As much as I love research and survey results, I've increasingly become aware that while quantitative research using surveys provides important data to help executives make decisions, it has certain limitations and chronic issues that are becoming more pronounced over time.
These issues are increasingly resulting in inaccurate data, unnecessary ad spend, and lost sales.
In comes AI market research. From quickly analyzing qualitative customer interviews to conducting quantitative experiments with synthetic respondents, executives can now access less biased, more accurate research insights in a fraction of the time and for one-fifth of the cost.
The result? Messaging, targeting, and channel choices can be more accurate, wasted ad spend is prevented, and products can get to market faster and more profitably.
So how does it work? Here's a deep dive into how AI causal research tools solve the problems of traditional survey methodology.
Survey Pain Points, and How AI Solves Them
Surveys can take months to complete
A good research process includes a discovery phase and multiple stakeholders aligning on methodologies, questions, and outcomes. Once the survey is developed, multiple email or text outreaches are required to reach statistically valid volume, and then analysis takes time to translate into a report. In my experience, this takes four to six weeks at a minimum and often drags out to three months when stakeholder scheduling is hard.
AI Benefit: With an AI research platform that uses synthetic respondents, this process narrows to a few days, or a few weeks if client schedules are busy. An experienced marketer or researcher who knows the company's goals can get helpful data in as little as 30 minutes.
Non-AI research is historical, not predictive
Traditional surveys measure what happened at one point in time and don't necessarily explain what will happen in the future.
AI Benefit: With AI causal experiments that deliver quantitative data, you can capture the most up-to-date data instantly, in real time.
People often lie on surveys
While it's mostly unintentional, many people answer survey questions the way they think they should rather than what they would actually do in reality.
AI Benefit: AI causal research uses digital twins created to respond based on what humans would actually do, not their emotional responses, eliminating the "say, do" gap.
Survey fatigue is real
People only have so much time to take surveys. Send them too many, or make them too long, and they ignore them or drop out halfway through.
AI Benefit: Synthetic respondents don't get fatigued and can be queried endlessly.
Human survey percentages are often inaccurate
Researchers work hard to find quality respondents and eliminate bias, but respondents are often people like unemployed college students who need the reimbursement and have time to participate. This skews data toward that demographic unless it's weighted appropriately.
AI Benefit: AI experiments use a formula to even out respondents across demographics for a more accurate representation of the actual population.
Some people are afraid to take surveys
There are topics people are uncomfortable sharing views on. Those with political views outside the current vogue position, for example, may avoid telephone polls for fear of being singled out.
AI Benefit: Synthetic respondents have no fear of retribution, so results can predict what people will actually do.
Human studies can harm participants
In clinical research, a "do no harm" philosophy prevents randomized controlled tests that would hurt participants. Studying whether excessive negative social media harms teenagers' mental health, for instance, would be unethical to test directly.
AI Benefit: Because synthetic respondents cannot experience harm, this barrier to insight is eliminated.
It's hard to find quality survey respondents
As content and communication have exploded, researchers increasingly compete for attention, making quality respondents harder to find.
AI Benefit: Synthetic respondents are always available.
The Human-AI Balance
When you consider that platforms using AI-generated synthetic respondents solve all these pain points, it's clear this is the best path forward for researchers and marketers.
That said, as with most AI tools at this point in time, caution must be taken by using a collaborative human-AI approach. Humans must have the knowledge to use the tools properly and verify the insights. They must also use tools built with exceptional care to ensure data integrity and monitor shifts in the AI models that may impact insights.
Curious what AI-powered research could do for you?
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