Now more than ever, marketing and sales leaders are taking a critical look at where to allocate their resources and how to staff their teams.

Attribution modeling is one of the best tools for providing clear guidance on what’s working, and what isn’t.

What Is Marketing Attribution?

Marketing attribution is the approach to understanding how various marketing and sales touchpoints influence the prospects’ move from visitor, to lead, to customer.

By implementing attribution in your organization, you’ll have a better idea of:

• Which channels are most influential during different phases of the sales cycle.
• Which content formats are more or less impactful in your marketing or sales enablement efforts.
• Which campaigns drove the most revenue and return on investment (ROI).
• The most common sequence of online or offline events that prospects interact with before becoming a customer.

Why Is Attribution Important In Marketing?

Analyzing attribution data provides you with an understanding of which marketing, sales, and customer success efforts are contributing most effectively and efficiently toward revenue generation.

Attribution modeling helps you identify opportunities for growth and improvement, while also informing budget allocation decisions.

With accurate attribution models, marketers are able to make more informed decisions about their campaigns, which has allowed them to increase ROI and reduce wasted budgets on ineffective strategies.

What Are The Challenges Of Marketing Attribution?

Developing a perfect attribution model that guides all of your decisions is a pipedream for most marketers.

Here are five challenges that result in inconclusive data models or total project abandonment:

Cross-Channel Management

This is a common challenge for enterprise marketers who have web assets across multiple websites, channels, and teams.

Without proper analytics tagging and system settings configuration, your web activities may not be tracked accurately as a visitor goes from one campaign micro-site to the main domain.

Or, the prospect may not be tracked as they go from your website to get directions to then go to your physical storefront to transact.

Making Decisions Based On Small Sample Sizes

For smaller trafficked websites, marketers using attribution data may not have statistically significant data sets to draw accurate correlations for future campaigns.

This results in faulty assumptions and the inability to repeat prior success.

Lack Of Tracking Compliance

If your attribution models rely on offline activities, then you may require manual imports of data or proper logging of sales activities.

From my experience in overseeing hundreds of CRM implementations, there is always some level of non-compliance in logging activities (like calls, meetings, or emails). This leads to skewed attribution models.

Mo‘ models, mo’ problems: Each analytics platform has a set of five or more attribution models you can use to optimize your campaigns around.

Without a clear understanding of the pros and cons of each model, the person building the attribution reporting may not be structuring or configuring them to align with your organizational goals.

Data Privacy

Since GDPR, CCPA, and other privacy laws were enacted, analytics data continues to get murkier each year.

For organizations that rely on web visitors to opt-in to tracking, attribution modeling suffers due to the inability to pull in tracking for every touchpoint.

How Do You Measure Marketing Attribution?

Measuring attribution is all about giving credit where it is due. There are dozens of attribution tools out there to assign credit to the digital or offline touchpoint.

Attribution measurement starts with choosing the data model that aligns with your business goals.

Certain attribution models favor interactions earlier on in the customer journey whereas others give the most credit towards interactions closer to a transaction.

Here is a scenario of how to measure marketing attribution in a first-touch attribution model (we’ll get to the different models next):

A prospect comes to the website through a paid search ad and reads the blog.

Two days later, she comes back to the site and views a couple of product pages.

Three days later, she comes back through an organic listing from Google and then converts on the site by signing up for a discount coupon.

With a first-touch attribution model, the paid search ad will get 100% of the credit for that conversion.

As you can see, choosing the “right” model can be a contentious issue, as each model gives a percentage of credit to a specific interaction or placement along the path toward becoming a customer.

If your business relies on paid search, SEO, offline, and other channels, then likely one of the individuals working on one of those channels is going to look like the superhero, whereas the other marketers will look like they aren’t pulling their weight.

Ideally, when you are choosing an attribution tool, you’ll be able to build reports that allow you to compare various attribution models, so you have a better understanding of which channels and interactions are most influential during certain time periods leading up to conversion or purchase.

What Are Different Marketing Attribution Models?

Marketers can use various marketing attribution models to examine the effectiveness of their campaigns.

Each attribution tool has will have a handful of models you can optimize campaigns and build reports around. Here is a description of each model:

First-Click Attribution

This model gives credit to the first channel that the customer interacted with.

This model is popular to use when optimizing for brand awareness and top-of-funnel conversions/engagement.

Last-Click Attribution

This model gives all of the credit to the last channel that the customer interacts with.

This model is useful when looking to understand which channels/interactions were most influential immediately before converting/purchasing.

Last-click attribution is the default attribution model for Google Analytics.

Multi-Touch/Channel Attribution

This model gives credit to all of the channels or touchpoints that the customer interacted with throughout their journey.

This model is used when you are looking to give weight evenly or to specific interactions.

There are variations of the multi-touch model including time-decay, linear, U-shaped, W-shaped, and J-shaped.

Customized

This model allows you to manually set the weight for individual channels or placements within the customer journey.

This model is best for organizations that have experience in using attribution modeling, and have clear goals for what touchpoints are most impactful in the buyers’ journey.

Marketing Attribution Tools

There are several different tools available to help marketers measure and analyze marketing attribution. Some attribution tools are features within marketing automation platforms or CRM systems like Active Campaign or HubSpot.

Others are stand-alone attribution tools that rely on API or integrations to pull in and analyze data, like Triple Whale or Dreamdata.

As you are evaluating tools, consider how much offline or sales data needs to be included within your attribution models.

For systems like HubSpot, you can include sales activities (like phone calls and 1:1 sales emails) and offline list import data (from tradeshows).

Other tools, like Google Analytics, are not natively built to pull in that kind of data and would require advanced development work to include these activities as part of your model.

(Full disclosure: I work with HubSpot’s highest-rated partner agency, SmartBug Media.)

Additionally, if you need to be able to see the very specific touchpoints (like a specific email sent or an ad clicked), then you need a full-funnel attribution system that shows this level of granularity.

Attribution modeling is a powerful tool that marketers can use to measure the success of their campaigns, optimize online/offline channels, and improve customer interactions.

It is important, though, to understand attribution’s limitations, the pros and cons of each model, and the challenges with extracting conclusive data before investing large budgets towards attribution technology.

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Featured Image: Yuriy K/Shutterstock