Every day, scientists around the world find their inboxes full of research papers, conference invitations, funding updates, and institutional announcements. Getting them to stop and actually read one of those messages (let alone engage with it) requires clearing a high bar.
Labroots knows this better than most. The California-based platform has spent 13 years connecting a global community of researchers, scholars, and science professionals with the latest discoveries across dozens of disciplines. Through scientific news, daily webinars, and large virtual events, Labroots gives scientists a place to hear directly from the researchers behind the work that moves their fields forward.
Rebecca Anaya, Labroots' Senior Marketing Manager, has been running experiments to figure out how to effectively reach that audience ever since she joined the team in 2021."I enjoy the science of it," Rebecca says. "You actually get something to compare as data. It's super useful."
With Intuit Mailchimp as her lab, Rebecca has helped Labroots move from sending emails with little intention behind them to communicating with precision. Now, their global audience of scientists stay engaged without feeling like they're being bombarded.
The challenge: Too many emails and not enough connection
Labroots runs webinars every single day, each with its own set of reminder emails. With a list of more than 429,000 contacts spanning research disciplines, institutions, and geographies, outgoing email volume adds up fast.
Scientists’ inboxes are already full of noise. Send too much, or send generic reminders, and Labroots risks becoming part of the problem.
"We’ve all become numb from the constant gratification we're getting from our apps, our phones," Rebecca says. "We have had to get a little more crafty with the way we formulate these emails, just to capture attention."
The team had instincts about what worked—short subject lines, clear calls to action, concise body copy. But instincts aren't data. Without a way to test assumptions at scale, they couldn’t know what was actually working or whether the whole approach needed to evolve.
