Customer research analysis: great questions for JTBD that drive deeper customer insight

Customer research analysis becomes transformative when you ask the right great questions for JTBD interviews.

To understand what job your customers are trying to get done, I never settle for surface-level feedback—it’s the real motivations and struggles hiding underneath that change the game.

Conversational surveys with AI follow-ups dig deeper than any traditional form. Let’s get into how to do it right.

Understanding the jobs-to-be-done framework

The jobs-to-be-done (JTBD) framework is all about grasping what your customers are actually trying to accomplish. It’s not just about features—they don’t buy products or services, they hire them to do a functional job, to make them feel a certain way (emotional job), or to impact how they’re seen by others (social job).

That insight changes everything: customers “hiring” a solution means there’s a purpose at work. They need help getting somewhere—efficiently, emotionally, or socially—so discovering those whys brings you closer to meaningful improvements.

Struggle moments are where the gold lies. When people hit friction, wrestle with awkward workarounds, or just wish things were better, those details reveal the true job to be done. Traditional surveys? They usually skim the surface, missing these nuanced, context-rich moments because they can’t probe the “why,” the frustration, or what people really want underneath. That’s why old-school forms rarely deliver JTBD-level clarity.

Conversational, AI-powered surveys change this equation—and recent research proves it. Organizations using generative AI in their customer workflows have seen customer satisfaction scores jump by nearly 20% and witnessed conversations that produce richer, more relevant answers that would otherwise remain hidden in the usual survey grind. [2] [3]

Essential JTBD questions for customer research

Getting to the heart of your customers’ experience means asking the right questions, tailored to your research intent. Here’s how I organize mine for maximum insight:

Timeline questions help paint the context and stages of their journey:

• When did you first realize you needed a solution?

• What triggered your search for something better?

• What were you doing right before you started looking?

• How long did you wait before starting your search?

Mapping the journey like this exposes where pain and urgency first show up.

Motivation questions dig for the real “why”—the driver under the driver:

• What made this problem worth solving now?

• What would happen if you didn’t solve this?

• How were things before you addressed it?

• Why didn’t you solve it earlier?

People’s answers often reveal surprising priorities or underlying anxieties.

Alternative questions illuminate how they’ve coped and who you’re really competing against (hint: it’s often not just other products):

• What else have you tried to get this job done?

• How are you solving this problem today?

• What’s the closest thing you’ve used in the past?

• If our solution didn’t exist, what would you use instead?

Alternatives are sometimes manual hacks, spreadsheets, or even doing nothing. Knowing this changes positioning—and product development—entirely.

I’ve found that these questions give so much more when you can ask AI-driven follow-ups based on what respondents say. That’s how you steer away from robotic answers and into surprising discoveries. If you want to instantly generate a tailored set of JTBD survey questions, you can try out the AI survey builder to speed things up.

How AI follow-ups uncover deeper customer insights

Traditional static surveys are like locked doors—you only see what’s on the surface. But this is where automatic AI-powered follow-up questions shine for deeper customer research analysis. Conversational surveys can adapt, probe, and clarify in real time, allowing you to uncover context and nuance that static forms miss. In fact, a study with 600 participants found that AI-driven conversational surveys delivered significantly higher engagement and far better quality responses than forms—more informativeness, relevance, and specificity. [3]

Here are a few AI-based analysis prompts I rely on to take survey responses further:

Probing for emotional drivers: Often, customers start by describing practical, functional reasons. But dig a little, and the true motivators—fear, aspiration, pride—bubble up.

When the customer mentions a problem, ask why this specific issue matters to them personally. Probe for emotional impact and consequences they're trying to avoid.

Uncovering workarounds: Workarounds or cobbled-together solutions expose unmet needs and friction with the status quo. AI detects these signals and digs in where it matters most.

If they mention using multiple tools or manual processes, ask specifically what's frustrating about their current approach and what their ideal solution would do differently.

Understanding decision criteria: When you know how a customer chose your solution, you spot what makes you uniquely valuable—or vulnerable.

When they describe looking for solutions, ask what specific features or capabilities they compared and which factors ultimately influenced their decision.

AI doesn’t just listen; it adapts in real time to responses, making these probing questions as seamless as a skilled human interviewer. Want to enable this at scale? Learn more about automating AI follow-ups here.

Validating product-market fit through conversational surveys

You’ll know you’ve achieved product-market fit (PMF) when customers say they can’t imagine life without your solution. But “fit” isn’t a binary switch—it’s a spectrum, and the signals are sometimes subtle. Through my experience, I focus on two key PMF indicators: desperation for your solution and positive word-of-mouth.

Value perception questions reveal if customers truly get (and feel) meaningful value. Ask things like:

• How would you feel if you could no longer use [product]?

• What's the main benefit you've experienced?

• What’s changed for you since adopting our product?

• What would you miss most if it disappeared?

If customers express loss, disruption, or anxiety, you’re on the right path.

Recommendation questions: Are customers so excited that they share your product with others? This is the ultimate “fit” signal.

• Have you recommended this to anyone?

• How would you describe this to a colleague?

• What kind of person do you think would benefit most?

• When was the last time you talked about us with someone else?

Don’t just tally “yes” or “no.” Ask for details—what did they say, to whom, and why? These qualitative signals frequently predict sustainable growth.

Follow-up questions make all the difference, surfacing context and motivations behind initial answers. To speed up product-market fit analysis, try using AI-driven response analysis—it surfaces PMF themes fast, so your team can spot signal amid the noise and act confidently.

Making JTBD research actionable with conversational surveys

Great customer research is only valuable if you can act. Conversational surveys—not forms—are the missing link. They feel like interviews, deliver richer insights, and now, with AI, they’re scalable. Here’s how I see it:

Benefits: You get higher completion rates, substantially richer and actionable answers, plus AI-driven summaries and chat-based insights at your fingertips. It’s no wonder AI-powered customer research raises retention rates by up to 15% compared to traditional methods.[2]

If you want to try JTBD research the easy way, the AI survey generator can turn your goal—“I want to map the jobs my users are hiring us for”—into a tailored survey in seconds, including the probing logic you need for actionable answers.

Once you get the first round of responses, head into the AI survey editor to refine, clarify, or add new questions without the hassle of manual updates. Changes are as simple as chatting your feedback—AI takes care of the rest. It’s the follow-up logic that turns these surveys into two-way, insight-rich conversations you just can’t get with a static form.

When you’re ready to see what your customers are really thinking and why, use a conversational survey instead of guessing.

Create your own survey and start uncovering the jobs your customers are truly hiring you for right now.

Ready to discover what job your customers are really hiring you for?

Transform your understanding by making every customer conversation count. With a conversational approach to JTBD research, you’ll finally see the motivations, pain points, and priorities that drive true loyalty and growth. Create your own survey and put these insights to work today.