Companies that use Statsig

Source: Analysis of job postings that mention Statsig (using the Bloomberry Jobs API)

My analysis shows that Statsig buyers are predominantly Product and Data leaders. Senior Product Managers (18%) and Directors of Data Analytics (8%) lead purchasing decisions, typically within Growth, Product Analytics, and Experimentation domains. These leaders prioritize building a culture of experimentation, scaling product development velocity, and establishing rigorous statistical frameworks for decision-making across their organizations.

Day-to-day users span product, engineering, and data teams. Senior Data Scientists (15%) and Senior Product Analysts (12%) use Statsig to design A/B tests, analyze experiment results, and measure product performance metrics. Growth Engineers (10%) integrate Statsig into product funnels to optimize conversion, activation, and retention. I noticed heavy emphasis on self-service analytics capabilities, suggesting teams want to democratize experimentation without constant data science bottlenecks.

The core pain point across postings centers on experiment velocity and rigor. Companies seek to build a culture of experimentation, run rapid, trustworthy online experiments, and replace guesswork with data-driven certainty. One posting explicitly wants someone to define experimentation best practices and statistical methodologies for creating, monitoring, and learning from experiments. Another emphasizes driving the velocity, rigor and culture of experimentation while delivering trustworthy insights. These companies recognize that systematic experimentation infrastructure is critical for product-led growth and want platforms that combine speed with statistical soundness.

Source: Analysis of Linkedin bios of 449 companies that use Statsig

I noticed that Statsig customers are predominantly product-driven technology companies building consumer-facing platforms and applications. These aren't traditional enterprise software shops. They're companies like Instacart reinventing grocery delivery, Chime disrupting banking for everyday Americans, and Second Dinner creating mobile games. They build products where user experience directly impacts revenue, and where small changes in conversion, engagement, or retention create massive business outcomes. Many operate marketplaces (Provi for wholesale alcohol, buycycle for bikes, Respondent for research participants) or financial services with complex user journeys.

The stage distribution surprises me. While there are Series A and B companies, the portfolio skews toward later-stage and established players. I see multiple public companies (Capital One, GitHub, Autodesk), unicorns (Chime, Instacart), and companies with thousands of employees. Even "younger" companies often have hundreds of employees and Series C or D funding. These aren't Day 1 startups figuring out product-market fit. They're scaling companies optimizing growth or mature enterprises modernizing their tech stacks.

Source: Analysis of tech stacks from 449 companies that use Statsig

I noticed something striking about Statsig users: they're engineering-focused companies obsessed with internal efficiency and developer experience. The overwhelming presence of Golinks (882x more likely) immediately signals companies where engineers move fast and share knowledge constantly through memorable short links. Combined with DX (2053x more likely), which measures engineering productivity, these are organizations that treat their internal development process as seriously as their external product.

The pairing of Statsig with Glean makes perfect sense. Glean searches across all your company's apps and documents, which matters when you're running dozens of feature experiments and need to quickly surface past learnings. UserTesting appearing 1393x more likely suggests these companies don't just A/B test blindly. They combine Statsig's quantitative experimentation with qualitative user research to understand not just what users do, but why. Decagon AI's presence (2005x more likely) adds another layer: these companies are sophisticated enough to automate customer support with AI, indicating they've reached scale where automation becomes essential.

The full stack reveals product-led growth companies in their scale-up phase. They're past the scrappy startup stage but still engineering-driven rather than sales-led. Impact (1541x more likely), which manages affiliate and partnership marketing, suggests they've moved beyond simple acquisition channels and are optimizing complex performance marketing programs. These aren't enterprise sales organizations with long deal cycles. They're companies that ship features weekly, measure everything, and need their internal tools to keep pace with external velocity.