Getting started with multi-touch revenue attribution requires an understanding of the various approaches. There are several commonly accepted models, and your choice depends largely on your business needs and data set. At BrightFunnel, we recommend experimenting with a variety of models to determine which attribution model is the best fit for your organization.
In our last series installment, we looked at the most common single-touch approaches to attribution, the starting point for most marketers seeking get started with attribution. In this post, we’ll focus on a range of common multi-touch models—offering the pros, cons, and example scenarios for each of the various approaches.
Before we get started, consider the following scenario*, which will be referenced throughout the “Revenue Attribution Basics” series as we examine the pros and cons of each model:
Jane Smith from ABC Company visited your company’s booth at a conference and was scanned by your booth staff, which was then used to create a lead in your CRM. Wanting to learn more about your company, she watched a video on your website, signed up for a webinar, and ultimately downloaded an eBook before an opportunity was created for ABC Company with Jane as the Primary Contact. Eventually, after engaging with sales, ABC Company signed a deal for $20K.
* While the average B2B buyers’ journey spans at least thirteen different touch points, for the sake of simplicity, we’re using four touches in this example. Similarly, most purchase decisions are made by a collective group of stakeholders, rather than an individual.
ALSO KNOWN AS: Linear Attribution
ABC COMPANY SCENARIO: Revenue credit for all of Jane’s interactions are split evenly among all campaigns (4 campaigns all receive credit for $5K, totaling $20K).
THE BOTTOM LINE: An improvement from single-touch, the simplicity of linear attribution makes it the most common starting point for most marketers seeking to employ multi-touch attribution. While this approach does apply credit to all touches along the buyers’ journey, it runs the risk of overvaluing lower-impact touches, which can lead to faulty assumptions about the effectiveness of a campaign. For instance, an email click-through may receive the same credit as a demo request.
Time Decay Attribution
ABC COMPANY SCENARIO: Jane’s interaction closest to the point of conversion—downloading your eBook—receives the most credit. All prior touches receive less and less, the farther back in history they are from the conversion event.
THE BOTTOM LINE: The core premise of the time decay model is that the closer a touch point is to conversion, the more it should receive. As respected author and Google Digital Marketing Evangelist, Avinash Kaushik, puts it: “If early touchpoints were so magnificent, why didn’t they convert?”
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ALSO KNOWN AS: U-Shaped Attribution, 40-20-40 Mode
ABC COMPANY SCENARIO: Jane’s first and last interactions receive the majority of credit ($16K total), while the remainder of credit ($4K) is divided among mid-funnel activity.
THE BOTTOM LINE: In this example, a position-based model generally attributes 40% of credit to the first and last touches, and distributes the remaining 20% evenly amongst mid-funnel interactions. These percentages, of course, can be adjusted in the process of finding the model that makes the most sense with your programming. Position-based attribution offers an interesting multi-touch option for marketers who want to emphasize lead generation and last touch events, without discounting mid-funnel nurture activity.
ABC COMPANY SCENARIO: Because you’ve historically seen that prospects who’ve engaged with webinars and eBooks are more likely to convert, more revenue ($8K and $6K, respectively) is attributed to these events than a conference or video ($4K and $2K). Credit is disbursed accordingly among all of Jane’s interactions.
THE BOTTOM LINE: A custom interaction-based attribution model relies on historical analysis to apply different weights to varying interactions. The biggest danger with interaction-based models is that they can often be subjective, and marketers must put considerable thought into which types of user behaviors are most valuable. Ideally, these decisions are informed by historical behavioral patterns and, because of this, it’s strongly recommended that you first experiment with models that don’t require gut-based inputs—first, last, linear, and time decay—in order to identify behavioral trends, before layering in other factors that are important to your business.
Have you seen success with multi-touch attribution? Stay tuned for the next installment of our “Revenue Attribution Basics” series to learn more about how machine-learning algorithms use scientific and proprietary algorithms to statistically determine appropriate credit.