a simple approach that measures what might well be the most important touchpoint on the road to conversion. 

But this type of model doesn’t tell you the whole story. There will be other marketing messages that aren’t getting the credit they deserve. And if you don’t recognize this, you might be missing out on higher conversion rates. The decision to convert rarely involves just one touchpoint. 

First-Click Attribution

First click attribution relies on the assumption that the consumer decided to convert due to the first message they saw. This model doesn’t take into account any subsequent touchpoints after the initial advertisement or message. 

 The Pros and Cons of First-Click Attribution

First-click attribution lets you know how people are discovering your business online. This approach is simple and easy to implement. 

But this model only gives you a very small part of the conversion story. That first click gives you an idea of why the consumer was first drawn to your brand, but it doesn’t tell you about why they chose to convert. 

Linear Attribution

Linear attribution tells the story of the consumer’s journey to conversion. Each touchpoint is given an equal weighting, so each marketing message is given equal credit. 

The Pros and Cons of Linear Attribution

Linear attribution tells you more about the consumer’s journey to conversion. This model can teach you a lot about how your customers think and make buying decisions. 

But linear attribution gives equal weight to all touchpoints. It doesn’t tell you which channels or messages were most important on the road to conversion. Time Decay Attribution

Time Decay Attribution

Time decay attribution gives each touchpoint to conversion different weighting. The most credit is given to the marketing messages nearest the point of conversion. 

The Pros and Cons of Time Decay Attribution

This model gives extra weight to the touchpoints nearest conversion. This is particularly handy if your sales process is relatively long, as the funnel often mirrors the steps consumers take during a specific time period.

But this model doesn’t weight touchpoints accurately. For example, imagine a consumer clicked a touchpoint a month before converting. This model wouldn’t reward that initial click with sufficient credit. 

Position-Based Attribution

Position-based attribution gives most credit (40%) to the first and last interactions along the route to conversion. The remaining credit is attributed equally among the transitional touchpoints in a linear fashion. If there are only two touchpoints, however, both are given 50% credit. This is sometimes referred to as the U-shaped attribution model.

The Pros and Cons of Position-Based Attribution

Position-based attribution models give weight to the first and last clicks. This recognizes the fact the initial attraction to your brand and the decision to convert are the most important stages on the journey to conversion. 

But this model might undervalue the various touchpoints between the first and last. If you’re using remarketing ads, for example, this model will deliver a skewed impression of what’s going on. 

W-Shaped Attribution

The W-shaped model is similar to the position-based model (U-shaped), but it includes an additional touchpoint called the “opportunity stage.” The first interaction, last interaction, and the opportunity are all given 30% credit. The remaining 10% is divided equally among all the remaining touchpoints. 

The Pros and Cons of W-Shaped Attribution

This model gives equal weighting to the middle of the conversion journey. This is particularly beneficial if you’re generating leads.

Customer journeys to conversion are growing increasingly complex. This model doesn’t tell a complete story. Modern consumers frequently switch between channels, messages, and devices before converting. The W-shaped model fails to capture these nuances.

Data-Driven Attribution

All of the other attribution models in this list work on the assumption that a particular click is the most valuable. But with the data-driven attribution model, you use your end goals to determine the efficacy of each marketing message on the route to conversion. Then, each channel is weighted based on how effectively it helps you to achieve these goals. 

For example, it’s possible that a consumer’s path to conversion includes social media, a display ad, email, and paid search. The data-driven attribution model ensures each of these touchpoints gets the credit it deserves.

The Pros and Cons of Data-Driven Attribution

Data-driven attribution takes into account several issues when attributing credit. Over time, attribution software collects and analyzes data in order to create a model that’s accurate for a specific product, service, or business. 

But for data-driven attribution to deliver accurate results, you need high levels of traffic. 

Which Attribution Model is Best for My Business?

 An experienced marketer with a proven track record of success will utilize most types of attribution models. But how do you know which model to use, and when to use it? 

Ask yourself what your marketing end goal is. Are you selling a specific product? Are you building an email database for a regular newsletter? Are you looking for customer registrations? What will persuade your target consumer to convert? Will it be the initial impact of a display ad? Or will it be a sponsored post that gives the consumer value and detailed information on your product?

Once you can answer these questions, you can decide which attribution model is the most effective “measuring stick.”

Choosing the best attribution models for driving your business’s online marketing strategies is a complex, time-consuming process. Let ONE12th manage all your data-driven digital marketing needs, so you can focus all your efforts on running your business. 

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