Who actually uses these products,
and what made them stay.

The Hex Collaborative Data Analyst

A data analyst or analytics engineer who uses Hex because it combines everything they used to do across 3–4 separate tools into one collaborative environment. They write SQL to pull data, Python to transform it, and build visualizations and dashboards — all in the same notebook. They share their work as interactive apps that stakeholders can explore without learning SQL. They've replaced Jupyter notebooks, Mode, and Google Sheets with Hex. They are the data person who makes data accessible to people who aren't data people.

“The marketing team asks: "Which campaigns drove the most pipeline last quarter?" The data analyst opens Hex, writes a SQL query to pull campaign data, joins it with pipeline data, and adds a Python cell to calculate attribution.”

The Hex Data Analyst

A data analyst or analytics engineer at a company with a modern data stack — dbt, Snowflake or BigQuery, and a growing demand from business stakeholders for self-service data access. They use Hex because Jupyter notebooks are hard to share and dashboards aren't flexible enough. Hex sits in the middle: code-first enough for real analysis, shareable enough that a PM can click through an interactive version without needing to run code. They build notebooks in Hex. Business people use the published apps. This is the workflow they've been trying to build for years.

“A teammate asked how they managed build analyses that colleagues can interact with without running code themselves.”

The PostHog Product Engineer

A product engineer or full-stack developer at a startup of 5–50 people who chose PostHog — or advocated for it — because they wanted product analytics that behave like engineering tools. They self-host or use PostHog Cloud. They instrument events themselves. They use feature flags as part of their development workflow. They are not a data analyst but they want to be able to answer product questions without filing a request to one.

“It happened mid-workflow — they've shipped a new onboarding flow behind a feature flag to 10% of users.”

The Amplitude Growth Analyst

A data analyst, growth analyst, or analytics engineer at a Series B–D company who owns Amplitude as the source of truth for product behavior. They are technical enough to write SQL but prefer not to for exploratory analysis. They've mastered the Amplitude chart types. They build dashboards that PMs and executives use but don't fully understand. They're the person in the room who says "let's look at the data" and then actually pulls it up.

“The head of product wants to know which activation milestone most predicts 30-day retention.”

The Mixpanel Product Manager

A product manager or growth lead at a B2C or B2B SaaS company for whom Mixpanel is the primary lens on user behavior. They are not a developer. They understand events and properties well enough to answer most of their questions self-service. They have a set of saved reports they look at every Monday. They also have questions that require a data analyst to answer — and they're slowly working to reduce that list.

“A teammate asked how they managed understand where users drop out of key flows and why.”

The Mixpanel Product Analyst

A product analyst or data analyst embedded in a product team who uses Mixpanel as their primary tool for understanding user behavior. They build funnels, analyze retention, and create the dashboards that PMs reference in every planning meeting. They know SQL but prefer Mixpanel's UI for speed. They've named every event in the tracking plan and written documentation for each one. They are the person the PM turns to and asks "are users actually using this feature?" — and they always have the answer.

“A teammate asked how they managed build funnels that accurately capture user journeys from signup to activation to retention.”

The FigJam Product Team Facilitator

A product manager, design lead, or team facilitator at a product company who uses FigJam for team whiteboarding because their team already lives in Figma. They chose FigJam over Miro because the context switch is lower — design references, wireframes, and working files can be linked or embedded directly from Figma. They run planning sessions, retrospectives, decision workshops, and design crits on FigJam. Their team knows how to use it. This matters more than they expected it to.

“The shift was quiet.”

The Maze Unmoderated Research Lead

A UX researcher or product designer at a company where research is valued but researcher time is scarce. They use Maze to run tests they can't run fast enough with moderated sessions. They design the test, connect the Figma prototype, send the link, and come back to results in 24–72 hours. They know unmoderated testing misses the nuance of moderated sessions. They also know that running 8 moderated sessions takes 2 weeks of scheduling and 2 days of synthesis. Maze takes 2 hours to set up and 1 hour to analyze. They're using the right tool for the question.

“A teammate asked how they managed get directional usability signal fast enough to influence a design decision.”