A marketer built Znich, an AI analytics tool for YouTube creators, in five months

A marketer has described on Habr how he single-handedly built Znich, an AI analytics tool for YouTube video creators, in five months. The tool answers a question that torments every content creator: why does one video get a million views while another, made with the same effort and at similar quality, vanishes into silence.

Where the idea came from

Working as a marketer, the author regularly analyzed competitors: he looked at which of their videos took off and tried to find patterns. YouTube Studio hands over the data readily: audience retention, thumbnail CTR, traffic sources, search positions. The problem is that it's all scattered. To get a coherent picture, you have to merge the numbers by hand, compare videos against each other, form hypotheses and test them one at a time. The process took hours and did not guarantee an answer to the main question.

The author decided to automate the analytics, not for the sake of a startup but for his own daily work. That detail matters: Znich began as a tool for himself and only later became a product. That is why it has nothing superfluous: each feature solves a specific problem the creator ran into personally, many times over.

What Znich can do

Five months of development, without a formal engineering education, mostly with the help of AI tools and an iterative approach. The author describes the process candidly: each feature appeared when the latest pain point became unbearable and manual analysis too costly.

Znich analyzes what standard analytics does not provide in a ready, interpreted form:

• Audience retention along the timeline: where exactly viewers leave, and at which second
• Thumbnail CTR in combination with the title: what works better in a specific niche and among competitors
• Traffic sources and their shares: search, recommendations, direct visits, external links
• The structure of competitors' successful videos: the first seconds, narrative transitions, endings
• Virality patterns: what videos with millions of views in a niche have in common

The key difference: not just numbers, but interpretation. The AI layer tries to answer "why," not only "how many." That shifts the author's work from collecting data to making content decisions.

Feature by feature, driven by pain

The author says the product developed not along a roadmap but in response to complaints, first of all his own. If an analysis had to be done manually more than twice, a new feature appeared. There was no product planning in the classic sense, only pain as a prioritization method.

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

But there is no coherent answer in those numbers; you have to assemble it by hand," the author writes.

Building for yourself first gave the product a rare quality: every interface element addresses a real problem, not a hypothetical one. This contrasts sharply with analytics dashboards built on the principle of "let's add everything we can measure," which ended up as overloaded interfaces where it is hard to find the answer to a simple question.

Over five months the product went from a personal script to a finished tool with a full interface. The author does not hide that the process was nonlinear and at times painful, but each iteration brought Znich a little closer to the question that started it all.

What this means

The story of Znich is a vivid illustration of how AI tools have made development accessible to non-engineers. A marketer with no technical background built a working niche product in five months, something that would previously have required a full team of developers. The barrier to creating specialized SaaS tools has dropped to one person with a clear pain point, time, and the will to solve it.