Question 1-47:
In your experience, what percentage of consumers who say "they'll definitely purchase" actually follow through?
Question 2-47:
How many times have you launched a product that tested well in research, only to see it underperform in market?
Did you actually read those questions, or skim them? Maybe opened another browser tab? Because that's exactly what's happening to your market research right now.
You spent three months on research for a new product. The numbers looked great—78% purchase intent, glowing feedback, respondents saying it's "exactly what they needed." You greenlit the launch, felt confident in the data…
Now it's been six weeks and the product has been sitting on shelves. And those same consumers who said they'd buy it? They're walking right past it.
Here’s the truth: Surveys ask people to predict their own behavior in situations they haven’t experienced. It's like asking someone who doesn’t drink coffee whether they'd order it black or with cream. They'll give you an answer—but that answer is a guess, filtered through what they think they should say and completely disconnected from the split-second reality of actual decision-making.
Research from Harvard Business School shows that 95% of purchase decisions happen in the subconscious mind, driven by emotional responses that occur in as little as 0.3 seconds. But surveys operate in an entirely different reality, one where people are sitting at their laptops, coffee in hand, carefully considering each question.
The person who tells you they'd "definitely buy" the sustainable option?
They might reach for the cheaper alternative when they're standing in the grocery aisle faced with two disparate price points and a very real budget. The respondent who rates "brand reputation" as their top priority? They might scroll right past your carefully branded content but linger on the industry newcomer with fancy advertising.
This is the say-do gap in action: the difference between what people say they would do when asked, and what they actually do in the real world. And the forces widening this gap—from social desirability bias to the algorithms shaping your feed—are making surveys even less reliable.
Here's why predicting future behavior is hard, even for ourselves:
Social desirability bias makes people present idealized versions of themselves. The person who checks "I prefer brands with strong, sustainable values" in a survey is the same person who bought a viral criss-cross chair from Temu because it showed up in their feed at the right price. In surveys, people describe the shopper they aspire to be, not the one frantically comparing prices between similar brands.
Hypothetical scenarios feel risk-free. When there's no real purchase to make, no credit card to pull out, no shipping address to enter, people are generous with their hypothetical commitments. They'll say they'd pay $15 more for premium ingredients… until they're standing in the aisle and that $15 suddenly feels like $50.
Artificial contexts remove reality. Survey respondents know they're being evaluated. They're giving you their thoughtful, considered opinion. The kind of response they'd never actually have time for when they're scrolling between meetings, half-watching TV, or deciding whether to impulse-buy something at 1am because the ad promised next-day delivery.
The result? That expensive research that took three months to complete might be telling you a compelling story, but it's not the full picture.
Think about where your target audience makes buying decisions.
They're mid-scroll through their feed, bouncing between apps, surrounded by a dozen things competing for their attention. An ad catches their eye, or it doesn't. A product stops their thumb, or they keep scrolling.
Now think about your survey environment. A recruited panel member sits down at their computer. They know they're being asked to evaluate products. They're focused, deliberate, and completely removed from the messy, unpredictable reality of how they actually spend time online.
These environments aren't just different. They create fundamentally different mental states. Survey respondents have the luxury of time to weigh options and rationalize choices. But that careful consideration doesn't exist when someone's quickly scrolling through their feed, half-distracted. You can't recreate that scrolling mindset in a survey, no matter how well you design the questions.
Testing in the wild takes a real-world approach, observing what people actually do in the digital environments where they naturally spend time and make decisions.
No surveys. No artificial contexts. No one knows they're being studied. Just real consumers encountering ideas, concepts, and products in their everyday feeds.
This approach captures something surveys never can: real consumer behavior. When someone stops scrolling, clicks, shares, or ignores content, they're revealing their true preferences through actions—not through filtered, socially acceptable responses to hypothetical questions or scenarios.
The data isn't distorted by what people think they should say. It's not biased by artificial framing. It's not filtered through respondent fatigue or the pressure to provide "helpful" answers. It's just the raw truth of what really gets their attention and what gets ignored.
See real actions instead of predictions
Traditional research tests an average of 10 concepts with a few hundred paid respondents. Orchard analyzes behavioral data from millions of real interactions, evaluating 60+ concepts simultaneously. Instead of asking "Would you consider this product?" you learn what people actually do when they encounter it in their feeds.
Get answers 10x faster for a fraction of the cost
Traditional research is expensive because it requires recruiting paid panels, scheduling sessions, and processing individual responses. By the time insights arrive—often months later—consumer behavior has already shifted. Orchard delivers actionable results in just two weeks, reaching both broad and niche audiences at a fraction of the cost.
Stay ahead with continuous learning
Surveys give you a snapshot that's outdated the moment it's complete. Testing in the wild creates a continuous feedback loop, showing you what's working today and what's emerging tomorrow. You can spot trends as they develop, adjust strategy as consumer behavior evolves, and keep testing as markets shift.
The shift from survey-based research to real-world testing is transforming how we understand consumers—taking us from what people say to what they actually do.
Qualitative methods still have value for uncovering the “why” behind behavior. But when it comes to understanding what works, what stops the scroll, what drives action—behavioral data beats predictions every time.
The person who rates your concept highly in a survey might scroll right past it in their feed. The positioning that tests well in a focus group might fall flat in the real world. The packaging design that wins in research might get ignored on a crowded shelf. Because the controlled environment where you tested it bears no resemblance to the messy, unpredictable, split-second reality of actual consumer decision-making.
But the concept that actually makes people stop, engage, and convert in their natural environment? That's the signal you can trust. That's the data that translates into real-world results.