figmaJul 23, 2023

The anatomy of launching a Figma open beta | Figma Blog (opens in new tab)

Launching a product is only the beginning of its go-to-market process. Figma used the two weeks after Dev Mode’s open beta launch as a coordinated research period to measure adoption, understand user sentiment, identify bugs, and prioritize improvements. The central lesson is to define success metrics and responsibilities before launch so teams can quickly turn feedback into product decisions. ## Post-launch work requires cross-functional ownership - Product, marketing, and support shared responsibility for evaluating Dev Mode. - The product team tracked feature usage. - Marketing monitored public sentiment and audience reach. - Support focused on bugs, requests, and potential improvements. - Product manager Avantika Gomes synthesized information from all sources, prioritized actions, coordinated follow-up, and guided iteration. - Constant communication ensured that important social posts and recurring user issues reached the broader team. ## Dev Mode as an open-beta test - Dev Mode is a Figma workspace designed specifically for developers. - It functions similarly to a browser inspector for design files, letting developers collect measurements, specifications, assets, and other information directly from the canvas. - The open beta provided free access to all Figma users through the end of 2023. - Figma used the beta to test whether Dev Mode addressed developer needs and to discover how it could be improved. ## Define metrics before launch - Feedback from developers and designers during the closed beta helped Figma refine core workflows, collaboration features, and newly identified needs. - The team selected a north star metric: the percentage of weekly active users in developer roles who used Dev Mode. - They also established benchmarks before launch, making it easier to judge whether results were strong or weak. - Prebuilt dashboards allowed the team to compare activity before and after the open-beta release. ## Measure adoption, reach, and user reaction - Figma tracked usage of individual features, including: - The Inspect panel - Compare changes - Related links - These measurements showed which capabilities were being adopted and which were most compelling. - To evaluate reach, the team monitored: - Social media impressions - Email open rates - In-product message impressions - Social sentiment and live audience reactions during Config helped reveal which features generated enthusiasm. ## Track problems through multiple feedback channels - Figma monitored Help Center article views to identify areas where users needed clarification. - In-product feedback submissions and support tickets provided direct reports of bugs and improvement requests. - Combining behavioral metrics with qualitative feedback helped the team understand not only what users did, but also where they struggled. The practical recommendation is to treat launch as the start of an intensive learning cycle: assign clear owners, establish benchmarks and dashboards in advance, and combine product analytics, public sentiment, and support data to guide rapid improvements.

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figmaOct 12, 2022

How we use data | Figma Blog (opens in new tab)

Figma uses data in two broad ways: functional data to provide its service and analytics data to improve the product. The company emphasizes collecting only the information necessary to operate Figma, while using aggregated usage insights and experiments to guide product decisions. Examples include improving file sharing and identifying performance problems in the iOS app. ## Functional Data - Functional data supports core account and product operations. - Figma collects a relatively small amount of information at signup: - Email address - Name - Role - This information enables usernames, password-reset messages, file creation, and collaboration. - Figma generally does not require sensitive information such as identity documents or verification. - Payment information for paid plans is collected and processed by Stripe. ## Analytics Data - Analytics data describes how users access and use Figma. - It helps teams understand: - Which features users adopt - Which features they ignore - Where users encounter difficulties - How usage varies across platforms - Data scientists analyze these signals alongside user research, product intuition, and direct feedback. ## Improving Features Through Experiments - A/B testing is a central part of Figma’s product-development process. - Experiments test hypotheses and measure how proposed changes affect user behavior. - In one study, Figma examined its file-sharing modal, where users invite collaborators, manage permissions, and publish work. - Research showed that: - Only 20% of users opened the share modal during their first month. - Only half of those users successfully shared a file. - Figma simplified the interface and moved secondary functions into separate tabs. - The change produced: - A 2% increase in users sending invitations - A 2% increase in users invited to each file - No observed decline in users publishing work to Figma Community ## Identifying Performance Issues - Figma uses data to monitor application performance across platforms and prioritize improvements. - After launching the beta iOS app, the data team analyzed crashes by platform, scenario, and timing. - The analysis found that prototypes were a major source of iOS crashes. - About 25% of prototype crashes occurred within the first 10 seconds of loading. Figma’s approach combines minimal functional data collection with analytics, experimentation, and performance monitoring. The practical goal is to use data selectively to improve usability, reliability, and collaboration while limiting the amount of sensitive information required from users.

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figmaJan 19, 2022

From experiment to launch: how data shaped a new comments experience | Figma Blog (opens in new tab)

Figma used a series of data-driven experiments to redesign its comments experience and encourage collaboration among both editors and viewers. Although comments strongly predicted team retention and growth, they were underused because users struggled to discover them. Experiments confirmed that improving visibility increased comment creation, while an intuitive-looking relocation of the comments control unexpectedly reduced discoverability. ## Collaboration and Comments - Teams that collaborate during their first month are: - 1.75× more likely to be retained. - 6.5× more likely to become customers. - Figma identified comments as a potential “front door” to collaboration because both editors and viewers can use them. - Despite their value, comments were not widely used, prompting the data science team to investigate the gap between user needs and behavior. ## Testing Comment Discoverability - Users previously entered comment mode through an icon in the upper-left corner of the editor. - Research sessions suggested that comments were valuable but difficult to find. - Figma tested a prompt encouraging view-only developers to leave comments. - After two weeks in a 50/50 experiment: - Comment creation increased by 45% in the test group. - The rate of users returning to comments the following week did not change. - The result showed that simply making comments more visible could substantially increase usage. ## An Unexpected Result - Figma hypothesized that moving the comments entry point from the left side of the menu bar to the right would improve discoverability. - The reasoning was that the left side emphasized creation tools, while the right side contained collaboration and viewing features more relevant to cross-functional users. - Among new users, the change caused a 20% decrease in comment discovery within seven days of signup. - The failed experiment demonstrated that seemingly logical product changes can significantly harm user behavior. - It also reinforced the value of controlled experiments: product hypotheses are often wrong, and teams should expect testing to disprove many ideas. Figma’s experience illustrates that effective product development depends on combining user research with rigorous experimentation. Rather than relying on intuition about interface placement, teams should validate each change with real behavioral data and use unexpected results to guide subsequent iterations.

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