A marketer built Znich in five months — AI analytics for YouTube video creators

A marketer told on Habr how over five months he independently built Znich — an AI analytics tool for YouTube video creators. The tool answers the question that torments any content creator: why does one video get a million views while another with the same effort and similar quality goes unnoticed?

Where the idea came from

While working as a marketer, the author regularly analyzed competitors: watched which videos they had success with and tried to find patterns. YouTube Studio honestly provides data — audience retention, preview CTR, traffic sources, search positions. But the problem is that it's all scattered. To get a complete picture, you need to manually compile the numbers, compare videos with each other, build hypotheses and test them one by one. This process took hours and didn't guarantee an answer to the main question.

The author decided to automate analytics — not for a startup, but for his own daily work. This is an important detail: Znich appeared as a tool for himself and only later became a product. That's why there's nothing unnecessary in it — each feature addresses a specific problem that the creator faced personally and repeatedly.

What Znich can do

Five months of development — without classical engineering education, mainly using AI tools and an iterative approach. The author describes the process honestly: each feature appeared when the next pain point became unbearable and manual analysis became too costly.

Znich analyzes what standard analytics doesn't provide in a ready and interpreted form:

• Audience retention on a timeline — exactly where viewers drop off and at which second
• Preview CTR linked with the title — what works better in a specific niche and for competitors
• Traffic sources and their shares — search, recommendations, direct visits, external links
• Structure of successful videos from competitors — first seconds, semantic transitions, endings
• Patterns of virality — what's common about videos with millions of views in the niche

The fundamental difference: not just numbers, but interpretation. The AI layer tries to answer the question "why," not just "how much." This shifts the author's work from data collection to making decisions about content.

Feature by feature — from pain

The author honestly describes how the product evolved: not according to a roadmap, but according to complaints — first and foremost his own. If some analysis had to be done manually more than twice, a new feature appeared. No product planning in the classical sense — only pain as a prioritization method.

"YouTube Studio honestly shows numbers — retention, traffic sources, CTR.

But there's no complete answer in those numbers: you have to compile it yourself," the author writes.

This approach — building for yourself first — gave the product a rare quality: each interface element solves a real problem, not an assumed one. This contrasts sharply with analytical dashboards that were built on the principle of "let's add everything we can measure" and turned into overloaded interfaces where it's hard to find an answer to a simple question.

Over five months, the product traveled from a personal script to a fully fledged tool with a complete interface. The author doesn't hide that the process was nonlinear and sometimes painful — but each iteration brought Znich closer to the answer to the question it all started with.

What this means

The Znich story is a clear illustration of how AI tools have changed the accessibility of development for non-engineering specialists. A marketer without a technical background built a working niche product in five months, something that would have previously required a full team of developers. The barrier to entry for creating specialized SaaS tools has dropped to the level of one person with a clear pain point, time, and the will to solve it.