Traditional qualitative research was a shrinking and specialized field not too long ago. One of the reasons was because – in the late 90s and early 2000s – it became hard to make a business case for a $100K qualitative study when big data was making its appearance. Big data enabled large numbers of consumers to be tied to percentages, making it easier for decision-makers to justify their plans. It’s easier to go to senior leadership with percentages based on 10,00 people compared to one with a bunch of emotions and only 12 people.
The market and technology weren’t keeping pace with what the world and companies wanted to see. People wanted easier and faster (and, let’s face it, cheaper) qual, and the perception was this wasn’t doable virtually.
When the pandemic hit, forcing everyone behind screens, it became clear that virtual qual wasn’t just a band-aid fix for traditional focus groups; in some cases, researchers found that it worked better than in-person for specific needs. Like so many industries, the pandemic made us adapt to our new environments, and innovations in technology and market research pushed the boundaries of virtual qual.
We’re of the mindset that the future of qual has never been brighter, it just looks very different than it did 5 or 10 years ago.
The future of qual
Qualitative research’s future depends on four key areas:
- Work-life balance of it all (traditional vs. virtual vs. hybrid)
- Technology facilitates access and speed
- How respondents have changed
- Artificial intelligence’s role in analysis and creating more studies that have both a qual and quant lens
The future of qual might appear to be totally virtual given the influx of options in the space. But we think a hybrid between in-person and virtual is here to stay. Our qual team is already back in-facility testing new products with consumers and moderating interviews.
Technology is a key reason for qual’s proliferation. The tech adopted during the pandemic made us realise you can have better line of sight into consumers’ lives, spend more time with them, and expand your reach all at the same time. Additionally, the combined power of qual and quant is starting to make waves in the market since real-time quant data can impact the direction of qual interviews, leading to better consumer understanding.
But it isn’t just technology that’s changed. Respondents are more comfortable on digital devices (i.e., computers, tablets, smartphones), making it easier to get more qualitative research through things like mobile safaris, or at-home interviews.
Finally, there are so many tools out there right now to make qual research quicker. Artificial intelligence (AI) seems to be the optimal way to speed up qual analysis and to help further the brainstorming process during your innovation lifecycles.
Traditional vs virtual qual vs hybrid
Before diving too deep into the future of qualitative research, let’s distinguish the differences between traditional and virtual qual.
Traditional qual is done in person: focus groups, shop-a-longs, and product testing at a facility. Virtual qual is, as it sounds, anything done through the internet or aided by technology where a moderator isn’t physically present with the respondent.
The depth of traditional qual is a big reason why some experts still think this is the preferred method. It’s easier to read body language and capture micro-queues. For example, you might miss those physical demonstrations of boredom, intrigue, excitement, anger, etc., when you can only read people’s body language from the shoulders up. Researchers understandably worry that missed queues might result in missed opportunities to probe or question respondents, resulting in fewer discoveries or insights.
To play devil’s advocate, some supporters of virtual qual believe that certain needs are better handled through virtual research because they should mimic what we do in our day-to-day lives. For example, if you’re product testing something, sending your product to respondents’ homes enables them to use it naturally, in their environment, without a moderator’s watchful eye. This leads to a better understanding of the product since the moderator isn’t there and can’t accidentally give queues about a new feature the respondent might have missed.
Eliminating moderator bias from things like product testing or shop-alongs is one of the advantages of virtual qual. Additionally, you often get access to more people, or a more diverse group of people, because respondents no longer have to come to you.
It’s worth noting that while you can now do close to 95% of qualitative interviews virtually, there are a few instances you should never consider for virtual qual. Sensory experiences, medical and other sensitive moments where you need to read someone to understand when to push a little deeper or to pull back. These are areas where respecting the respondent is of the utmost importance and are more challenging to do through a computer screen.
Similarly to many of our working lives, a hybrid model should become the norm.
Technology’s role in qual
A key component of qualitative research is empathy. How do you keep empathy in the tech world while also moving at the speed of consumers? It’s a reasonable question.
Luckily, innovation in qualitative methods (i.e., online diaries, mobile ethnographies, virtual product testing) exploded out of the pandemic and gave us a better line of sight into consumers’ lives. Additionally, you can spend more time with people than you ever had before because no one has to leave their living room couch, and people have become more willing to be on mobile devices and share details of their lives.
Another benefit of technology in qual is expanding your reach. There were access issues before the pandemic. Not everyone had a stable internet connection. Not everyone could travel to major urban centers, where most facilities are located, to participate in these in-depth studies. Now, thanks to online boards, chat rooms, video chats, etc., a single mother living in a rural area can participate in your studies.
(Note: Technically, there are still access issues. High-speed internet and computers don’t have 100% penetration. But we’re moving closer to giving everyone a voice.)
Qual and quant working together: a use case for the power of tech
The power of tech in qual is not only about expanding reach, accessing diverse global respondents, and retaining empathy with those people. A huge benefit of integrating research technology is in combining qual and quant more strategically.
At Dig Insights, the majority of our custom projects are a fusion of qual and quant. It’s about building projects in tandem to explain the why behind the numbers, building on the findings from the quant studies. In many ways, we’ve been able to do this easily because of technology.
A Real-Life Example:
We ran a study from kick-off, to execution, to delivery of the final report. It was a combined qual and quant study. The study compared two potential name options for a brand. As we ran the qual study, we had our Upsiide dashboard up. As the qual interviews happened, our facilitators watched the quant responses come in, allowing them to adapt the discussions based on which concepts were winning out with their target market. It all happened in real time. After the interviews, our qual team regrouped with the quant team, discussed the customer story, and completed the report.
All of this in just two weeks.
We probably couldn’t have done any of this 6-8 months ago. And there is no way this timeframe was doable before the pandemic. It’s the perfect example of how much technology moved the discipline of market research forwards.
How respondents have changed
Many differences between in-person and virtual qual were outlined above, and we’ve alluded to the fact that the people taking part in these interviews have changed too. It’s an important part of qual research to consider.
The pandemic forced everyone to communicate significantly more from behind a camera. Sure, FaceTime and other video apps existed, but they weren’t as integral to our everyday lives as they are now. Before the pandemic, the maximum daily users of Zoom reached 10 million. By April 2022 (not even two months into lockdown), Zoom surpassed 300 million daily users. Familiarity with video chats reached the masses, and the awkwardness of virtual qual dissipated as well.
A symptom of this transition to virtual is that people are often more comfortable from behind a screen. Anecdotally, younger generations seem more comfortable online than they are in person. And the stats seem to back that up. The younger the generation, the more time they spend online. Millennials spend 8.5 hours engaging with content online. But compared to Gen Z, that’s nothing – that number jumps to a whopping 10.6 hours per day for them. Virtual qual might be the easiest way to engage a younger demographic.
Some people are more likely to go deep or get vulnerable in a virtual setting, too. During in-person focus groups, we’ve found that some people’s defense systems are up because you’re out in the world. The online/virtual world allows you to let your guard down – to be a bit more blunt or outspoken than you’d be when sitting in a room full of people. If being behind a screen helps consumers open up more than in-person, it’s vital to find ways of incorporating it into the research process.
Mobile safari example
Mobile Safaris are a great example of how consumers and qualitative research work virtually. With mobile safaris, a brand looks to understad consumers’ insights. This could be breakthrough at shelf, resonance, packaging, or shopability (i.e., organization, ambiance, findability).
So, instead of following someone around a grocery store, watching them shop, taking notes, and asking them questions, you can get them to do their shopping as they normally would. Before they leave, they check an app that has questions for them to answer (i.e., did they notice the new communications – signage, screens, directions?). You can add tasks, too – you might ask the respondent to take a picture of something new or recall the messages on a promotional sign. They can also upload selfie interviews. And you can follow up with some of the respondents virtually to ask more detailed questions.
This approach to a shop-along saves a lot of time and helps remove moderator bias. No one is watching over a respondent’s shoulder, and they don’t need to wonder what the moderator/facilitator is doing or thinking while they shop. Plus, some people still aren’t comfortable having someone close to them in public. Everyone has a different comfort level post-pandemic. So, once again, you can get more people to participate because you don’t need someone there watching them, which saves time and money and increases the number of respondents.
The AI of it all
Quantifying qualitative feedback
We’re using AI to analyze open-ends through summarization or emotional sentiment tracking. Tools like Canvs AI are great partners of ours. Their tech helps us understand the emotional responses to open-ended questions in quant studies. A good example of how Canvs AI helps us quantify emotional responses is in our inflation report. Analyzing open-ends helped us understand there was (obviously) quite a lot of negativity surrounding how inflation affected consumers. But one thing we didn’t expect was the positivity people felt as a result of the economic downturn; they were proud of themselves for managing to cut back on non-essentials and create better budgets.
Moderating conversation
Chatbots can be used in a few different ways in qual research – by becoming more of a moderator in a chat board, asking questions and following up with answers from the respondents. You can also use AI to prompt respondents to go deeper, avoiding the potential of receiving a blank open-ended question. Asking them to elaborate on why they said they didn’t like a product or offer.
For example, when trying to test new products to bring into the market, a respondent said they liked a spicy chicken sandwich. We then have an open-end that asks, “Why did you say you like the spicy chicken sandwich concept?” and the respondent says, “The bun looks yummy.” That doesn’t give us a lot of information about why they liked the new offering. So, the AI might chime in and say, “The bun looks yummy, but I want to know more about the sandwich! What kind of chicken is it? Is the spice level adjustable?”
This allows us to get more detailed information on what a respondent thinks about the products they’re reviewing.
Generating new ideas
AI means more than just analysis. When you push the boundaries, you can use AI for idea generation.
To create AI-generated ideas, you start with a large language model that understands natural language the way a human might. But this language model has read a curated version of half the internet, meaning it understands trends and preferences, so it can combine ideas across different categories to suggest new innovations automatically.
Innovation workshops are the perfect place for AI-generated ideas. Use AI to help you brainstorm. But only after you’ve interviewed relevant stakeholders to see what’s important. What direction do you want to go? What business plan/strategy does this align to? Are your consumers begging for you to be more socially conscious? Really anything that helps inform your innovation. Find your parameters and brainstorm a bit yourself. The more ideas you have and the more direction you can give the AI, the more likely it is to generate something completely new.
When testing our approach to AI with a client, our AI-generated ideas outperformed the majority of the ideas the client brought to the table, which goes to show that AI-generated ideas aren’t a fantasy but can actually help your innovation process.
2023 and Beyond
Qual itself isn’t changing. It’s all about understanding the consumer and finding empathy. But how we go about finding that is changing. Not entirely or all at once, no. The future of qual will remain a hybrid model – keeping traditional/in-person qual when it makes sense (sensory, product testing, delicate conversations) and leveraging technology for virtual qual to increase speed, reach, and quantity of respondents.
One of the ways to do that is by integrating qual into quant – finding ways to get more out of standard open ends through text and sentiment analysis and prompting respondents through AI chatbots to ensure you’re getting enough information in your quant studies.
Finally, AI will change how we analyze qualitative studies and ideate through the innovation lifecycle.
The combination of all these advancements makes qual’s future incredibly bright.
