Finding the Perfect Fit: Market Research and The TikTok Girl's Ideal Man

“I'm looking for a man in finance. Trust fund. 6’5". Blue eyes.” - @girl_on_couch via TikTok

If you’re unfamiliar with the quote or image above, kudos to you for steering clear of social media’s tentacles.

I’ll fill you in. 

A young woman on TikTok has gone viral for a video where she describes the type of man she’s looking for. Her ideal man should: have a job in finance, a trust fund, be six foot five or taller, and have blue eyes. Sounds like quite the catch! 

Seems simple to find, right? If you live in a major city like New York, San Francisco, Chicago, or Boston, you’ve probably seen a gaggle of finance bros strutting around in vests and loafers, and at least a couple of them look pretty tall. On top of that, it seems logical that some of these men would have trust funds. To even get their finance job, they probably went to top universities, and higher education costs a ton these days. Lastly, blue eyes are rare, but not that rare, right? 

You might be wondering what the heck is going on and why we’re talking about this?

Market research projects, much like the infamous TikTok girl’s quest for her dream man, often involve a process of narrowing down a broad population to find the perfect fit. However, the perfect fit can be at times “too perfect”. Researchers often work with their clients and decide their “dream participant” is something along the lines of “looking for a decision maker in banking, $200B+ assets under management (AUM), cybersecurity, no past par (3 months).”

Catchy, right!?

It seems great  - until you start looking at the numbers and how much each requirement whittles down the available sample from the population.

The Dream Man

Let’s take a look at those numbers, starting with the dream man. Let it be known that we are NOT accounting for all biases or racial, ethnic, or social factors - we know our fellow Bayesian statisticians, mathematicians, and seasoned researchers will pick this apart. This also assumes that each variable is statistically independent, when that is clearly not the case. Perfection is not our goal here.

There are 334,914,895 people in the United States. Of those, 88.3% are 18 years or older, leaving us with ~296M. Breaking it down further, the population is approximately 50.5% women and 49.5% men. That leaves us with ~146M candidates - still a lot of eligible bachelors!

Now we add in the additional requirements.

Finance: 7.39M Americans work in Finance & Insurance of which 54% are men. Leaving around 3.99M available men. 

Blue Eyes: ~27% of American citizens have blue eyes; we’re down to 1.1M eligible men.

Six Foot Five: The Census Bureau reports that only 0.172% of the population is over 6’5”. We’re left with around 1,900 bachelors. 

Trust Funds: The data isn’t great here so we’ll use what we can. According to The Cut, 1.3% of individuals surveyed reported having a trust fund in their name. If we assume a 50/50 split by sex, we’re left with ~25 men. 

Lastly, unless this young woman wants a man that’s already in a relationship or married, we need to factor in Pew Research’s study that found only 32% of men over 18 are single, which leaves us with around 8 eligible bachelors. 

Of course, the order of operations is key here, and we chose not to include race, ethnicity, age cohort, and geographical factors. We also recognize that many underprivileged communities don’t have the same resources available that, when used, may lead to careers in finance. The U.S. Equal Employment Opportunity Commission reports that ~72% of finance workers in the US are caucasian. This will inevitably skew what percentage of eligible individuals have blue eyes. Again, a further analysis could be run but the goal of this is to make a comparison between a funny TikTok and market research projects.

The Dream Participant

If we look at the numbers for the research equivalent where we’re “looking for a decision maker in banking, $200B+ AUM, cybersecurity, no past par (3 months),” we can use available data to make a crude comparison. We’ll assume a 100% IR meaning if they make decisions about cybersecurity products and fit the criteria of bank size, AUM, location, etc. then they will pass.

We’ll start with banks. There are over 4,500 banks in the US alone but when looking at AUM of over $200B, only 22 US banks meet the requirement. 

Of those banks, there are ~60k IT employees. 

Of the ~60k, only ~3k are regularly involved in cybersecurity as part of their role. 

Of those ~3k, only 360 are at the Director level or higher and 200 VP or higher, which are the levels we typically see decisions being made.

Of these 360, how many of them regularly participate in qualitative research? It’s certainly not 100%. While it’s likely much less, we’ll assume 50% of them participate in research regularly. We’re left with 180 Director+ and 100 VP+ candidates. 

Finally, let’s consider the past participation requirement of three months or longer, meaning that in order to qualify, they can not have participated in calls on cybersecurity in the last three months. When Zintro studied past participation in 2023, the team found that 52% of B2B participants had taken part in a study either through Zintro or another company in the previous three months. When we broke down the data by seniority, 78% of the Director+ participants reported taking part in studies over the last three months. Applying this new information, we’re left with 40 eligible people. 

Finally, if we take the average screener length provided by tech-focused researchers (12 questions) and compare it to the average completion rate of 12-question screeners (64.5%) we conclude our breakdown with 26 available people in the United States. 

Not a great starting sample of potentially eligible participants for this study!

Whether it's for market research or finding the perfect man, each additional requirement whittles away at the available sample. While being precise is critical for detailed research, specific requirements come at a cost and will jeopardize the outcomes of your research. 

Wondering whether the sample is realistic and if changes can be made to avoid falling into reduced sample size traps? Your Zintro project team is always here to help and answer any questions you might have.

Other Blogs You May Enjoy