As marketers create more content, leverage more channels, and vary the touches, it is essential given the investment of time, energy, and money that we understand each of these plays to the buying process, and which components have the greatest impact on generating conversations, consideration and ultimately consumption. This article explores optimization and attribution modeling, when to use them, how they differ, plus the various approaches for assigning and measuring attribution.
Recently a customer sent us a file that included the some of the following data fields: prospect name, request for proposal date, deal size, deal status (won or lost), marketing channel (website, tradeshows, digital, etc.), marketing touches (demo, phone calls, online chats, email, etc.), and marketing content (white papers, customer testimonials, thought leadership articles). They wanted to know if we could help them understand which channels, touches, and content were having the greatest impact on driving customer opportunities and wins. Essentially, they were looking to create an attribution model.
This scenario is becoming more typical as marketers create more content, leverage more channels, and vary the touches. It is essential given the investment of time, energy, and money that we understand the role content, channel, and touch points play in the buying process, and which components have the greatest impact on generating conversations, consideration and ultimately consumption increases significantly. Since time, energy and money are often in short supply, knowing how to use models such as marketing mix attribution and optimization to make the best content, channel, and touch point decisions are key skills that every marketer needs to add to their capabilities.
Before we launch into a conversation about various attribution models, a brief tutorial on the topic of optimization and attribution modeling might be helpful. There are a number of sources and tools available today to help create either model. Both attribution and optimization modeling are about improving mix and understanding the impact of marketing investments on customer behavior. Both approaches are important for measuring and improving the performance of multi-channel, multi-touch campaigns. Let’s begin by reviewing what these models are, the pros and cons of each model, when to use them, and how they differ.
Optimization relies on predictive models that track non-linear relationships between specific goals and spend levels in order to “predict” the incremental changes in conversions based on the relationship between the variables. Many organizations attempt to “optimize” campaigns via A/B testing, a form of scenario analysis. Unfortunately A/B testing doesn’t address the complex non-linear interactions. An algorithmic approach that simultaneously analyzes all possible scenarios is needed to see which combinations produce the best incremental results.
Attribution is based on capturing touch point data over a historical period to determine which touch points are the most effective at which stages in the buying process to support investment allocations and produce higher aggregate results.
Attribution is simply the ability to evaluate the performance of each touch point in the buying process. A key premise of attribution is that all touches play a role in impacting the buying process. To create any type of attribution model you need data related to both converting and non-converting opportunities. There are various approaches to attribution. Three of the most common are last-touch, equal attribution, and fractional attribution:
- Last-Touch Attribution is based on the idea that the last touch has the greatest impact on the buying process and therefore receives the majority or all of the credit for the entire sale. Some companies take the opposite approach and use first-touch attribution which is based on the idea that the first touch is what “primes the pump.” Neither of these models account for all the prior or following touches that may have impacted the buying behavior. As a result you may end up eliminating important earlier or later touches because you aren’t sure of their value. Despite the problems with these techniques, people use these approaches because they are relatively easy to create.
- Equal Attribution is one way to overcome last-touch attribution issues. Just like it sounds, this approach assumes that all touches are equal, which means that an equal value is assigned to every single touch. The downside is that you may end up unnecessarily duplicating some efforts because you aren’t sure which touches have the greatest impact. So you may end up investing more than you need to – because this approach doesn’t provide insight into which touches perform best. That takes us to the concept of Fractional Attribution.
- Fractional Attribution assigns a calculated “weight” to each marketing touch throughout the buyer’s purchase journey. Typically this weight is determined by the corresponding relative impact that particular touch will have on producing the desired business outcome, such as purchase. This approach enables marketers to take multiple prior exposures into consideration. Determining the weights requires understanding which touches perform best. Using fractional attribution requires understanding of the statistical significance of the various touches in order to quantify their contributing effect. When building this type of model and assigning weights it is important to keep in mind that there are touches other than marketing touches that drive the desired outcome.
The Optimal Approach
Most attribution experts agree that fractional attribution is better than last-touch or first-touch attribution. These experts typically recommend the fractional approach where marketers assign weights to touches based on their type, and position in the buying process to create the model. Marketers can then use this model to make touch and investment decisions.
The key challenge to address with the fractional attribution approach is that buying decisions are not serial or linear. Rather, it is often a combination of touches that impact behavior. This is why some experts have created incremental attribution models, which attempt to calculate the change in revenue resulting from a particular touch. With this technique, touches are classified by the buying stage they support, and buyers are tracked as they move through the stages. Marketers use this structure to compare the effectiveness of different touches (messages and media) in moving buyers from one stage to the next in order to determine the incremental impact on cost and revenue of the different touches. As you see as we move from first or last touch to fractional attribution there is increased complexity and sophistication.
Attribution models are typically framed in terms of assigning credit for a particular purchase. As marketers we know that one touch has ripples that can affect multiple purchases and behaviors. If you decide to tackle attribution, the need to combine online and offline quality data will become increasingly apparent. Attribution modeling serves as an important decision making tool. If you’re just beginning the process, create a roadmap for how you plan to approach model development, including defining the data sources and methodology. If you’re further along in your efforts by all means dive in. Regardless of how you decide to proceed, good data will be essential. Turning data into insights is table stakes in today’s environment. Looking for more on this topic? Check out the free white paper, Intuition To Wisdom: Transforming Data Into Models and Actionable Insights, at.