Understanding Marketing Attribution Models: Which One Is Right for You?

Marketing professionals rely on marketing attribution models to inform and optimize their campaigns. If you plan on getting into marketing as a career, knowing the different types of marketing attribution models (and when to use them) can serve you well. At the same time, understanding different types of marketing attribution models can be complicated, especially as innovations like generative AI have entered the game.
 

By knowing how to select the appropriate marketing attribution model and how to avoid some common mistakes, you can be well on your way to optimizing your future campaigns and maximizing returns on investment (ROI) for your clients.

What Is Marketing Attribution?

More specifically, marketing attribution models refer to strategies that marketing professionals use to better understand how different factors can impact the customer journey. Using different models, marketers can determine which marketing efforts are most effective at various stages of the customer journey.

Using marketing attribution modeling, marketers can not only analyze the success of their individual campaigns but also examine more closely which aspects of their campaigns are most effective in attracting leads and converting customers. Marketers can leverage these insights to refine future marketing strategies, maximize client ROI, and deepen their understanding of target audiences.

What Are the Different Types of Marketing Attribution Models?

There are several different types of marketing attribution models that marketing professionals can use to better understand the customer journey and each single touchpoint along the way. Each model has its own unique benefits and applications, so marketing teams need to know when to use each to achieve their goals.

First-Touch Attribution

In a first-touch attribution model, all "credit" for the customer journey goes to the initial interaction that engaged them in the first place. Also known as first-click attribution modeling, this strategy looks specifically at the marketing efforts that are most effective in bringing customers into the funnel. This is regardless of the other marketing activities that may have contributed to a conversion or purchase after the fact.

Last-Touch Attribution

It can be helpful to think of last-touch attribution modeling as the polar opposite of the first-touch model. In a last-touch or qualified-lead attribution model, all credit is given to the final marketing interaction before the customer converts. This model can be helpful in helping marketers hone in on the specific marketing efforts or channels that are driving customers to action, which can be useful in some contexts.

Linear Attribution

Whereas first-touch and last-touch attribution give 100% of the credit for a customer conversion to a single touchpoint, linear attribution aims to take a more balanced approach. In a linear attribution marketing model, credit is evenly distributed across all touchpoints along a customer's journey. This means that if the customer journey involved four different marketing activities, each channel would receive a 25% share of the credit.

Time-Decay Attribution

Another similar attribution model used by some marketing teams is known as time-decay attribution, where each touchpoint receives credit for a conversion. However, rather than dividing that credit into equal shares among marketing efforts, the touchpoints that occurred most recently receive a greater share of the credit because it is considered more relevant.

Position-Based Attribution (U-Shaped)

Last but not least, position-based or U-shaped attribution models split equal credit (40% each) for a conversion between the first interaction with the brand and the stage at which they convert into a lead. From there, any remaining credit is distributed evenly among any remaining marketing touchpoints.

Choosing the Right Attribution Model for Your Purposes

With several marketing attribution models to choose from, how can marketing professionals decide what's right for their unique applications? Ultimately, there are a lot of factors to keep in mind when determining which attribution modeling strategy will best apply to your campaigns.

One of the biggest determining factors of this will be the length of the sales cycle. For example, longer sales cycles with higher purchase values, such as a first-touch model, may make more sense. With shorter sales cycles, a last-touch model can provide a more robust understanding of where and how conversions are taking place.

Other considerations for choosing a marketing attribution model may include:

• Your marketing team's data analysis capabilities
• How deep of an understanding you need of your customer journey
• How complex your marketing campaigns are (number of channels, etc.)
• The extent to which external factors (such as the price of materials/supplies) may also affect sales

With a more comprehensive understanding of these different factors and their potential impact on campaign effectiveness, marketing teams can choose the attribution modeling strategy that best suits their clients.

Why Is Marketing Attribution Important?

No matter your strategy, being able to choose the right marketing attribution model to suit your needs is essential for a few reasons. The right marketing attribution modeling strategy can:

• Enhance ROI analysis, giving your clients a better understanding of how their marketing dollars are working for them.
• Improve customer insights by helping your team better understand and visualize each stage of the customer journey.
• Ensure your marketing team has the information and insights it needs to make data-driven decisions to optimize campaign performance.

How Does Generative AI Impact Marketing Attribution?

In recent years, generative artificial intelligence has been adapted by marketing teams to automate content creation, improve personalization, and optimize campaigns. Now, marketers are also beginning to see how generative AI can be used to improve marketing attribution modeling.

Enhanced Data Processing and Pattern Recognition

Using generative AI technology, even marketing teams without robust data analysis tools at their disposal can process large amounts of modeling data, pinpoint trends, and make predictions that can enhance decision-making for individual campaigns.

Real-Time Attribution Insights

In some cases, marketing teams may even be able to use generative AI to analyze historical data, which can then be used to gain real-time insights into the different touchpoints that can be attributed to conversions and purchases.

Integration of Non-Traditional Signals

With more robust data analysis tools at their disposal, marketers may also be able to rely on generative AI to more accurately assess the impact of less traditional marketing channels on the success of their overall campaigns.

Personalization at Scale

Finally, generative AI technology enables marketing teams to create highly personalized marketing campaigns without the need for additional investment or resources. This enhanced personalization will also create the need for more sophisticated attribution modeling to assign attribution to these different marketing activities.

Common Marketing Attribution Mistakes to Avoid

When the right marketing attribution models are used, marketing teams can gain extremely valuable insights into their customers' journeys while improving overall ROI. At the same time, when these common mistakes are made, marketing dollars and resources may also be wasted.

Correlation-Based Bias

One of the most common marketing attribution modeling mistakes is correlation-based bias, which occurs when marketers fail to remember that correlation does not always equal causation. At the end of the day, the customer journey can be incredibly complex, so giving 100% of the credit to a single marketing interaction could be drastically oversimplifying things.

Cheap Inventory Bias

Another common mistake marketers make is falling into cheap inventory bias. This occurs when marketers attribute credit to a campaign with lower-priced products simply because the products sold better than higher-priced ones. In reality, what most likely happens in this scenario is that the conversions were simply due to the cheaper goods and not anything directly related to their marketing.

Brand and Behavior

Unfortunately, marketing attribution models can never account for every consideration when looking at the success of a marketing campaign. All too often, for instance, marketing professionals will overlook the importance of a brand's perception or image when it comes to purchasing behavior. In some cases, brand-building campaigns are more directly responsible for conversions than direct marketing efforts, so this needs to be taken into consideration when assessing marketing data.

In-Market Bias

In some cases, customers are already in the market and ready to make a purchase before they even see a particular ad or become exposed to a marketing channel. When this occurs, traditional marketing attribution models will not accurately reflect the customer journey.

Missing Message Signal

Likewise, some marketing attribution models are too general to see the connections between brand messaging and the target customer. For example, just because one marketing message may not have effectively converted a large audience doesn't necessarily mean that message couldn't be useful in targeting a small audience segment. For this reason, further assessment and analysis are always needed when attributing credit to different touchpoints in the customer journey.

Digital Signal Bias

Finally, in digital signal bias, marketing teams make the mistake of only looking at online activity and online sales. While it's true that many businesses focus the majority of their marketing dollars on digital marketing and online sales, the reality is that all marketing efforts can influence both online and offline sales.

With this in mind, marketing teams are encouraged to look closely at all marketing and sales data when carrying out attribution modeling. This way, they can paint the most accurate picture and better understand the customer journey.

Advancing Your Expertise in Marketing Analytics

The world of marketing attribution models can be complex, especially as new trends and innovations continue to change the marketing landscape as we know it. With the right education, training, and experience, marketing professionals can learn how to leverage these models to optimize their campaigns and deliver tangible results.

Looking to advance your own marketing expertise? It may be time to explore a Master's in Marketing Analytics from Champlain College Online. This program, which consists of just 10 total courses, is designed to be completed in as little as 10 months with flexible scheduling and a career-focused curriculum. Reach out to learn more or get the ball rolling with your online application for admission.