Every company that prioritizes customer centricity can't ignore 'empathizing with the customer.' And for good reason: gathering insights from your target audience improves the speed and quality of many decisions.
However, 'empathizing' is often seen as an expensive, challenging, and time-consuming factor. The temptation to not ask the customer or to reduce it to a mere checkbox 'for the record' is strong. Luckily, there's AI as a savior. Or is there? Or maybe there is after all?
'Empathizing' isn't so straightforward. Engaging with 'target audiences.' That's my job. Whether it's through an online survey or in 2.5-hour group discussions. By having conversations and asking questions, you try to gather insights valuable for making business decisions. Real experiences, frustrations, and needs that emerge in these conversations open doors to new ideas and provide direction. That's where the value lies. Yet, this 'empathizing' isn't so straightforward for many companies, especially concerning qualitative research. Several reasons contribute to this:
Qualitative research is often seen as 'n=1' and therefore not 'significant' or 'representative.' And that's true. Qualitative research isn't about significance, and you only speak with a small group of people. The challenge in such research is to invite your core target groups. By carefully choosing who you want to speak with, you gain sharper insights into where the opportunities lie.
Another hurdle is time and money. Qualitative research requires a significant investment, and when you can survey n=1,000 people faster for the same amount of money in an online survey, the choice is quickly made. Not an unreasonable thought. Here, it's important to be able to interpret the type of insights. Qualitative research has a different purpose than quantitative research. When there's truly no room for qualitative research, ensure at least that there are enough open-ended questions in your survey. This way, you immediately see if the survey is being taken seriously and you have more context for the numbers. It can also help to experience the value of qualitative input in a 'light' form.
A third significant obstacle is the visualization of qualitative data. Quantitative data are presented in numbers, diagrams, tables, and graphs: convenient! Everyone immediately sees – this is the story: 30% think this, 70% think that. So we choose this (somewhat exaggerated, of course). "Numbers are more pleasing to present in 'the board'," you often hear. And I understand that too. The challenge is to also choose visualizations, clear statements, and a 1-page Management Summary in conclusions and recommendations of qualitative insights. But a mindset shift is also necessary: we need to move away from the idea that quantitative research is 'better' than qualitative research. It's different, and each method has its own value.
But even quantitative research isn't always straightforward to conduct, while this type of research can be quick and relatively 'cheap.' The difficulty arises when it comes to B2B target groups. Because try finding 300 facility managers in hospitals responsible for coffee or the person who handles lease cars within SMEs – just to name a few. Let alone specialists. Those are often costly and time-consuming studies to conduct.
Empathizing with AI solutions.
Developments in market research are moving fast with the arrival of AI tools. How about 'on the spot' interviewing of synthetic personas mimicking reactions from your core customer? Or AI-supported questionnaires, where based on someone's answers, further questions are asked about the responses. And of course, AI data analyses where video conversations or texts are analyzed and reported. Asking questions to your target audience, probing for deeper answers, presenting ideas at an early stage, and conducting quantitative validations with 'one press of a button.' Okay, we're not quite there yet, but that's where we're heading. It will make a world of difference in time and money. The pace at which AI tools can assist market research is significant, but the step towards a fully automated approach seems premature. Understanding the context and interpreting the meaning behind the data is a skill that currently still relies on human expertise. What someone says and what someone means can be very subtle, and non-verbal communication cannot yet be fully captured by AI systems. And when AI takes on a central role in understanding customer needs, what remains of 'feeling' the customer experiences? Wasn't this precisely what could offer so much value? Not only by delving deeper with follow-up questions but also in the realm of sharing information with trust, creating the human dimension that makes you understand your customer more. From this understanding arise new solutions, new inspiration, and new energy that can drive marketing, communication, and product development. But perhaps this is too romantic a notion, and AI provides sufficient input, rendering the 'self-feeling' as a marketer or innovation manager no longer necessary. In my opinion, in 2024, it's still a matter for companies to navigate smartly between the possibilities of AI and the value of human insights.
And ChatGPT agrees with that, he just said.
Marjolein van Ballegooij