How analyzing 1,080 Instagram accounts helped us find a defensible niche in the psychology market

Entering the psychology market was never simply a marketing problem.

We were entering a category where thousands of qualified psychologists, therapists, coaches, educators, and content creators were already competing for attention. At the same time, hundreds of accounts with no clearly visible qualifications were publishing advice every day and, in many cases, attracting more attention than licensed professionals.

Creating another general psychology page would have been easy.

Creating one with a commercially defensible position was a different problem.

The question we needed to answer was:

Where in this market was there still meaningful demand, enough purchasing power, and enough room to differentiate without competing directly against hundreds of nearly identical accounts?

We decided not to begin with content.

We began with the market.

We analyzed more than 1,000 accounts before choosing what to talk about

The first phase was essentially reverse engineering.

We collected approximately 1,080 successful Instagram accounts across psychology and closely related categories. We deliberately avoided using follower count as the main filter.

A large audience could tell us that an account had learned how to attract attention. It did not necessarily tell us whether the account had found a valuable market.

For each account, we tracked roughly 16 variables, including the audience being targeted, the primary problem addressed, services or products offered, apparent price level, monetization model, content format, high-performing topics, calls to action, positioning, audience sophistication, and the apparent level of competition around the topic.

Once the data was cleaned and tagged, the accounts began to fall into approximately 17 recognizable sub-niches.

That changed the nature of the research.

Instead of comparing 1,080 individual Instagram pages, we could compare 17 different market structures.

The objective was not to identify the "best" account.

It was to identify repeating combinations of:

Audience + problem + offer + content

that appeared to work across multiple businesses.

Popularity turned out to be a poor proxy for market quality

We scored the major sub-niches across eight commercial criteria:

problem urgency, demand, audience purchasing power, willingness to pay, competitive intensity, differentiation potential, content depth, and the ability to convert attention into a product or service.

Each factor received a score from 1 to 5, giving each market a maximum possible score of 40.

Some of the largest psychology categories initially looked attractive because the potential audience was enormous. But after scoring them, several landed in the 24 to 27 out of 40 range.

The problem was rarely demand.

The problem was the combination of demand and economics.

Some topics attracted a large volume of attention but relatively weak willingness to pay. Others attracted financially capable audiences, but the problem was not urgent enough to motivate action. A third group had strong demand and clear commercial potential, but competition was so intense that a new brand would need substantial reach simply to become noticeable.

The sub-niche we eventually selected scored approximately 33 out of 40.

Its strongest characteristics were problem urgency and content depth, both close to 5 out of 5. Purchasing power, willingness to pay, differentiation potential, monetization potential, and customer value were closer to 4 out of 5. Competition was still meaningful, but at roughly 3 out of 5, it was more manageable than in the broad psychology categories.

That combination mattered more than raw audience size.

The market did not need to be empty.

It needed to contain enough unresolved demand to justify entering it.

Then we studied 180 pieces of content instead of trying to invent a content strategy

Once the market position was clearer, the next temptation would have been to create something completely original.

We did the opposite.

We identified approximately 45 accounts that were particularly relevant to the audience and problem we had selected, then reviewed around 180 of their stronger-performing posts and videos in more detail.

We were not looking for posts to reproduce.

We were looking for the mechanics behind them.

We compared opening hooks, problems discussed, language, emotional framing, content structure, calls to action, and the difference between content that generated passive reach and content that appeared to generate commercial intent.

Several patterns appeared repeatedly.

Roughly two-thirds of the strongest save and share content could be grouped around only six recurring problem themes. The wording changed from creator to creator, but the underlying problems were remarkably consistent.

The successful creators were not producing 100 completely different ideas.

They were approaching a relatively small number of important problems from different angles.

That observation gave us the basis for the first content system.

We copied the structure of success, not the content itself

The distinction was important.

We did not copy captions, scripts, reels, or visual concepts.

We extracted patterns.

We looked at questions such as:

Which hooks consistently made the audience stop?

Which problems generated comments from people describing their own situation?

Which topics produced saves and shares rather than views alone?

What type of language made technical psychological concepts understandable without making the brand sound generic?

Which calls to action naturally moved a user from consuming content to asking for help?

Most importantly, we started separating attention content from conversion content.

The two were not always the same.

During the early testing period, some broad posts could generate around 15,000 views while producing only one or two qualified enquiries.

A more specific problem-focused post might receive only 7,000 to 9,000 views, yet generate four or five qualified enquiries.

Measured purely by reach, the first post looked more successful.

Measured by commercial relevance, the second was several times more productive.

That distinction changed how we evaluated content.

The first 12 weeks became a structured experiment

We launched the initial content strategy with approximately 60 pieces of content over 12 weeks, averaging about five posts per week.

The purpose of that period was not simply to grow the account.

It was to replace competitor data with our own data as quickly as possible.

During the first four weeks, a typical post reached approximately 4,500 accounts, and combined saves and shares were around 3% of reach.

More importantly, the account was generating roughly eight qualified enquiries per month.

Those numbers became the baseline.

As we identified which problems, hooks, and formats produced stronger intent, we gradually concentrated more of the content around the six recurring pain themes identified during the original research.

By weeks nine to twelve, average reach had increased to approximately 7,000 accounts per post.

But reach was not the most important change.

Combined save and share rates had moved closer to 6%, and qualified monthly enquiries had increased from roughly 8 to 24.

In other words, average content reach had increased by roughly 55%, while qualified enquiries had approximately tripled.

That gap told us that the improvement was not coming from audience growth alone.

The audience being attracted was becoming more relevant.

Our own data eventually became more valuable than competitor research

The original database of 1,080 accounts had helped us decide where to start.

It was never supposed to dictate the strategy permanently.

Once we had enough first-party data, we began weighting our own results more heavily.

Every new piece of content became an experiment:

Hypothesis → publish → measure → learn → adjust

One post might test a different framing of an existing pain point.

Another might compare a broad educational hook with a situation-specific hook.

A third might keep the topic constant while changing the call to action.

Over time, the distinction between reach and business value became clearer.

Some posts were excellent discovery assets. They introduced the brand to new people but created relatively little immediate buying intent.

Others reached fewer people but generated several times more qualified conversations per 10,000 views.

Both types had a role.

The mistake would have been judging them by the same metric.

What changed

The biggest result was not that the brand discovered a magical untapped category.

It found a more defensible position inside an already crowded one.

The research started with approximately 1,080 market examples, reduced them to 17 meaningful sub-niches, evaluated those markets against eight commercial criteria, and ultimately focused the brand on one position that scored approximately 33 out of 40 on the opportunity framework.

Content research then narrowed hundreds of possible topics into six recurring audience problems that repeatedly showed evidence of demand.

During the first 12 weeks, approximately 60 content experiments gave us enough first-party evidence to begin replacing assumptions with actual audience behaviour.

Average post reach moved from roughly 4,500 to 7,000, save and share activity roughly doubled, and qualified monthly enquiries increased from approximately 8 to 24.

None of those numbers, individually, explains the strategy.

Together, they show what changed.

The brand stopped competing for generic psychology attention and started becoming more relevant to a specific group of people experiencing a specific type of problem.

Why the strategy worked

The advantage did not come from discovering something nobody else had ever done.

It came from reducing uncertainty before investing heavily in execution.

Instead of asking what content we personally wanted to create, we first asked where the strongest combination of demand, urgency, purchasing power, willingness to pay, and competitive opportunity existed.

Instead of assuming which content would work, we studied patterns across proven examples.

Instead of copying individual posts, we extracted the mechanisms behind them.

And instead of continuing to rely on competitor benchmarks forever, we used the first few months of publishing to build our own evidence base.

That sequence reduced random experimentation.

We were still testing, but we were testing hypotheses that already had some market evidence behind them.

The broader business lesson

In a saturated market, originality is often overvalued at the beginning.

You do not necessarily need to discover an idea that nobody has seen before.

You need to identify something that already works, understand why it works, separate the underlying pattern from the visible execution, and apply that pattern more precisely to a specific audience and problem.

The competitive advantage is not copying what the market produces.

It is learning faster from what the market has already proven.

Questions worth asking in your own market

If your market already feels crowded, consider a few questions before producing more content:

1. Are you evaluating opportunities by audience size, or by demand, urgency, purchasing power, and willingness to pay together? 2. Which sub-niches are large enough to support a business but narrow enough to allow meaningful differentiation? 3. Which competitor content generates attention, and which content appears to generate buying intent? 4. What patterns appear repeatedly across several successful competitors rather than one exceptional account? 5. Are you still making decisions from competitor data when you already have enough first-party data to learn from your own audience?

A crowded market does not automatically mean there is no opportunity left.

Sometimes the opportunity becomes visible only after you stop looking at the market as one large category and start examining the smaller combinations of audience, problem, offer, and content inside it.