Often an overlooked piece of the marketing puzzle, attribution requires the development of a program that provides insight and feedback on which pre-revenue activities were most impactful in developing a pipeline or assisting in a revenue transaction. Establishing and tracking these contributions is crucial, since it helps companies to effectively structure content calendars, assign budgets, and invest in—and deprecate—pieces of the tech stack. In other words, it allows decision makers to determine which third-party tools are necessary to tie all of the data together.
In other words, attribution clearly identifies where you should invest resources and what you can expect the returns from those investments to be. A one-size-fits-all method of attribution doesn’t exist, since companies have unique marketing and sales tactics; however, there are commonly accepted practices that most companies utilize as a starting point.
Single Touch Models
First Touch – This approach assigns 100% of the conversion value to the first known touch—most often, the first form that a website visitor submits (otherwise known as the identification event). Through this model, a record with the person’s information is entered into a company’s MAP/CRM system, giving credit to the source of the information.
Last Touch – This model assigns 100% of the conversion value to the last known touch prior to an opportunity being created (e.g. Primary Campaign Source in Salesforce). The Last Touch model, which is one of the most prevalent models used today, analyzes a person record, and gives all of the credit to that person’s most recent campaign response.
Last deliberate touch – Very similar to the Last Touch model, this approach excludes channels such as direct/none and referrals, since they don’t provide any insight into where or how a company should allocate budgets. These factors are only included if they exist as part of a specific marketing program.
Data Strategy & Pipelines
Turn user activity into actionable insights while reducing cost, complexity and privacy risk.
Learn More →Multi-Touch Models
Linear – With this model, each touch point along a visitor’s journey receives the same amount of credit. For example, if a visitor’s interaction with a site has 10 touch points, each of them would earn 10 percent of the credit. This approach is a good starting point for companies that need to determine which of their touch points are most valuable but still want to see the full picture when it comes to user engagement.
Campaign Influences
Time Decay – This is a useful model for evaluating the effectiveness of a touch point, especially if the customer lifecycle is lengthy and the act of purchasing comes with a lot of deliberation. By taking this approach, companies assign a value to the most recent touch point and use an algorithm to detract percentages of that value based on how far in the past each earlier touch point occurred.
U-Shaped – Using this model, the first and last touch points share an equal—but disproportionately large—percentage of the credit. With a U-Shaped campaign, the first and last touch points are each typically assigned 40 percent of the credit, with the remaining events splitting the final 20 percent. (Companies can modify these percentage totals based on what makes the most sense.) The U-Shaped model works best for companies that rely heavily on of top-of-the-funnel marketing, since they’ll learn how a consumer was first introduced to their brand and what persuaded them to become a customer.
W-Shaped – As you might expect, with this model, the first, last, and chosen middle touch points all evenly split the credit (e.g. a campaign identifying a person record, the campaign before MQL is created, and the campaign before SQL is created). The W-Shaped model is often used when a company has a specific set of conversion-focused materials but is also pushing other types of advertising. The large, conversion-focus campaigns would split the attribution, while other materials are seen as assisting components to those campaigns.
Markhov Chain – Markhov chains determine the synergistic effect that different platforms have on one another by analyzing how users transition from one source/medium to another. With this type of attribution, statements such as, “if we remove channel X, overall conversions would decrease by Y percent,” can usually be made. For companies that are spending a lot on tactics that don’t directly generate conversions, this approach is especially useful, since it reveals what kind of impact removing one of those tactics will have.
Shapley Model – This model uses game theory to identify the marginal contributions that each channel is making toward an overall conversion. It focuses on every possible combination of channels that contribute to a conversion, identifying the ROI for each combination, and it uses this information to determine how much of a share each channel should receive. This model works best when it can evaluate the entire funnel, looking at actual revenue generated. And it’s an extremely useful tool for companies that need to analyze hard dollar amounts.
Custom – As its name suggests, a custom model can be anything that a company believes aligns well with its specific needs or structure. The previous models described are only suggestions. Some clients find they aren’t the most effective methods to use. If you’re considering a custom model, it’s a good idea to have a data engineer guide the structure of that custom model, as things can get complicated, especially at scale.
Determining which attribution model is right for your company can be a complicated task. Ultimately, the purpose of all of these systems is to assist a company’s growth, providing insight as to where investments should be made—both in terms of the right tools and the right employees. With this in mind, here are a few questions to ask yourself as you begin the journey toward improved attribution:
What Is Our Current Model’s Biggest Fault? How Would A New Model Address This?
This may sound simple, but too often marketing professionals and many corporate executives become enraptured with the newest trends in attribution and are drawn to a new method simply because it’s popular. Before you implement any changes to your attribution model, make sure you can clearly answer these two questions. Those answers will provide a lot of clarity and will dictate the approach that you take.
What Is The Source For All The Necessary Data?
For several of the models outlined above, platform data may be all that’s needed. However, that’s not always the case. Most multi-touch models operate best when post-marketing-qualified-lead (MQL) data is included. Incorporating that data often means integrating with software programs such as Salesforce or Pardot, and often requires developer bandwidth.
Enhanced Analytics Stack Drives Revenue Growth
Segment was deployed across the website and app, generating events based on user activity. These events were then used to build a comprehensive reporting system to track user behavior, the number of insurance policies sold, and total revenue generated. This allowed us to gain visibility into ROAs and help them allocate funds for paid and other marketing efforts more effectively.
View customer story →Who Will Be Using The Insights And Data That This System Produces?
Remembering who the end users are in this process is paramount to the success of any data system, including attribution. If you put a system in place that is too complex for the end users to utilize, no meaningful insights or change will occur. Pay special attention to the amount of time that is necessary to produce meaningful insights, not to mention the amount of time that is needed for new team members to become competent using the system.
At the end of the day, attribution is all about tracking user interactions with your company and assuring that those interactions are as efficient and effective as possible, especially as it pertains to customer conversion. There is no such thing as a “best” model. A business-to-business enterprise will likely use a different model than one that operates as a direct-to-consumer operation; and a company with an expensive product will likely have a different model than a company that specializes in affordable wares.
The most important thing to understand is that attribution is an ever-evolving practice. A company may change models over time, simply because the way it does business has changed. If you’re wondering when the time is right to reevaluate your attribution models, consider that approach if it feels like your corporate budgets aren’t carrying the same weight that they once did. It could be that your attribution model is no longer portraying accurately how much each channel is contributing to the bigger picture. A solid program can only be built when you have a foundation of solid data from which to start.