Attribution has been the foundation of marketing analytics since the dawn of marketing. Every marketing team worth their salt has some variation of an attribution model in place so they can optimize and prove the ROI of their campaigns. But the modern digital world has caused an evolution of the customer journey. The legacy attribution models marketers have in place are ill-suited for this new customer journey that has expanded in both length and number of interactions.
Single-touch attribution models still have their place, but marketers need to embrace multi-touch attribution to effectively report and optimize campaigns. Here are the pros and cons of the 5 most popular attribution models.
The first-touch attribution model assigns 100% of the credit to the campaign that initiated the very first interaction somebody had with your business. This model ignores the campaign that ultimately drove the conversion as well as each interaction after the initial touch.
Pros: Marketers who are solely focused on demand generation and don’t rely on conversions may find the first interaction model useful. This model highlights the campaigns that first introduced a customer to your brand, regardless of the outcome.
Cons: Using a first-touch attribution model offers very limited optimization ability to marketers. Without the ability to determine what ultimately drove conversions and revenue, marketers will have a hard time justifying their impact on the company’s bottom line.
100% of the credit goes to the marketing interaction that drove the conversion in a last-touch model. Marketers using this model will gain insight on campaigns that have the highest conversion rate, but lose sight of any influences leading up to the conversion.
Pros: This model can be powerful for marketers who are solely focused on driving conversions. If non-converting actions hold no value at all for your business, a last-touch model can be effective attribution strategy.
Cons: The last-touch model completely ignores influencers on the path to conversion. A lead may interact with your company a dozen times before converting and a last-touch model provides zero visibility into what can be very influential interactions.
The linear attribution model is the first step towards multi-touch attribution. This model assigns credit evenly to every marketing touch throughout the customer journey. If there are 10 touches, each will receive 10% of the credit. When there are 5 campaigns, each will receive 20%.
Pros: This model offers an easy way to start analyzing your marketing campaigns with multi-touch attribution. By assigning even credit to each marketing interaction, you can start optimizing for the customer journey instead of a single activity.
Cons: Linear attribution moves you past a single-touch attribution model, but still has many limits. Because every touch receives equal credit, you lose the ability to optimize for specific outcomes. Linear attribution assigns the same amount of credit to low-value touches like email clicks as it does to high-value conversion activities like demo requests, making it difficult to optimize for the demo request conversion.
The time decay model is another big step forward in multi-touch attribution analysis. Time decay assigns the most credit to the interaction that resulted in a conversion. Touches leading up to the conversion event receive less value the further back they are from the conversion.
Pros: Now we have the ability to truly optimize. This model recognizes the significance of every interaction leading up to a conversion while still placing the most value on the activity that actually drove the conversion. The touches closest to the conversion become more valuable as each increases the likelihood of a conversion. Marketers can use this model to optimize for touches that drive conversions as well as the touches which increase the likelihood of a conversion in the near future.
Cons: While providing excellent attribution for conversion optimization, this model lacks the ability to recognize the interaction which originally introduced the customer to your brand. The linear model may also result in a low amount of credit for highly influential touches (like a trade show visit) if it happened too early in the customer journey.
The position based attribution model combines the best features of the linear and time decay models. Position based will assign 40% of the credit to the first and last touch with the remaining 20% being divvied out evenly to every touch in between.
Pros: This model ensures that every touch point in the customer journey receives a portion of the credit while still allowing you to optimize for the first and last touches. This means you can assign significant credit to the campaign that introduced your brand to the customer as well as the campaign that eventually drove them to convert. Bonus points for adjusting the weighting in your position based model to reflect your business needs.
Cons: Blindly assigning so much credit to the first and last interactions can be dangerous. This could easily result in two very low-value touches being given too much credit. Think about this: does it make sense for a first touch email blast to get the same amount of credit as the paid search ad that resulted in a conversion?
While it may be easier to simply rely on the readily available pre-built attribution models, you can vastly improve your marketing attribution by building your own custom model. With a custom model, you can make adjustments to the weight for each position based interaction, assign more credit to higher value touches, and optimize for the outcomes which hold the most value for your business.
At the end of the day, marketers who rely on single-touch attribution models cannot effectively optimize their marketing mix or accurately attribute revenue to the right campaigns. With the ability to report on every marketing interaction, capture both online and offline conversions, and connect all of the dots, the result will be truly powerful multi-touch attribution analysis.
Contact us to learn more about why offline conversions need to be included in your attribution modeling.
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