Marketing Attribution


Quick definition: Attribution refers to the practice of assigning credit for conversions or revenue to marketing touchpoints in order to pinpoint the ones that are working best and allocate resources accordingly.

Key takeaways:

The following information was provided during an interview with Nate Smith, group manager of product marketing for Adobe Analytics Cloud.

What is marketing attribution

How does marketing attribution divide credit among marketing touchpoints?

How does a marketing attribution model look and work?

How specific can marketing attribution models get?

Is there a recommended attribution model?

How often should you check your marketing attribution model?

Is it difficult to use an attribution model?

Does attribution impact other parts of business besides marketing?

What are some challenges that come with attribution, and how do you combat those?

How will marketing attribution be different in the future?

What is marketing attribution?

Put in the simplest terms, attribution marketing is a way to assign credit to all your marketing touchpoints for revenue or conversion. It's a way to identify the marketing decisions that actually produced revenue. It also helps you allocate your marketing budget accordingly, dividing the larger sections of your budget to more successful touchpoints.

To better understand attribution marketing theory, a great analogy to use would be a soccer match. Before a team scores a goal, the ball might hit several different feet, until one last kick secures the point. But each set of feet contributed to winning that goal. The same happens in marketing. Attribution marketing pinpoints the marketing “kicks,” or choices, that led to winning the business goal, such as conversion or revenue. And marketers use attribution marketing models to decide which types of kicks their companies should do more of.

How does attribution marketing divide credit among marketing touchpoints?

There are several different types of attribution marketing models. The three most common ones are first-touch attribution, last-touch attribution, and linear attribution. These models fall under a type of attribution model called a rules-based model. The model you choose for your company will reflect the different strengths of your marketing touchpoints.

You can adjust rules-based attribution marketing models to assign credit to whichever touchpoints you want to focus on. For example, you can use time-based attribution to assign credit to your most recent touchpoints.

Besides rules-based attribution marketing, there’s also another category called algorithmic attribution. This type of attribution marketing uses machine learning to determine where credit should be assigned. This approach is called the “best-fit model,” and some marketers prefer this model because it’s less work for them. However, it might be a good idea to apply some other types of models to compare to an algorithmic model.

How does an attribution marketing model look and work?

Typically, attribution data from an attribution marketing model is shown as a table. In the left column will be the channels you’re pinpointing, and in the right column will be your metrics, such as revenue, item orders, or any other type of conversion metric you want to measure.

The model will calculate the impact for each channel that you choose to include in your model. It will also show revenue percentage, contribution percentage, and other metrics you want to add. The same thing happens with an algorithmic model. If you’re using Adobe Analytics, you can even create visualizations of the data that you can show to stakeholders.

How specific can attribution marketing models get?

Historically, attribution marketing has been based on marketing channels, paid media being the most common and over-rotated channel. The problem with this, however, is that in focusing on paid media as your only channel, you’re missing out on deeper-funnel touchpoints.

Assigning a ton of credit to one broad touchpoint like paid media doesn’t help you improve something more specific, such as your email optimization. More useful technology would allow you to focus on deep-funnel specifics like campaigns and even search terms.

Adobe already offers this type of technology. With Adobe Analytics, you can assign algorithmic, best-fit models to things like email campaigns and determine which type of attribution to applies to other campaigns and channels.

Adobe’s main priority is to offer as many types of attribution marketing models as possible. We don’t push one type of model, because we want you to choose the types of attribution that are perfect for your marketing strategy. There is no attribution marketing model that provides every bit of information that you need. The types you choose will depend on what each user in your organization needs and each use case.

For instance, digital analysts and channel marketers are tasked with answering questions about marketing, whatever those may be. Their use-case would be using attribution to garner data that would answer those types of questions. Operations’ use case for attribution is determining how to best optimize media spend. B2B companies’ use case would be account-based attribution, because of their longer sales cycles

How often should you check your attribution marketing model?

A great rule of thumb is to check your attribution marketing model quarterly — at a minimum — and make appropriate adjustments. This regular maintenance lets you account for new types of marketing choices or changes to your marketing strategy.

Is it difficult to use an attribution marketing model?

Attribution marketing models are marketer-friendly. You do need some knowledge about data to make things easier, but for the most part, an attribution marketing model is designed to help marketers understand how to allocate their budgets. The industry is pushing to make attribution more and more user-friendly. Adobe has already taken big steps to make attribution easy to use and learn.

Does attribution impact other parts of business besides marketing?

Attribution can be used for a lot more than just marketing touchpoints. For instance, you can use it for product development, including with digital products like mobile apps and websites.

Attribution can help you figure out how you can refine your product. You can also use attribution to improve customer service, especially with post-sale interactions. Attribution can help you understand what types of interactions work, and which don’t.

Even though attribution has mostly been used for ad spend, you can use attribution for any area of your business that you’re looking to improve, as long as it has a measurable result that you can trace to a choice.

What are some challenges that come with attribution marketing, and how do you combat those?

A big challenge with attribution marketing is how easily it can be manipulated. Because people don’t want to lose their portions of the budget, they can run attribution models that purposely make their own work seem more impactful than it might be.

To combat this, it’s important to have a level of governance over attribution marketing modeling, to avoid different teams and departments fighting over control so that they don’t lose funding. You could assign this governance to a specific team and decide as a company what type of attribution models and lenses you will choose.

How will attribution marketing be different in the future?

To bring some context about how attribution marketing has worked for now, the way to gather personal data about potential or current customers has been through third-party cookies. But companies like Verizon, Google, and Adobe are currently trying to find a new way to gather data for different personas, while still respecting customer privacy.

Besides focusing on privacy in future evolutions of attribution, the industry is also focusing on breaking down the siloed aspects of attribution. Instead of focusing on only one part of the customer journey at a time, attribution platforms are seeking to provide an experience that would help put the entire customer journey through the different lenses of attribution. Instead of just ad spend, the future of attribution will focus on the holistic customer experience.

And the biggest piece of technology that’s going to help achieve this is AI and machine learning. The future of attribution points to highlighting specifics of the customer journey like cross-device ID, and even customer biases.

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