ESOMAR’s 2018 Global Market Research Report notes that only 15% of research spend was dedicated to qualitative methods last year. Qualitative research undeniably provides context and detail to data that simply cannot be captured through surveys alone and should be considered an essential asset to any effective market research strategy. So why have smart teams historically shied away from running it? 

The 4 Major Challenges of Traditional Qualitative Research: 

  1. In-person travel time and cost are high for participants and researchers.  
  2. Small sample sizes lead to an increased potential for sample bias.  
  3. Running the research is intensive and requires the coordination of many parties. 

Despite these challenges, most researchers agree that qualitative methods provide a value entirely unique from quantitative methods. Let’s dive deeper into what makes qualitative research so essential

  • Qualitative research provides space for individualized consumer expression that cannot be found in a survey. As convenient as it is, survey software limits the participants’ responses based on the researcher’s framework of questions.  
  • Qualitative research provides a more robust view of the consumer and their experience. There is often a difference between what consumers think they want and what they ultimately choose in a natural setting. Essentially, real purchase decisions are not made on a Likert scale. 
  • Qualitative methods have a tendency toward organic interaction and often aid in the discovery of customer use cases, jobs-to-be-done, and emotions in a way that highly structured research cannot.  

The ability to make quick decisions based on quality insight is a major differentiator in the success of modern businesses, tasking today’s research teams with finding more agile methods of running and analyzing in-depth qual. Fortunately, our industry is making strides in supplying researchers with agile qualitative solutions.  

Fuel Cycle’s Qualitative Research Toolkit Includes:  

  • Text analytics: using machine learning, text is analyzed, categorized, and tagged in real-time to provide summaries of frequent topics and sentiment analysis around themes of interest.  
  • Computer vision: using machine learning, photos and videos are analyzed for objects, emotions, and meaningful text. We can identify landmarks of interest in near real-time.  
  • Mobile-first video interviewing: using consumers’ smartphones, we’re able to conduct video interviews on the fly, wherever respondents are—whether that’s in a grocery store, at their desk, or at an event.  

If you winced at the mention of machine learning, we get it. If your concern is over its accuracy, remember that perfect certainty is virtually impossible to achieve even when we rely on human judgment and sample. While the current state of automation doesn’t offer perfect certainty, it does enable more scalable and cost-efficient research while producing mostly accurate data. 

While technology continues to evolve and catch up to meet the needs of the modern researcher, the cost-benefits of adopting early are clear. Through machine learning, insights teams and organizations can generate results in half the time and amplify their qualitative research impact. 

To learn more about Fuel Cycle’s qual-focused applications and integrations, check out the FCX Marketplace.