User Personas Are Dead: How to Actually Know Your Customer (OSINT Edition)

#marketing #analytics #osint #data

Originally published at axelfreeman.com

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. These are different words for the exact same thing: who is my customer, what do they look like, and what do they do?

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 subjective judgment every time.

The Rule: There is no useless information in understanding your user. 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)

Install Analytics. Today.

Yandex Metrika. Google Analytics. AppMetrica for mobile. 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?"

Analytics gives you the first, most brutal truth: is anyone even there?

Track Everything

Every click. Every scroll. Every page. Every rage-click. Every form abandonment. Total surveillance of the user journey.

When you can replay a user's session and feed it to an AI for analysis, you start seeing patterns that dashboards hide.

Critical Distinction: Drop-off point vs Loss-of-interest point. A drop-off is when someone who intended to pay hits friction — fix it. A loss-of-interest point is when a curiosity browser naturally disengages — they were never a customer. Put them in a subscription funnel instead.

Phase 2: Where the Guides Stop

Every tutorial ends here: "Set up analytics, track events, hire analysts, good luck." But you can go much deeper. The information is already out there — publicly available, legally accessible.

Phase 3: Freeman Framework — OSINT-Level User Profiling

OSINT — Open Source Intelligence. All data has already been leaked or published. It's accessible through Google, public registries, and open APIs. One email address can become 200+ data points.

From an Email:

Registered sites: Which platforms have this email? GitHub? Stack Overflow? LinkedIn?
Professional identity: GitHub profile → coding languages, projects, activity patterns.
Business affiliations: Company registries → where they work, role, company size.
Public photos: Email → name → public photo → computer vision analysis.

From an IP Address:

Network type: Corporate? Home? Mobile?
Working hours: 2 PM from corporate IP = business user.
Organization targeting: Multiple users from one company network? That company is evaluating your product.

The Depth Potential: Classic approach: "user visited at 2 PM and left." Freeman Framework: "user visited at 2 PM from a corporate network, works at a 50-person fintech, codes Rust on GitHub, last tweeted about payment APIs."

Why: Hours = Money

User hours = revenue. More time in app = more value extracted. Every data point helps AI agents make better retention decisions.

The Legal Line

This is dangerous if you're sloppy. GDPR, CCPA, local variants. Rules:

Only open sources. Public websites, public registries, public APIs. Never breach paywalls.
Marketing purposes only. Build profiles to serve better, not to stalk.
Evaluate risk per jurisdiction.
When in doubt — lawyer. Real one, not a blog post.