How Paul Daniel and Fusable combined its Omeda + GA data to drive more ABM revenue

Note: Fusable’s engagement-driven ABM campaign won the Best Revenue Idea award at our 2024 Omeda Idea Exchange conference (learn more about the rest of our award winning campaigns here!). This case study was adapted from their presentation at OX. Prefer video? Scroll down to watch Fusable’s presentation at OX7!

Audience = revenue. Nobody has proved it like Fusable, who used its Omeda first-party audience data and reader engagement reporting to sell a major ABM campaign to one of the biggest aftermarket companies in the world. And lucky us — they’re telling us how they did it.

In 2022, Paul Daniel, VP of Audience at Fusable (then known as Randall Reilly), knew that he needed to go beyond basic aggregate data to see how each individual was responding to their content. They were getting traffic. But they weren’t always driving deeper, repeat engagement — or engaging the core audience that their clients wanted to reach.

“One of the big questions that our editors and sales team had was, ‘Who is our audience? Really?’ We had a lot of different ways of looking at that, but being able to get down to the user level, that behavioral level, was challenging depending on which kind of platform you were looking at it through.”

So they created their reader engagement model, which gives them a richer picture of each individual audience member’s engagement across its entire site. They achieve this by combining user-level behavioral and performance insights from their Omeda CDP and Google Analytics with their internal firmographic and demographic data. (Get a step-by-step breakdown of Fusable’s reader engagement model here.)

“We were able to intertwine it and surface it to our editorial teams and other stakeholders in the organizations to talk about exactly who our readers were, how much time they spent with our brands, what articles they read, what editors they preferred, all the types of things that helped us get a better sense of who our engaged core audience really was,” Daniel says.

The model saw quick results: In the first year, the model helped Fusable grew their email audience by more than 10% across all of its brands.

Using reader engagement to drive ABM

Coming off that success, the Fusable team knew their reader engagement model could drive revenue as well. But to achieve that, they needed a way to showcase the value of their behaviorally-driven audiences to their clients.

“Audience is revenue,” Daniel says. “And there’s a lot of ways for you to make revenue from your audience data, but what we really wanted to be able to do was do it in a more active way, not just the passive ways of people coming to your site and you getting ad impression revenue or those types of things. How can you really bring these to an offering that you can sell to a client and make money?”

Below, discover how Fusable used its audience data to create stronger audience profiles, pursue best-fit ABM opportunities and drive critical revenue for their business.

Strategy

The Fusable team focused their efforts on a global trucking aftermarket corporation, which we’ll name Client X for the sake of clarity.

Using their reader engagement process, the Fusable team showed Client X exactly who had read their sponsored content on one of their trucking media sites, along with what topics their key accounts were reading about sitewide.

“The first thing that we did was we took the articles in the site sections that were most pertinent to them, that were the highest performing, and we illuminated where are those most relevant to this brand, what’s relevant and what is noise,” Daniel says.

From there, they gave Client X a long list of companies that were consuming that content. Once client picked their best-fit target companies, Fusable went back to their reader engagement model to get behavioral data about individual decision makers at those accounts.

They told Client X what those decision makers were reading, how they’re interacting with Fusable’s brands on a day-to-day basis. Armed with that intel, the Fusable team could infer what they care about most.

“From there, this was a really interesting evolution in our discussion because it spawned them asking us whether we possibly had information on one particular decision maker at one particular account that they were extremely interested in reaching, so we created this and that is that person anonymized to protect the innocent, but nonetheless, essentially a profile of everything that they had recently read.”

The Fusable team paired that analysis with a set of ABM recommendations specifically derived from what their Omeda and Google Analytics insights told them about their target(s).

With this data, Fusable could create a single, cohesive story about their client’s ideal customer — one that combined insights and known pain points with empirical proof in user behavior. This helped them develop content and provide recommendations specifically targeted at those pain points.

Results

With such detailed engagement data, Fusable removed the guesswork from their ABM strategy and surfaced best-fit targets more quickly. As a result, Client X spent 15% more on new digital spend.

Numbers aside, Fusable’s reader engagement model has helped them shift audience development from a cost center to a value driver.

“When you can say, ‘I’m telling you what I understand about your readership, so let’s work together to make content that doesn’t feel like advertising, but content that is truly useful,’” Daniel says, “It helps your people, it helps your target accounts, it helps your decision makers solve a problem and it positions your brand in that way.”

Besides earning sales, Fusable’s reader engagement model has helped them grow, engage and activate their audience across every channel.

“We’re now able to take this process, which used to be email focused and also extend it to all forms of website visitation, and it is used by our editorial team and our audience team regularly, so it’s very, very useful to us in a lot of different ways,” Daniel says.

Some other highlights include:

Fusable saw double digit growth yearly growth in unique monthly email readership
Their editorial team used performance data from its reader engagement model to create more credible, specific content and develop better products for individual personas within their audience.
Their audience team has also begun adjusting its acquisition efforts to fill gaps revealed by the data.

Takeaways

Team work makes the dream work: This wasn’t a plug-and-play effort, Daniel says. It took extensive setup and cross-functional collaboration.

“It took a really strong partnership with our editorial team, which we are fortunate to have because we meet with them on a monthly basis to plan out all of our promotions. We also have shared KPIs, so there’s mutual accountability for working collaboratively together,” Daniel says.

However, teams can scale the model up or down depending on their bandwidth.

“You can take the underlying methodology that we used all the way down to a single URL, so you can simply look at how a specific article was performing. A salesperson could do that to get a general sense of how a piece of sponsored content went, so they’ll have a little bit better insight into the audience when they go to generate a quote. Or you could look at it, as we did, all the way up with GA level data across your entire readership base for a particular brand,” Daniel says.

“You can target it to what you have time and inclination for. I think this use case is best for your big accounts, but that’s why we tried it here because we thought there would be a receptive audience for that.”

Use your first-party data to segment and personalize down to the user level. None of this would be possible without the first-party data Fusable can generate and activate through Omeda and Google Analytics, Daniel says.

“There are so many winds blowing in our industry that we cannot control. What’s happening with cookie deprecation? How many core updates is Google and release in a year? Generative search, declining social referral traffic… It’s still there. First-party data is one place where you have control. This is where you have power.”

You shouldn’t totally abandon your broad, traffic-generating efforts, he says. But with first-party data, you can engage the individuals within your audience more easily and effectively. From there, you can drive repeat engagement and reduce your reliance on outside platforms and traffic plays.

So how can you get started? If you’re an Omeda user, most of the information you need is in the Field Count Report, Daniel adds. From there, you can scale or customize the model to meet your needs. “You can add custom fields to your customer records for the attributes that you care about the most,” he says, “so that when you pull down this information that’s coming with it, either way, there’s a way to make it work for you.”

Prefer video?

Watch Paul’s presentation at OX7 below.