Customer needs and wants analysis: best questions for customer segmentation that reveal actionable insights

Understanding customer needs and wants analysis starts with asking the right questions—but traditional surveys barely scratch the surface.

Segmenting customers by their actual needs, jobs-to-be-done, and usage patterns reveals insights that demographics miss.

Conversational surveys with AI follow-ups capture the nuance behind each response, making segmentation more accurate and actionable.

Why demographic segmentation misses the real story

Age, location, and income tell you who your customers are—not why they buy. While these demographics are easy to collect, they ignore the real reasons customers choose, stick with, or leave a product. This is why needs-based segmentation is now a must for customer-focused teams.

With demographics, you group people superficially. With needs-based segments, you discover the actual drivers of behavior. Surveys show that 85% of consumers want brands to understand their needs and expectations—not just their age or region. [2]

Jobs-to-be-Done (JTBD): This approach asks what specific outcome your customer is “hiring” your product to achieve. Instead of “Who is buying?” JTBD asks “What job are they trying to get done?” This unlocks product and experience insights that surface only when you see the world from the customer’s real context.

Usage frequency patterns: How often and intensely customers use your product often says more about their needs than any static trait. Power users, occasional dippers, and dormant accounts all need different engagement and messaging strategies.

Role in decision process: Your customer might be an end user, an administrator, or an executive sponsor. Each role comes with different needs, pain points, and priorities.

Layering these dimensions reveals what truly matters to each segment—letting you move beyond guesswork into genuine understanding.

Best questions for segmenting by customer needs

Let’s make it practical. Rather than guessing, I lean on three proven question frameworks you can mix and match in any AI-powered survey. You can use the AI survey generator to create and refine these question sets for your audience.

• Role-based questions:

• What is your main responsibility when using our product?

• Do you make decisions, influence purchases, or simply use what’s given?

• Who else is involved in choosing this solution at your organization?

• How does your team rely on your input about tools like ours?

These questions pinpoint where each respondent fits in your buying journey. You’ll discover if you’re speaking to a decision maker, a power user, or an internal influencer—crucial for customizing sales and onboarding flows.

• Jobs-to-be-Done (JTBD) questions:

• What problem or task led you to try our product?

• What outcome would make you say using our product was a success?

• What other options did you consider for this job?

• What’s missing or frustrating about your current approach?

JTBD questions unlock what your customer values most—not just features, but the results they’re after. These insights drive product roadmap choices that actually resonate with real users.

• Frequency/intensity questions:

• How often do you use our product (daily, weekly, monthly)?

• Which features do you rely on most?

• What slows you down or prevents more frequent use?

• Have you ever stopped using the product? If so, what caused that?

This line of questioning reveals varying needs across casual, regular, and power users. By tailoring follow-up questions based on frequency, you segment for growth and retention at a much deeper level. For more ideas, explore the customizable AI survey builder.

AI probing questions that reveal hidden needs

Initial answers rarely tell the whole story. That’s where AI-powered follow-ups shine—diving deeper to uncover pain points, workarounds, or unspoken expectations.

Here’s how probing prompts might work in the wild:

Pain point follow-up

Your response mentioned that “setup was difficult.” Can you walk me through what made that challenging and how you got around it?

Use case exploration

You said you use the dashboard for reporting. How often do you run these reports—and are there other methods you’ve tried in the past?

Satisfaction/dissatisfaction probe

You shared you’re satisfied with our notification system. Was there a specific moment or feature that made you feel this way?

This level of interaction comes standard with automatic AI follow-up questions. AI interviewers adapt on the fly, enabling you to surface needs you’d otherwise miss.

These follow-ups make the survey feel more like a conversation, turning a ‘form’ into a genuine back-and-forth that increases both the depth and honesty of your data. That’s the magic of a true conversational survey.

Filtering conversations and running parallel analysis by segment

Once survey responses start flowing, the power of segmentation comes alive in the analysis phase. With AI-driven tools, I can quickly filter conversations by any dimension I’ve set up—whether that’s job-to-be-done, frequency of use, or customer role.

One of my favorite techniques is setting up multiple parallel analysis chats, each focused on a key segment—say, power users, occasional users, and churned customers. In each chat, I dive into unique patterns and pain points, letting insights emerge in context. This is exactly what AI survey response analysis enables. Here are some prompts that help turn raw data into instant insight:

Compare needs across segments

How do the jobs-to-be-done differ between frequent and occasional users?

Identify unique pain points per segment

What frustrations are unique to decision makers versus end users?

Find common threads across all segments

What themes or suggestions appear across every segment, regardless of frequency or role?

Each analysis chat maintains its own focus and context, making it easy to share findings with product or marketing teams—and to actually act on what you learn.

Turn segmentation insights into action

Segmented needs analysis isn’t just for reports—it’s for building a product people truly want. Here’s how teams turn these insights into results that matter:

• Personalized onboarding: Guide each role or segment to the value they care about most.

• Targeted features: Prioritize new releases based on what distinct user types lack today.

• Segment-specific messaging: Speak directly to power users versus new adopters—or to influencers versus decision makers.

• Retention campaigns: Re-engage inactive customers based on the specific blockers they face.

If you’re not segmenting by needs, you’re missing out on actionable insights that drive higher satisfaction, loyalty, and purchase rates. With more than 80% of consumers more likely to buy from brands that personalize their experience, the stakes couldn’t be clearer. [3]

Start transforming your understanding—create your own survey with segment-driven questions and let AI bring genuine customer needs to the surface.