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

The Perplexity Research Analyst

A research analyst, journalist, consultant, or knowledge worker who has replaced their Google-and-10-tabs workflow with Perplexity. They don't search for links — they ask questions and expect synthesized answers with citations. They use it for competitive analysis, market research, fact-checking, and deep dives into topics where they need to learn fast. They've learned which types of questions Perplexity handles well (factual synthesis) and which it doesn't (opinion-based, very recent events). They trust it more than ChatGPT because of the citations, but they still verify.

“A teammate asked how they managed get comprehensive, cited answers to complex research questions in minutes instead of hours.”

The Excel Financial Modeler

A financial analyst, FP&A professional, or investment banker who builds financial models in Excel the way architects build buildings — with structure, precision, and the knowledge that if one formula is wrong, everything above it falls. They've been using Excel for 5–15 years. They think in cell references, not coordinates. They know keyboard shortcuts that most people don't know exist. They've built models that a CEO used to make a $50M decision, and they've spent weekends debugging a circular reference that shouldn't have been circular.

“The CFO asks for a scenario analysis by tomorrow morning: what happens to cash flow if revenue grows 10% slower than projected and two enterprise deals slip to next quarter.”

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 Google Analytics Marketing Analyst

A digital marketer, marketing analyst, or growth lead who uses Google Analytics as their primary source of truth for website performance. They lived in Universal Analytics for years — they knew where every report was, how sessions worked, and what their bounce rate meant. Then GA4 happened. The interface changed, the data model changed, sessions became events, and reports they relied on disappeared or moved. They're learning GA4 because they have to, not because they wanted to. They are adapting their expertise to a tool that feels like it was rebuilt for data engineers, not marketers.

“Not a single dramatic moment — more like a Tuesday at 3pm when they realized they hadn't thought about the GA4 interface is unintuitive — reports that took one click in UA now require custom explorations in two weeks.”

The Google Analytics Marketing Manager

A marketing manager or digital marketer at a company of 10–200 people who is responsible for understanding how the website is performing and why. They are not a data person. They've been through the GA4 migration and have not recovered emotionally. They know enough to navigate the interface but not enough to build custom reports without three tabs of documentation open. They check analytics several times a week and leave most sessions with more questions than answers.

“The VP of Marketing wants to know if the new landing page is performing better than the old one.”

The FullStory Digital Experience Analyst

A product analyst or UX researcher at a digital product company who uses FullStory as their lens into the user experience. They don't just look at funnels and conversion rates — they watch sessions, identify frustration signals (rage clicks, dead clicks, error clicks), and correlate behavioral patterns with business outcomes. They've learned to find the story in the data: why conversions dropped, where users get confused, what makes the checkout feel broken. They are the translator between raw user behavior and product decisions.

“The product team sees a 15% drop in checkout completion after a recent redesign.”

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 PostHog Growth Engineer

A growth engineer, product engineer, or technical PM who uses PostHog as their all-in-one growth stack — analytics, feature flags, A/B tests, session replay. They chose PostHog because they didn't want to stitch together Amplitude, LaunchDarkly, and Hotjar. They think in funnels, retention curves, and statistical significance. They are technical enough to self-serve but product-minded enough to care about the "so what" behind the data.

“It happened mid-workflow — the growth engineer is running an A/B test on the onboarding flow.”

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 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 Segment Data Architect

A data engineer or analytics engineer who manages Segment as the central event routing layer. Every product event — page views, clicks, purchases, signups — flows through their Segment workspace before reaching the data warehouse, analytics tools, and marketing platforms. They are the plumber of the data stack. Nobody thanks them when data flows correctly, but everyone notices when it doesn't. They think in events, properties, and destinations. They've learned that the hardest part of data infrastructure isn't moving data — it's keeping it clean.

“The shift was quiet.”

The Excel Financial Analyst

A financial analyst at a corporation, investment firm, or consultancy for whom Excel is not software — it is the medium of thought. They spend 5–7 hours a day inside spreadsheets. They build models that other people are afraid to open. They have keyboard shortcuts memorized that the rest of the company doesn't know exist. They've rescued a broken model the night before a board presentation. They have opinions about Excel that they share without being asked.

“It happened mid-workflow — they're building a 3-year P&L model for a new product line.”

The Pendo Product Manager

A product manager at a B2B SaaS company who uses Pendo as both their analytics platform and their in-app communication tool. They track feature adoption, build onboarding guides, run NPS surveys, and analyze user paths — all without filing engineering tickets. They appreciate that Pendo lets them own the user communication layer. They've become the person who says "let's add a guide for that" whenever a feature has low adoption, and they're starting to wonder if they've created guide fatigue.

“It happened mid-workflow — the PM launches a new dashboard feature.”

The FullStory Behavioral Analytics PM

A senior product manager, digital experience lead, or data-savvy UX researcher at a company of 200–5,000 people where FullStory was purchased as a platform — not a point tool. They use it to answer questions that neither analytics dashboards nor individual session recordings can answer alone: what does the full behavioral pattern look like for users who churn? Where in the enterprise checkout flow do users consistently struggle? Which UI elements are generating frustration signals at scale? They work with data. They also watch sessions. Both inform the decision.

“The shift was quiet.”

The Pendo Product Manager

A product manager at a B2B SaaS company who owns feature adoption and in-app user education. They have engineering bandwidth for product, not for tooltips. Pendo lets them publish in-app guides without a ticket. They've also realized that Pendo's analytics tell them something different from their product analytics tool — not better, different. Pendo tells them where users are, not just what they do.

“A major new feature shipped three weeks ago.”

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 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 Hotjar Session Watcher

A product manager, growth marketer, or UX designer at a company of 20–500 people who uses Hotjar to answer questions that quantitative analytics can't. They know their funnel. They know where users drop off. What they don't know is why. Session recordings are how they find out. They've watched hundreds of sessions. They've seen users rage-click on things that aren't buttons, scroll past CTAs without seeing them, and get confused by flows the team thought were obvious. Each one of these is a design decision waiting to happen.

“The checkout conversion rate dropped 2.”

The Dovetail Research Operations Manager

A UX research lead or research operations manager at a product company who uses Dovetail to turn the chaos of qualitative research — interview transcripts, survey responses, usability test recordings — into a structured, searchable insights repository. They tag, code, and synthesize findings so that when a PM asks "what do we know about onboarding friction?" the answer is a link, not a 3-week research project. They are the librarian of user insights, and they've learned that research nobody can find is research that didn't happen.

“A teammate asked how they managed tag and code qualitative data (transcripts, notes, videos) with consistent taxonomy.”

The Hotjar UX Researcher

A UX researcher, product designer, or growth PM who uses Hotjar as their window into real user behavior. They watch session recordings to understand confusion, analyze heatmaps to validate layout decisions, and run micro-surveys to capture user sentiment in context. They are the person on the team who says "let me check what users are actually doing" before anyone makes a design decision based on assumptions. They think in user journeys, not funnels.

“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.”

The Segment Data Engineer

A data engineer or analytics engineer at a tech company for whom Segment is the central nervous system of the data stack. Every tool the company uses for analytics, marketing, and customer success gets its data through Segment. They did not design the original tracking plan. They inherited it. They've been cleaning it up for eight months. It will take eight more. They are the person who gets paged when an event stops flowing.

“A teammate asked how they managed maintain a clean, consistent event schema that all downstream tools can rely on.”

The Grammarly Professional Writer

A professional writer, business analyst, marketer, or non-native English speaker for whom written communication is central to their professional credibility. They use Grammarly not because they can't write — they can — but because they write quickly and under pressure, and the gap between their intent and their output sometimes closes imperfectly. Grammarly is the layer that catches what their brain skips. For non-native speakers especially, it's the difference between writing with confidence and writing with anxiety.

“It happened mid-workflow — they're writing a proposal to a new enterprise client.”

The Beehiiv Newsletter Operator

A newsletter founder, media operator, or content entrepreneur who runs a publication with 5,000–100,000 subscribers and treats it as a business with its own P&L, not a side project. They chose Beehiiv because it was built for operators — it has ad network access, referral programs, segmentation, and analytics that treat the newsletter as a product. They think in CAC, LTV, open rate, and click-to-open rate. They have a growth number they're working toward. They may or may not write the newsletter themselves.

“They're in the monthly business review.”

The Salesforce Admin

A business analyst, operations manager, or former power user who became the Salesforce admin because they were the person who understood the data best. They don't write code — they build Flows, create reports, manage permissions, and configure the org to match how the business actually works. They have 3–5 Trailhead certifications and a bookmark folder of Salesforce Help articles they reference weekly. They are simultaneously the most important and most under-appreciated person in the revenue organization.

“Not a single dramatic moment — more like a Tuesday at 3pm when they realized they hadn't thought about flow Builder that's powerful but crashes on complex flows and has limited debugging in two weeks.”

The Perplexity AI-Native Searcher

A researcher, analyst, consultant, or curious professional who started using Perplexity for quick lookups and gradually shifted most of their search behavior to it. They value citations. They appreciate the synthesized answer more than a list of links they have to open and read. They trust it for most things. They verify independently for decisions where being wrong has consequences. They've tried to explain why they prefer it to people who use Google and have not fully succeeded because the difference is in the feel of the first answer, which doesn't translate to a verbal description.

“It happened mid-workflow — they're researching a niche regulatory question for a client: the current status of SEC rules around.”

The Kajabi Course Creator

A course creator, coach, consultant, or subject matter expert who chose Kajabi because they wanted one platform instead of five. They have a course, probably a coaching program, possibly a membership community, and they wanted all of it to live together with one checkout, one email system, one analytics dashboard. They pay more for this than they would if they stitched together cheaper tools. They've decided that simplicity and integration are worth the difference. The Kajabi community is genuinely part of their decision — knowing that tens of thousands of other creators are building on the same infrastructure.

“A teammate asked how they managed run a profitable online education business without managing multiple platforms.”

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 Wix Small Business Builder

A small business owner — a personal trainer, a photographer, a bakery, an accountant — who built their website on Wix because they could drag and drop their way to something that looked professional enough. They aren't designers. They aren't developers. They are business owners who need an online presence. They picked a template, moved things around until it looked right, added their text and photos, and hit publish. They update it when they remember to. It's not perfect, but it exists, and it brings in customers.

“A potential client searches "photographer near me" and finds the business owner's Wix site on the second page of Google.”