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It used to be so simple. Consumers became aware of brands through traditional means: driving past a billboard, hearing a radio spot, or seeing a commercial. But with the democratization of the digital space, the process of reaching consumers has drastically changed.

Consumers now live in a world where they’re inundated with digital ads everywhere they work, live, and play online, adding complexity to a once linear customer journey. This complexity makes it a challenge to determine which marketing initiatives and channels are contributing to the bottom line. Thankfully, attribution modeling has come to the rescue, providing the deeper insights that modern marketers need to thrive in a crowded digital space.

For more from our attribution series:
Why First-Touch and Last-Touch Attribution Are Out of Style
Designing a Multi-Touch Attribution Model
A Beginner’s Guide to Data-Driven Attribution
How to Choose the Right Attribution Model for Your Business

What Is Attribution in Digital Marketing?

Simply put, attribution helps digital marketers identify which touchpoints a customer interacts with before making a purchase. It requires a clear understanding of the customer journey, including where and how consumers engage with a brand across marketing channels. When these elements are in place, marketing teams can use attribution to better understand which of their efforts, platforms, and messages are performing well with their audiences, and which need improvement. When used properly, attribution is a key tactic for better ROI and improved customer experience.

Why Is Attribution Important?

Attribution is vital for marketing teams because it helps to determine which touchpoints effectively drive conversions. However, it’s critical to ensure that the attribution model you use accurately represents how customers interact with your brand. Accurate attribution enables marketers to allocate budgets and resources in a way that maximizes ROI and boosts conversions. Perhaps to understand its importance, it’s easiest to consider what marketing looks like without attribution. If you never know how customers interact with your brand, which efforts are successful, and which are costing your company without providing return, how can you expect to compete and thrive in an increasingly competitive business landscape?

What is Attribution Modeling?

Attribution modeling is the process that businesses use to distribute credit for conversions across multiple touchpoints. This exercise enables marketers to define their most successful channels and to optimize their omnichannel strategies. Recently, more multi-channel attribution tools, like funnel tracking, have become available, making attribution modeling simpler than ever. As a result, it has become an essential element of conversion-based marketing strategies. 

How Should Attribution Be Used?

Quality data and analysis are key to making strategic marketing decisions. When functioning correctly, an attribution model can be used to improve a company’s strategies, allow for accurate measurement of campaign performance, and provide valuable information about customer behavior. For example, if a company finds that consumers convert after a particular how-to product video, they can put more budget behind promoting the video. Similarly, if a certain email rarely converts and has low engagement numbers, the marketing team can refresh the copy and include an enticing offer to make it more valuable. These are just a few examples of how attribution impacts marketing efforts in practice.

Overview of Attribution Models

There are many attribution models available. Finding the right one for your company, audience, and buyer’s journey is critical to ensuring that you get the best information possible. This data will guide crucial aspects of your marketing strategy, so be sure that the model you choose aligns well with your goals and process. Utilizing the right attribution model will help increase your conversion efficiency, drive more revenue, and accurately forecast the marketing activities that will move the needle for your business.

The three main categories of attribution models are single-touch, multi-touch, and data-driven. Here are some of the highlights of each approach. 

Single-Touch Attribution

The single-touch attribution model assigns 100% of the credit for a sale to just one touchpoint—the first or the last.

  • First-touch: This model assigns 100% of the credit to the first marketing touchpoint (the channel or activity that acquired the contact).
  • First-click: This model assigns 100% of the credit to the “first-click.”
  • Last-touch: This model assigns 100% of the credit to the final marketing touchpoint before conversion.
  • Last-click: This model assigns 100% of the credit to the “last-click” before conversion.

Pro: This approach is simple to use. Marketing teams can track how people first discover or engage with their brand, or which interaction last pushed the customer to convert. 

Con: Various sources will tell you that it typically takes between 5 and 60 interactions with a brand before a consumer makes a purchase. Since sales cycles can vary dramatically depending upon the product, audience, and company strategy, it makes sense that the number is wide-ranging. However, they all agree that it takes more than one interaction to get the job done. 

Because single-touch does not provide the omnichannel view that encompasses the entire customer journey, brands that adopt this model miss the influential touchpoints that lead to the final purchasing decision. This option is limited, at best.

Multi-Touch Attribution

The multi-touch attribution approach acknowledges that there is a wide range of touchpoints that contribute to the final conversion. However, the credit is dispersed differently between the touchpoints depending on which model you choose. Multi-touch is typically more accurate than single-touch attribution because it looks at the full path that a customer travels before they purchase. 

The following are rule-based attribution models. With the rule-based approach, the marketing team will subjectively determine how much credit should be applied to each touchpoint.

Linear

The linear attribution model assigns equal credit across all touchpoints. It is often used by rapid-growth companies that aim to scale quickly. 

Pros: This option is easy to understand for marketers who are new to attribution.

Cons: The simple approach is not always an accurate one. As brands become more advanced in analytics, they are likely to discover that certain touchpoints have a more powerful influence over conversion than others. The even spread of a linear attribution model can make it difficult to see opportunities for optimizing campaigns and product use. 

Time Decay

The time decay attribution model assigns the most credit to the final touchpoint. The allocation of credit decreases based on the distance from the sale. The first touchpoint will have the least amount of assigned credit. 

Pros: This is a preferred method for many marketers because it acknowledges all touchpoints while giving the final interaction extra credit for getting the conversion. This aligns nicely with the value that many marketing teams place on the final touchpoint.

Cons: It devalues the touchpoints at the top of the funnel. This may incorrectly alter the importance of those interactions in the minds of the marketing team and inaccurately represent the true impact of those efforts on future customers.

W-Shaped

The w-shaped attribution model credits the first and final touchpoints with 30% each (60% total). The remaining credit is divided between the additional touchpoints throughout the customer journey. 

Pros: This approach acknowledges the heavy lifting of the initial interaction that captures the consumer’s attention and the final touchpoint that closes the deal. 

Cons: Valuable interactions may live in the middle of the journey, and this model would not account for their impact in an accurate way.

U-Shaped / Position-Based

The u-shaped attribution model is a hybrid between the first- and last-touch models. It assigns 40% of the credit to the first and last touchpoints (80% total), splitting the remaining 20% between the middle interactions.

Pros: The pros are similar to the w-shaped model. Position-based attribution is popular with leads-based businesses that are looking for a clear view of conversion drivers.

Cons: This model misses the middle touchpoints even more significantly than the w-shaped model does since it only reserves 20% of the credit for those interactions.

Custom

If a company knows how their customer journey is unique, the custom model is a terrific approach. They can allocate credit based on their findings rather than using a generic attribution model. The weight placed on each touchpoint can be carried over into the ad budget as well to optimize for the highest-performing interactions.

Pros: This option is custom-built to your organization, which should lead to more accurate results and enhance ROI.

Cons: If the internal analysis is inaccurate, the custom model may become less useful than one of the standard approaches listed above. Marketing teams must continue to monitor these touchpoints and update the custom attribution model accordingly to maintain its accuracy.

Data-Driven Attribution

Unlike the attribution models listed above, data-driven attribution is not determined by human opinions. This model uses an intelligent combination of real data and machine learning technology to determine how much credit to assign to each touchpoint across marketing channels. The technology automatically updates the attribution model based on the current data to deliver higher quality results than a subjective approach would.

Moving Forward with Attribution Modeling

The future of attribution modeling will center around analytics tools that are purpose-built to clarify the attribution process and provide a clear picture of where conversions come from. The more that brands can harness the power of their own data from their websites and marketing channels, the more accurate their attribution models can become.

With the introduction of technologies such as AI and machine learning, marketing teams benefit from the ability to respond in real-time as consumer behavior shifts and marketing channels become more or less influential in purchasing decisions. Automatic refinement of attribution models and the allocation of advertising budgets will relieve marketers of the strain placed on their time, resources, and finances. Ultimately, marketers will enjoy a more transparent process that provides a clear understanding of the customer journey and enables brands to earn more with less effort.