Insights

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: 

Surveys can take months to complete: A good quality research process includes a discovery phase and multiple stakeholders aligning on the methodologies, questions and the outcomes needed. 

Once the survey is developed, multiple email or text outreaches are required to get enough volume to get statistically valid results. After that, the analysis takes time to complete and translate into a report. 

In my experience, this process takes four to six weeks at a minimum and often drags out to three months if it's hard to schedule meetings with stakeholders. 

AI Benefit: With an AI research platform that uses synthetic respondents, this process can be narrowed down to a few days or a few weeks if clients’ schedules are too busy to meet. 

With an AI quantitative research platform, someone who is an experienced marketer or researcher who knows the company’s goals and objectives, 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, one 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 answer rather than what they would actually do in reality.   

AI Benefit: Quantitative AI experiments eliminate this “say, do” gap because it removes the human emotion that biases human responses.

Survey fatigue is real: People only have so much time to take surveys and if you send them too many or they are too long, they ignore them or drop out half way through the survey.

AI Benefit: With AI quantitative experiments, synthetic respondents don’t get fatigued and can be queried endlessly. 

Human survey percentages are often inaccurate due to the type of people who take surveys: While researchers work hard to find good quality survey respondents and eliminate bias, it’s often respondents like unemployed college students who need the reimbursement money and have the time to take a survey. This leads to survey data that can be skewed towards that demographic. 

AI Benefit: AI experiments use a formula to even out respondents among various demographics to get a more accurate representation of the actual population. 

Some people are afraid to take surveys: There are some topics that people are uncomfortable sharing their views on in surveys. For example, those who have political views or support candidates that are outside the current vogue position, may not participate in telephone polls for fear of being singled out for retribution or put on a list to target by the opposite party.  

AI Benefit: Synthetic respondents have no fear of retribution so the experiment results can predict what they will do in real life. 

Human studies can harm participants: In clinical research, there is a “do no harm” philosophy that prevents researchers from conducting randomized controlled tests that would hurt one of the parties.  For example, if a researcher wanted to learn if social media was harming teenagers' mental health, they would consider it unethical to expose a group of teenagers to excessive amounts of negative social media content. 

AI Benefit: Considering that synthetic research respondents cannot experience harm, this concern and barrier to getting research insights is eliminated. 

It’s hard to find quality survey respondents: As the availability of content and communication has grown extensively, researchers are increasingly competing for attention, making it harder to find and get quality survey respondents. 

AI Benefit: Synthetic survey respondents are always available. 

In conclusion, when you consider that platforms using AI-generated synthetic respondents solve all these pain points, it’s clear that it’s the best path forward for researchers and marketers. 

That said, as with the use of 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 understand how to use the tools properly and verify the insights. 

They also must use tools built with exceptional care to ensure data integrity and monitor shifts in the AI models that may impact insights. 

Michelle Seay Baker, APR, is the founder of Creators Edge Strategy, a consulting agency specializing in hybrid-human AI-generated market research to optimize marketing strategies and business outcomes. If you would like to explore human-AI collaborative research for your company or organization, connect with Michelle by clicking on the "Find Out More" button below.