How Loom won attention from top accounts with the perfect ABM campaign

Some meetings could be emails, some could be instant messages. But Loom introduced a third option where some meetings could be screen recorded videos. 

Loom is an async video platform that gives folks the ability to quickly share thoughts or ideas by recording their screen, their camera, or both, and sharing the final output with a linked video. It can be used by everyone from engineers to HR. With an audience that broad, they have to be pretty thoughtful and precise about their messaging and targeting, to create value for the right consumer. 

This week, Case Studied examines how Loom caught the attention of their top accounts with an ABM campaign on LinkedIn that drove incredible results. 

The Brief:

Account density was a key part of Loom’s paid media and go-to-market strategy. It was important to increase the number of users within specific accounts because as that number grows, the likelihood of enterprise deals grows and so does MRR (monthly recurring revenue).

Loom had a list of target accounts where they were hoping to grow awareness and usage. Based on that list, they turned to ABM aka account based marketing, a strategy deployed by marketing teams looking to target very specific B2B accounts, often times enterprise ones.

With this approach, campaigns are then personalized to specific target accounts. Any content that’s sent to those folks gets tailored based on whatever information the marketers have about them: company, demographics, location, etc. 

After being initially fueled by the push from mass marketing to more personalized marketing, there’s now a growing number of vendors offering ABM solutions, specifically for B2B. And it makes sense why: Adobe found that 76% of B2B marketers said ABM generates a greater ROI than any other marketing strategy. 

With all that in mind, Loom decided to test whether ABM could work for them and their goals. Despite having a very horizontal product with a broad audience of knowledge workers, they narrowed down their focus to one channel where most folks on their target accounts were: LinkedIn. 

The Execution:

LinkedIn advertising ain’t cheap, there CPMs typically cost anywhere from 5-10x that of other social networks. To make sure they got the most bang for their buck, Loom used third party data from Metadata to personalize their ads to each target account. They imported their target account info into both LinkedIn and Metadata to ensure the ads went to the right people at the right companies. 

From there, Loom’s Head of Growth Marketing, Nick Lafferty, said he would manually create the ads using templates in Figma that the brand team made. Lafferty would import company logos himself and add language calling out each company specifically in the ad copy. 

The ads followed this format: a caption that read, “Hey [company name], you free for a quick sync?” with a graphic that said, “Join [x number] of your teammates at [company logo] using Loom to reduce meetings by 29%.” 

With each ad, Lafferty would manually swap out the company name, logo, and number of current users. But all that manual work and personalization certainly paid off. 

And if you’re eager to go even more in depth, there is a great 30-minute recording where Nick walks through their strategy:

The Results:

Lafferty said he typically sees a 0.4 - 0.5% click-through-rate (CTR). 

“I’ve seen as high as 0.8 - 1.0% for super broad, targeted ads on LinkedIn with the right messaging, the right creative, the right offer. I’ve never seen higher than 1% for click through rate on any of those types of ads,” said Lafferty.  

Loom’s LinkedIn ABM campaign numbers were nowhere near that. In a screenshare Loom video of the LinkedIn campaign manager page, the lowest CTR seen is 2.19%, with the highest being 3.68%. 

According to Metadata, Loom’s average cost per click was $4-10 and the average CTR was between 2.0 - 3.5%. For context, the average CTR for LinkedIn in general is 0.65%. 

Following such a successful test run of ABM, Lafferty said Loom is looking to take the personalization of their ads into the onboarding experience. The question is, “How can we give these users the white glove treatment so it’s not just personalization of the ads… How can we really treat these people in a unique and personalized way, and take this experience all the way through their first time using—and hopefully recording—with Loom.” 

The Takeaways:

There’s a lot to learn from Loom’s ABM campaign. Here are a few lessons you can take away to your next campaign:

1. Personalization still works

Personalization has always outperformed mass advertising, the challenge has been doing it at scale, as the operations necessary make it difficult. With ad platforms such as Meta encouraging advertisers to go broad and let their AI-based algorithms target the ad, it feels as though personalization has almost taken a back seat.

That is why the Loom case study is so refreshing, as it’s a great reminder that despite the extra effort needed to execute a campaign like this, there is still a lot of benefit to personalizing campaigns specifically in B2B.

2. Data partners exist for a reason (but they're not cheap!)

One of the more interesting parts of this case study is the reliance on 3rd party data from Metadata to enhance the ads Loom ran. As any marketer can tell you, there is an increasingly long line of data providers waiting to sell you a subscription to their platforms, but they will cost you a pretty penny.

But that’s not to say they don’t work. In Loom’s case, a campaign like this is unfortunately not possible without enriched data on their target accounts and it obviously paid off as the data quality led to huge increase in CTRs.

3. Consider your blended costs

When reviewing a campaign like this, it’s easy to just consider the media costs, divide it by your number of leads and then get a cost per lead. The problem with this is it fails to consider the other costs required to run a campaign like Loom’s.

Make sure that when conducting your campaign post-mortem, you factor in costs like data subscriptions, the time it takes to execute the personalization and any offers or promotions you are providing to discount the product.

This will enable you to run a more accurate comparison of campaign results to others you’ve conducted in the past.