Best of Both Worlds: Qualitative Depth with Quantitative Sample Size
Striking the right balance between qualitative depth and quantitative sample size is a delicate dance that often leaves the experienced market researcher in search of better tools for a perfect solution. While traditional qualitative research methods, such as focus groups, use exploratory and probing questions to uncover a nuanced answer, those rich verbatims are meaningful but derived from a relatively small sample size. On the other hand, traditional quantitative methods, such as surveys, may gather large numbers of answers but lack the depth of understanding the qualitative verbatim offers.
But what if you had access to a tool that blended both approaches? What if there were a way to get that rich understanding qualitative research provides and gain confidence in the representative scale provided by quantitative methods? That would be qual at the scale of quant and could be a game changer for how brands move forward.
A blend of qualitative and quantitative research is certainly nothing new in the consumer insights landscape. It’s the answer to the old conundrum of “Fast, good and cheap: pick two.” When it comes to fast, quantitative certainly delivers. When it comes to good, qualitative is the undisputed winner. But when it comes to adding the third stool of the perfect situation, cheap may seem elusive. But with the realities of compressed timelines and shrinking budgets, any solution that can still deliver great insights at speed and do so at less expense is a welcome option.
Game-Changing Technology
The answer, of course, lies in the skilled application of artificial intelligence (AI), which can be used to process large amounts of qualitative data within seconds. Advances in natural language processing (NLP) accuracy mean this intelligence is getting better at parsing human intention to deliver summaries that don’t miss important nuances of meaning hidden in language. AI can be integrated into research processes to facilitate a qualitative conversation with a larger number of people than traditional focus groups and simultaneously allow for statistical relevance. But with the proliferation of tools claiming to use AI, the careful researcher is left to consider the next best choice. Before integrating new approaches and technologies into the research mix, let’s take a look at what technology can offer.
Blending Qual and Quant
As a moderator in a qualitative session, this simple response, “I love this ad!” with eyes rolled, is easily understood as a sarcastic response. If a non-human AI program processes this, it may miss the intended irony. How, then, can we ensure that the technology we use is delivering the confidence we need by adding a larger sample size without compromising the integrity of the meaningful feedback? As tools are evaluated, a few considerations should be made about how this technology can affect the quality of research outcomes. When we bring scalability to our efforts, we want to make sure what we are amplifying or expanding is based on high-value sources.
Synthetic Respondents v. Real Responses
Open-ended questions are the basis of discovery in qualitative research methodologies. When consumers offer up their genuine opinions about a given product, service or brand, each response is valuable for its uniqueness. Some AI tools are already introducing synthetic respondents at this stage - querying non-human entities for these responses. They use an amalgamation of past human responses to best approximate the most human-like potential response. But this application of generative AI may simply be generating more of what we know and not generating potential new insights. Going back to the original purpose of qualitative work, these methodologies are used when humans explore something that is unknown and use exploratory questions to discover.
Instead, AI can be used to evaluate unique human responses and categorize them into general themes as they emerge. Grouping responses can be a great way to understand larger topics that can be further queried. Tools like wisequeryAI formulate new questions within these themes to probe for more specificity on the subject but to a larger audience to gain statistical validation on a qualitatively sourced topic. Strong sentiment analysis protocols layer over natural language processing modules to effectively identify intent even from nuanced participant feedback.
Increasing Speed to Insights
As brands look to answer important questions, they need data to inform their decisions. But the speed at which the market changes is increasing. Blending a qualitative approach with quantitative methodologies can bring important consumer data to stakeholders in time to create a competitive advantage. This speed of market change means AI holds special promise for bringing meaningful insights through validation and analysis phases quickly.
When looking at AI-enabled market research tools, the benefit of shorter time frames is significant. While the scope of work can vary greatly per project, many projects can be completed within 48 hours of the first session. Consumer insights teams can use this time to further socialize key findings on their team, consider new possibilities and ultimately improve outcomes.
How AI Brings Qualitative and Quantitative Worlds Together
How AI is used is incredibly important for market research professionals as we seek to understand changing consumer behavior - the deeper whys behind these shifts, beliefs, preferences and aspirations. Starting with a skilled moderator, qualitative responses can provide meaningful direction for pairing AI tools to bring research projects to completion. With a human connection at the foundation of the process, AI will continue to lend a scale and speed to work to maximize brand benefit. Getting the best of both worlds means delivering a qualitative depth of human understanding with the confidence that comes with a larger sample size - all at the speed of business.
Regardless of the methodologies in play, brands trust our expert Athena Brand Wisdom team. Our consultative approach evaluates each business challenge from a logical framework with added layers of cultural context. This experience means each project is ideally set up to leverage the power of traditional approaches, both quant and qual, with the exponential benefits of artificial intelligence.
