What is Directional Research, Anyway?

If you’ve spent any amount of time around market researchers, you have probably heard the term “directional.” As in, “the survey findings seem directionally correct” or “this was just a quick study to get some directional findings.”

This means the data was good enough to test a hypothesis/make a decision but not good enough to trust the exact numbers. In short, it means, the study was good enough, let’s move on. It often means there were no surprises.

Literally, it means that we believe the trend lines or shape of the data but not the exact statistical output. Like, if the results show people prefer Brand A over Brand B by a statistically significant margin, we believe that is the case in the market and will not quibble or spend more money trying to figure out if people really like Brand A twice as much or only just slightly. It doesn’t matter because the question is answered. Brand A is the winner.

The term has saved research buyers hundreds of millions of dollars and made operations easier for survey sample purveyors. According to some, it also means we can stop worrying about survey data quality as an unsolved problem in market research and just lean into consumer survey sample as a commodity. Because if the data being provided were not good enough, people wouldn’t be buying it and the market wouldn’t be the size it is (i.e., more than a billion USD).

In theory, people seek directional research for quick relatively unimportant decisions. When there’s real money on the line, it’s worth investing in better data that you can parse and make good inferences about. I believe, however, that in some cases, buyers pay for directional research and treat it like it’s much more robust. This is understandable, as statistical thinking is challenging, especially for busy executives who don’t look at survey data all day, and it’s hard to conceptualize multiple types of error simultaneously. It is always tempting to take the survey results at face value when you’re staring at a PowerPoint slide filled with specific numbers.

Sure, we have statistical rules. We know how big the sample should be to reach certain confidence intervals on normally distributed results, etc. But when the industry moved away from probability samples, we gave up some important ground and perhaps got used to the feeling of doing so. You can survey 1,000 consumers, but if you recruited all of them through online video games, you don’t have a representative sample, and the directional findings are worthless.

I suspect that when the industry moved on to convenience sampling, use of the term directional increased one thousand-fold. As in, “sure the sample isn’t perfectly representative, but it should be directionally correct.” So you target 400 completions to feel better about the sample and only end up getting 230. You lose some confidence in your inferences but you can shrug and say the research was only intended to be directional anyway. 230 responses is better than 0 responses, right?

Here’s the problem, and yes it’s a slippery slope issue. Let’s say you survey 400 US consumers about frozen pizza brands. You find that 35% of respondents prefer Brand A and Brand B is the next closest brand at 20% preference. But then you clean your data, stripping away the obvious fraud, and the findings change such that Brand A is preferred by 27% of people and Brand B by 22%. A lot of people will say, well, Brand A is still the preferred brand. Good enough. Case closed.

But what about the quality issues that weren’t obvious? Let’s say you cut 100 responses, leaving you with just 300. But suppose also that 50 additional respondents are not actual US residents, they just lied and said they were. And suppose an additional 50 respondents had already completed 10 surveys that afternoon, and suppose the survey was 25 minutes long and the question about frozen pizza brand preferences was towards the end. Are you still sure Brand A is the preferred brand in the market among your target consumers? You may be directionally wrong now.

My main contention is that there are certain fundamental requirements to generating usable survey data. I would argue that having ID-verified respondents is one of them. If you want to survey US consumers, you need there to be, let’s say, a 99% likelihood your respondents are in the US. Because if you don’t know whether this is the case, you don’t know how much your data is skewed and therefore whether any findings are in fact directionally correct.

Do I think directional research is ever appropriate? If you really trust that your respondents are who they say they are, you are okay with how they were recruited and qualified for your study, and you have a relatively short, engaging, and unbiased survey, feel free to make statistical inferences. If you survey 100 people then and 80% prefer Brand A, you can assume that’s directionally correct. Brand A is the winner. Just know that there’s a large grey area and if you just treat survey data like a commodity and go with the cheapest option all the time, you are increasing the risk of your directional research pointing in the wrong direction or having such large error margins that your research doesn’t say anything.

I know many in 2026 are stuck with limited research budgets, but if you can afford to do better, you should. Do fewer studies and invest more in those studies. If you can find enough of the people you want to survey.

One of the worst things about market research is that people who buy it do not often revisit whether the research indicated the right or wrong decision. Sometimes it’s impossible to know if you made the right decision, like if you decided to not launch a particular new product. You’ll never know how that product would have done. I suspect that if it were easier to track these kinds of things, people would be far less satisfied with the survey sample market than they are and it would be hard to say the current system is working as intended.

You might say, well, if the market is selling vibes based on fairy dust, if that’s what people want to buy, then you might as well sell it to them. A few buyers are honestly okay with vibes. They’re just checking a box. But most think they’re getting something better. We really need more research-on-research focused on research-based decision outcomes. A lot of people thought smoking was fine in 1950. But it’s a lot clearer what the risks are today in 2026. Because we did the research.

From my experience, it seems that the one important reality check for buyers is whether the market size/share results in the survey jive with their sales conversations and sales data. If some concordance is there, the rest of the data gets a pass. But if concordance is not there, the whole study will be met with suspicion and perhaps rightly so. Perhaps you make this concordance a criterion. That’s fair, but research is about more than confirming what you think you already know.

I hope one day ten years from now, we’ll look back with relief that we are no longer taking such risks with survey data. Perhaps the next generation of leaders with our vaunted idealism can usher in new expectations. Yes, I know I’m being idealistic. I’m a Millennial!

But it’s time for non-negotiable standards. And it’s time for post-mortem reality checks. It’s time to expect your survey data to prove its value. Don’t just ask, “Did the decision work out well?” Ask, “Did the survey data really make any difference?”

Remember that bad data often has a flattening effect, smoothing out interesting differences in opinion or experience. If your survey report feels bland, don’t blame the analyst. It’s okay to question the data (and survey designer).

As a final recommendation, I would encourage the Insights Association to launch a research-on-research initiative where buyers conduct post-mortems and submit them for study. Client names and revenues can be redacted. The question is whether the survey data provided a positive impact. The IA’s data quality benchmarking initiative is one thing, but I suspect it’s not enough. Anything that helps us understand impact and ROI seems like a good thing.

Thanks for reading.

CW