Why traditional B2B marketing attribution is broken and what you can do about it

Tell me B2B marketing attribution reporting is broken without actually telling me B2B marketing attribution reporting is broken…

This is one of many quote requests where the message that came across Session Media didn’t align with the attribution source concluded by either Google Analytics or HubSpot. 

And it’s not just me—I’ve spoken to many marketers who find that their data doesn’t mirror how the user actually found out about them. The struggle is real—even for SaaS brands like Cognism—dealing with many inbound inquiries.

So let’s talk about the issues of traditional digital B2B tracking and how you can use Supermetrics to help optimize your demand generation efforts on our favorite B2B social channel—LinkedIn.

But first, what is B2B attribution?

What are the B2B attribution models

Before we get deeper into the issues behind B2B attribution, let’s recap on the different types of attribution models we use to report marketing success.

Last click

Last click attribution refers to a web analytics model where the ‘last click’ is given credit for a conversion or a sale. This is the default attribution model for Google Analytics and most CRM data.

Time decay

Time decay attribution is a multi-touch attribution model which gives each ‘touchpoint’ credit but weights it more towards the last-touch point.

Linear

Linear attribution distributes the credit for the conversion equally across all interactions.

Position-based

The position-based attribution model gives credit to all touchpoints along the conversion journey but assigns the majority toward the first and last click. 

First Click

First click attribution is an attribution model that assigns 100% of the credit for a lead or conversion to the first channel that a user clicked through.

The problem with attribution models

In theory, these models make a lot of sense and should answer every question we have about how users engage with our brand.

This is how most analytics software like Google Analytics and CRM Systems like HubSpot and Salesforce are set up by default—reinforcing the attribution model’s credibility.

It’s nearly perfect.

There’s just one main issue that pokes a hole in what should be a perfect attribution system. 

It’s not how B2B buyers buy.

Every now and then, we get a tracked user journey like the one above, restoring trust in multi-touchpoint tracking.

While this can offer some great insight into what content is working, the problem lies when looking at this data in isolation to make informed decisions on how to scale your marketing budget.

For example, if you’re running a video ad on LinkedIn, the user may not always click your ad, but they may have watched the full 4 minutes of content. Later they come to your website and fill out a high-intent form, and they’re categorized as a ‘Direct’ conversion.

From what we’ve seen and tested, this is a typical buyer’s journey. But as it doesn’t fall into the default last-attribution reports as ‘LinkedIn’, the data and marketing approach falls by the wayside.

Worse still, the non-trackable activity that’s driving results is replaced by trackable activity which doesn’t. For example, the video being replaced by static image ads which drive the user to a ‘Get Demo’ ad for the sole reason ROI is easier to track.

In an information-rich world, B2B conversions on a first touchpoint are getting rarer.

The majority of activity that warrants driving users to your website—to read blogs, case studies, podcasts—will rarely be the source of direct-response leads. Meaning they will rarely receive attribution for a sale.

Once more, a lot of your target audience won’t be the ones who actually get in contact. Instead, they forward the information to someone else who’ll fill out the ‘Get Quote’ or ‘Get Demo’ form.  

This is known as dark social or dark funnel.

Dark social involves all the untracked activity that happens on platforms like Slack, WhatsApp, DMs, which aren’t as easily traced.

A recent example of this in action is our Head of PPC, Alex, saw multiple LinkedIn Ads for a new piece of IP blocking software, which he thought would help with the day-to-day running of our PPC Agency. He sent me the name of the brand via Slack, and the same day we had a paid license.

The funny thing is the LinkedIn ad he saw wouldn’t have gotten credit for this revenue. It would’ve fallen under ‘Organic Search’—as that’s how I searched for the brand and paid for the software.

How to attribute to the dark funnel

There are a few ways you can fix this. The simplest way is by taking marketing back to basics and actually asking the users how they heard about your company. Here’s an example of our mandatory field.

If you leave it as an open text field, you’ll be surprised at how much information the user gives you on how they heard about your brand.

You can then set up some automated rules in your CMS that update the attribution with keywords users have used in that field.

LinkedIn Ads to Google Data Studio

Now to the good bit.

By using the Supermetrics integration with Google Data Studio, we’ve developed a method of reporting the wider impact LinkedIn Ads have on the bottom line.

Check out how we do it in this step-by-step video.

Wrap-up

LinkedIn Ads isn’t an ‘intent’ channel, so you shouldn’t be running high intent ads. Instead, you can use their hyper-specific targeting to deliver content that resonates, and then see which businesses come through as ‘Direct’ or ‘Organic Search’ traffic who have seen your ads. And use this data to scale.

About the author

Ben Brown is the Founder of Session Media, a SaaS Marketing Agency. Living off burritos & his Pret-à-Manger coffee card, Ben loves helping B2B SaaS companies create strategies that drive business growth—almost as much as he likes referring to himself in the 3rd person.