User Personas Are Dead
How to Actually Know Your Customer (OSINT Edition)
Buyer personas are fiction. Customer avatars are wishful thinking. ICP templates are corporate fanfiction. They all ask the same question — "who is my customer?" — and answer it with imagination instead of data. This article is about what comes after the Figma template. How to build a real user portrait: from Yandex Metrika to total session tracking to OSINT-level profiling that turns one email into 200+ data points. And where the legal line is.
All Roads Lead to One Question
User persona. Buyer persona. Customer avatar. Ideal Customer Profile. Jobs to Be Done. Customer journey map. Segmentation model.
These are different words for the exact same thing: who is my customer, what do they look like, and what do they do? The industry invented a dozen frameworks to answer one question, and somehow made it seem like rocket science.
It's not. But there's a reason this work exists — and it's important. When you know who comes to your product, you can start building relationships. Patterns emerge. Segments become visible. Every piece of data about your user is raw material for product ideas. And unlike your intuition, data doesn't lie. Mathematics beats the subjective judgment of one person's brain. Every time.
📊 The Rule
There is no useless information in the journey of understanding your user. Every data point you collect — every click, every second of session time, every email domain — can become a living, actionable product insight. The goal isn't a pretty persona card. The goal is to know more about your user than they know about themselves.
Phase 1: The Basics (What Everyone Tells You)
Let's start with the obvious — the stuff every guide covers and you should absolutely do.
Install Analytics. Today.
Yandex Metrika. Google Analytics. AppMetrica for mobile. If you don't have analytics running, stop reading and install it. Connect it as an MCP server to your AI agent. Then ask your agent real questions: "How many users came today? Where did they go? What did they click?"
Analytics is the foundation because it gives you numbers. And numbers tell you the first, most brutal truth: is anyone even there? Zero visitors should only exist in the first second after you deploy. After that, every second of zero is resources burning into nothing. A great funnel with zero users is a work of fiction.
Track Everything
Every click. Every scroll. Every page. Every rage-click. Every form abandonment. Total surveillance of the user journey. Not in a creepy way — in a "we care about every interaction" way.
When you can replay a user's session and feed it to an AI for analysis, you start seeing things that traditional analytics dashboards hide. Patterns. Hesitations. The exact moment someone gives up.
⚠️ The Critical Distinction
Drop-off point ≠ Loss-of-interest point. A drop-off is when someone who intended to pay hits friction and leaves. Fix it — that's a conversion problem. A loss-of-interest point is when a curiosity browser (a trend-watcher, like you and me) naturally disengages. They were never a customer; they just wanted to see what's happening. Don't optimize for them. Put them in a subscription funnel instead — they're a free audience base for later.
Phase 2: Where the Guides Stop
This is where every tutorial ends. "Set up analytics, track events, hire analysts, good luck." That's the standard advice. And it's where most people stop.
But here's the thing: you can go much deeper. Much, much deeper. And the information is already out there — publicly available, legally accessible, waiting to be collected. This is what I call the Freeman Framework for user profiling.
Phase 3: Freeman Framework — OSINT-Level User Profiling
There's a field called OSINT — Open Source Intelligence. The premise is simple: all data has already been stolen, leaked, or voluntarily published. It's sitting there, accessible through Google, public registries, and open APIs. You just have to know where to look.
What does this mean for understanding your users? One email address can become 200+ data points.
What You Can Learn From an Email
A user signs up. You have their email. Here's what you can legally discover from open sources:
- Registered sites: which platforms have this email? GitHub? Stack Overflow? LinkedIn? ProductHunt?
- Professional identity: GitHub profile → coding languages, projects, activity patterns. This tells you how to talk to them. A Python developer and a marketing manager need completely different communication styles.
- Business affiliations: if the email appears in company registries, you know where they work, their role, even company size.
- Public photos: email → name → public photo. Run it through computer vision. Age range, gender presentation, environment. More context for your profile.
This isn't science fiction. This is what OSINT practitioners do every day. Applied to marketing, it means you understand who you're dealing with before you send a single message.
What You Can Learn From an IP Address
Even simpler. Their IP tells you:
- Network type: corporate? Home? Mobile carrier?
- Working hours: if they're accessing your product from a corporate IP at 2 PM on Tuesday, that's a business user. If it's 11 PM from a residential IP — different context.
- Organization targeting: see multiple users from the same company network? That company is either evaluating your product or already using it. Time for a targeted outreach campaign.
🔍 The Depth Potential
For an active user, you can realistically collect hundreds of parameters: email → registered sites → social profiles → professional history → public mentions → behavioral time patterns → network origin → device fingerprint. Feed all of this to a neural network, and you can predict behavior before it happens. The classic approach says "user visited at 2 PM and left." The Freeman Framework says "user visited at 2 PM from a corporate network, works at a 50-person fintech company, codes in Rust on GitHub, and last tweeted about payment APIs." That's the difference between guessing and knowing.
Why This Matters: Hours = Money
There's a simple equation in any product: user hours = revenue. The more time someone spends in your app, the more value you can extract — through subscriptions, ads, upgrades, referrals. Every extra data point you collect helps an AI agent make better decisions about how to keep users engaged, what to show them next, and when to ask for money.
At small scale, this feels like overkill. At scale — with thousands of users — it's the difference between guessing at retention and engineering it.
The Legal Line (Read This)
Let's be crystal clear: this is dangerous territory if you're sloppy. Every country has data protection laws — GDPR in Europe, CCPA in California, local variants everywhere else. Using OSINT data for commercial purposes operates in a gray zone that varies by jurisdiction.
Rules to stay on the right side:
- Only open sources. Public websites, public registries, public APIs. Never breach a paywall. Never access private databases. Never use leaked passwords or credentials.
- Marketing purposes only. You're building a profile to serve users better, not to stalk individuals.
- Evaluate risk per jurisdiction. What's fine in the US might violate GDPR in Germany. Know where your users are and what laws apply.
- When in doubt, consult a lawyer. Not a blog post. Not an AI. A real lawyer who understands data protection in your target markets.
The Freeman Framework is powerful specifically because it works with data that's already public. But "public" doesn't always mean "free to use commercially." Know the difference.
The Bottom Line
User personas — the Figma templates, the smiling stock photos, the imaginary "Sally the SaaS Manager who's 34 and loves yoga" — are comfort food. They make founders feel like they've done the work without actually doing the work.
Real user understanding comes from:
- Analytics: Metrika, GA, session replays. Know your numbers.
- Tracking: Total surveillance of the user journey. Every click matters.
- Distinction: Drop-offs vs. loss-of-interest. Fix the first, funnel the second.
- Enrichment: Email → OSINT → 200 data points. Know who you're talking to.
- AI feeding: Feed all of it to neural networks. Let machines find the patterns humans miss.
More data about your users means better product decisions. Better decisions mean more engagement. More engagement means more money. The equation is simple. The execution is the hard part — and now you know how to start.
📮 Want to profile your users?
Send me a message on Telegram: @axelfreeman. Tell me about your product and I'll show you what OSINT-level profiling looks like for your specific case. One analysis, free.
FAQ
What's wrong with user personas?
Most personas are fiction — based on assumptions, not data. A Figma template filled with imaginary demographics doesn't tell you who actually comes to your product. Real profiling starts with analytics data, session tracking, and — if you're rigorous — OSINT-level enrichment from publicly available sources.
How do I find out who my actual users are?
Start with analytics: install Metrika or GA, track every event, record sessions. One email address can be enriched to 200+ data points using OSINT — registered sites, GitHub profiles, company affiliations, even public photos. This builds a real, data-backed portrait.
What is the Freeman Framework for user profiling?
It combines traditional analytics with OSINT enrichment: email → public registrations → professional identity (GitHub, LinkedIn, company registries) → behavioral patterns (time, IP, session path). The result is a multi-dimensional profile that feeds AI agents for better product decisions.
Is OSINT user profiling legal?
Using publicly available, open-source data for marketing purposes is generally legal, but each jurisdiction has specific laws (GDPR, CCPA, etc.). Only use open sources, never breach paywalls, and consult a lawyer when in doubt.
What's the difference between drop-off and loss-of-interest?
A drop-off is where paying-intent users hit friction — fixable UX problems. Loss-of-interest is where curiosity browsers naturally disengage — they were never customers. Session replay + AI analysis helps distinguish between them and focus on real conversion issues.