How Figma’s data science and user research teams weave together insights that count | Figma Blog (opens in new tab)
Figma’s data science and user research teams combined quantitative and qualitative methods to understand why notifications were not driving enough collaboration. Data revealed where users dropped out of the notification funnel, while user research explored the reasons behind those behaviors. Their synthesis showed that the biggest issue was not interaction with notifications, but that many users were never receiving them, leading to new alert types and changes to notification recipients and timing. ## Building a Cross-Functional Process - Data Scientist Caitlin Hudon and Researcher Jennifer Sanders began with a teamwide FigJam brainstorm. - They reviewed notification funnel metrics, including the percentages of users who: - Were eligible for notifications - Received them - Viewed them - Interacted with them - The team identified strategic questions, potential notification types, and data gaps. - Questions were divided according to whether data science or user research was best suited to answer them. - Caitlin and Jennifer maintained close communication while conducting their separate investigations, treating quantitative and qualitative work as complementary rather than independent. ## Understanding the Notification Funnel - Figma notifications can arrive through email, Slack, mobile devices, the file browser, the system tray, or the desktop app. - Notification events include comments, replies, reactions, invitations, editing access, and @mentions. - Users must move through several stages before engaging: - All Figma users - Users eligible for notifications - Users who receive notifications - Users who view them - Users who interact with them - Quantitative analysis showed what users were doing at scale. - User research was needed to understand why users behaved that way, since people may not always accurately explain their own motivations. ## Finding the Biggest Opportunity - The activity team initially needed to determine which stage of the funnel deserved attention. - Cross-functional analysis revealed that most users were not receiving notifications at all. - This shifted the focus away from simply improving notification engagement. - The main opportunities were to create new alert types and reconsider who should receive notifications and when. ## From Insights to Impact - Figma’s activity team began running notification experiments based on the combined findings. - A new notification type was released to address major user pain points. - The broader goal is to help teams stay connected and collaborate effectively across Figma and FigJam. The case demonstrates that product teams get stronger, more actionable conclusions when behavioral data is paired with direct user research.