How to analyse a competitor by figures rather than impressions

How to analyse a competitor by figures rather than impressions

Scrolling somebody's account for twenty minutes is not analysis: you remembered what you personally liked. The real question is which subjects produce a response above their own average, and eyes cannot answer it. Below is how it gets calculated.

Scrolling somebody's account for twenty minutes is not analysis: you remembered what you personally liked. The real question is which subjects produce a response above their own average, and eyes cannot answer it. Below is how it gets calculated.

"It works well for them" is not analysis

The standard way of studying a competitor: open his account, scroll for twenty minutes, note that "theirs feels livelier" and "they post more often".

Not one decision can be made from that. You looked at what happened to be on the first screen and remembered what you personally liked.

The real question is different: which subjects and formats produce a response above their own average. Not above yours, not above "the market" — above their usual level. Only that separates luck from a pattern.

Eyes cannot answer it. You need figures for every publication, and you need a mass of them rather than the last dozen.

What analysis like that showed me

I collected 6 502 posts from eleven channels over 19 months and calculated the shares by subject.

The result was unexpected for everybody in this market. Posts with price benchmarks: nine. Nought point one percent of the feed. About a first job: seven. About a service as paid work: twenty.

Meanwhile the "analysing mistakes" format showed 14% hot posts against a base of 11.6% and worked for eleven channels out of eleven. The strongest measured format in the niche — and used systematically by almost nobody.

Not one of those conclusions can be reached by reading a feed. They can only be calculated.

And my whole content strategy is built on those two figures: I write about what is needed en masse and what people are silent about en masse.

How this is done without manual work

Collecting data from social media is the first step of a chain: real figures are pulled in first, and the remaining steps work on the basis of them. Chains like this are assembled from the n8n and Make automation templates (Creator plan and above).

Where to get the data

Post metrics are collected by ready templates and skills on the platform, one for each site:

The key point: you need not only the text but the metrics — views, likes, comments. That is, material fit for analysis rather than for reading.

They work on open accounts.

The whole chain

A scraper by itself gives a table. The value appears when the following steps stand behind it.

Step 1. Collection. The scraper takes the account's recent publications with their metrics.

Step 2. Analysis. A chat model receives the mass of data and looks for the pattern: which hooks in the first seconds, which subjects produce a response above that same account's average, which length works better.

The phrasing of the request matters here: the comparison must be against their own median rather than against absolute numbers. A post with a million views on a million-follower account is normal rather than lucky.

Step 3. Generation. The model makes your versions on the logic found — not copies but material on the same principle.

Step 4. Publication. The final step of the chain sends the finished piece to the major social platforms.

The whole circle inside one chain: collected → analysed → made your own → published.

Three scenarios that pay back immediately

Analysing competitors continuously. Not once before a strategy session but regularly. A niche changes, and what worked six months ago is now the norm.

Content from your own statistics. Pull your own posts with their metrics, find the most successful and ask the model to make new ones on the same logic. The most underrated scenario: you already have data on what works specifically for you and are not using it.

Monitoring the niche on a schedule. The chain collects recent videos from several accounts itself and sends a summary. With no manual scrolling.

The last is put on a schedule in the n8n and Make automation templates (Creator plan and above).

What scrapers do not do

Honestly, so expectations are not inflated.

They do not read closed accounts. They work on open ones.

They do not explain the cause. They show that this post landed and that one did not. Why is the next step's work and yours.

They do not replace judgement. The data will show what works for a competitor. Doing the same is not always the right decision: sometimes the most valuable thing in an analysis is finding a subject they do not have, as happened with me and price benchmarks.

And they are not about copying. An analysis exists to understand the mechanics rather than to repeat somebody's post in your own words.

How a competitor breakdown is sold

Competitive analysis as a separate service. It used to be expensive precisely because it ran into man-hours: somebody had to consolidate hundreds of publications into a table by hand. Now the collection takes minutes and the work remains in the interpretation — that is, in what people pay for.

What is delivered to the client: a table of every publication with metrics, shares by subject and format, what produces a response above their own average, the open niches — subjects that are needed and that nobody has, and format recommendations justified by figures.

How to approach a client. Not with an offer of analysis but with an analysis of his own account already done. Three precise observations gathered before the first conversation work better than any commercial proposal.

What the platform offers for competitor analysis

The n8n and Make automation templates (section, Creator, Make, N8N and Full plans) are ready chains: scrapers, chat models, generation, publication. Nothing has to start from a blank sheet: open a template, insert a new account, launch. The launch schedule is set in the template itself, and the chain collects the data daily by itself.

Individual generations inside a chain are paid from the balance on usage; the price is visible before launch.

Registration is free and opens three days of full Basic access — all the assistants, the prompt texts, the skill files and six foundational express courses, including "Perplexity as an investigative tool" on the method of verifying data.

Where to start today

One action. Take one competitor's account and collect its last thirty publications with metrics. Do not read — count.

Calculate the median views and find those one and a half times above it. Usually three to five out of thirty, and they have something in common.

That something is the answer to the question you spent twenty minutes trying to get by scrolling.

A ready tool for finding open niches

Analysing a competitor answers the question "what is he doing". There is a tool for a different question — where is anything free at all.

The n8n template “Finding young YouTube channels by niche” works like this: you enter a query, limit the maximum age of a channel in days and specify the search depth.

The system finds videos from the last two weeks with the highest views, requests the channels' statistics, calculates their age and keeps only those younger than the limit set. Duplicates are removed and the result goes into a table with links.

The logic of the strategy. If a channel is twenty or thirty days old and its video has hundreds of thousands of views, the subject has enormous potential. You find not where you compete with giants but where even a newcomer with no history gets traffic.

What that gives for competitor analysis. Instead of analysing one well-known player you get a list of young channels that broke through within weeks — that is, fresh working approaches rather than established ones.

This is an advanced template: it opens on the Full plan, or for a one-off $9, or for 1000 XP.

The prompt that does this to a method

The express course Freelance 2.0, prompt №4 "Competitor research across channels" — 90 minutes, Basic plan. Why channels specifically is explained in the course itself: there the view counts are not inflated and the comments reflect real interest.

The output for each competitor: the top 10 hot posts with metrics and analysis, the semantic categories — subjects that consistently land, positioning and sales funnel, weak points. And most of all, the open niches: where the competitors are not working.

Assistants for the analysis, Basic plan, the catalogue:

Monitoring instead of a one-off measurement. n8n and Make automation templates — the chain is assembled once and launched on a schedule. That turns an analysis into a subscription: the client pays once for a snapshot and then monthly for the answer to "what has changed".

Search and fact-checking — the express course "Perplexity as an investigative tool", included in the Basic plan and above or $9 one-off: precise search operators and working with databases instead of the general web.

Skills for analysis and monitoring

A one-off analysis goes out of date within a month. The value appears when it becomes a series — and here is what does that.

What makes an analysis a product rather than one-off work. The second and third items. The client pays once for a snapshot and then monthly for the answer to "what has changed" — and that is a subscription.

What to take on the platform for this task

What to read next

Build it once, it runs always — how to assemble a chain like this.

Content gets made while you sleep — how to put the analysis on a schedule.

How to find trends — what to do with the findings.

Audience segmentation as a service — how to sell an analysis like this.

n8n and Make automation templates — the full guide to automations.

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