A well-crafted customer analysis template starts with asking the right questions—but the real insights come from understanding the 'why' behind each answer.
This guide gives you practical question sets, organized by analysis goals, with examples for both landing-page and in-product conversational surveys.
Questions for customer persona development
Persona development aims to capture the key characteristics, motivations, and behaviors that define distinct segments of your customers. These personas help you tailor everything from messaging to product features.
Demographics: "Which best describes your role or job title?"
Insight: Clarifies user context, guiding segment-specific strategies.Behaviors: "How often do you use products or services similar to ours?"
Insight: Reveals usage patterns and potential for repeat engagement.Goals: "What’s the biggest goal you hope to achieve by using our product?"
Insight: Surfaces key motivators for purchase and retention.Frustrations: "Describe a recent challenge you faced with a similar solution."
Insight: Points to unmet needs and pain points.
With AI-powered conversational surveys, follow-up questions automatically dive deeper. If someone says their goal is "saving time," the AI can instantly ask, "Can you share a specific scenario where time was lost?"—uncovering actionable details you’d never get with a static form.
Generate a customer persona survey exploring demographics, core goals, and typical frustrations. Include follow-up probing on each open-ended answer.
In-product surveys collect persona data by observing real user actions and behaviors—think segmenting based on feature usage, onboarding patterns, or churn risk. These embedded feedback points tap into live, contextual insights as users interact with your product.
Landing page surveys help you research audiences before they become users. These are perfect for understanding broader market segments, early adopter profiles, or new personas. Landing-page flows often capture less biased, more exploratory perspectives from potential customers before product adoption.
It’s not just theory. AI-driven conversational surveys can achieve 70-90% completion rates—versus just 10-30% for traditional forms—meaning you get more complete persona data, from more people, effortlessly [1].
Uncovering Jobs-to-be-Done through conversational questions
The Jobs-to-be-Done (JTBD) framework helps us understand why customers "hire" a product—what jobs, outcomes, or progress they’re actually looking for. Well-chosen JTBD questions cut through surface preferences to the heart of user motivation.
Main job: "What main problem do you hope our product will solve for you?"
Reveals: Core jobs and context of use.Previous attempts: "How have you tried to solve this before?"
Reveals: Pain of switching, alternatives compared.Desired outcome: "Describe what success looks like—how would you know our product did its job?"
Reveals: Underlying outcomes and customer criteria.Triggers: "What happened right before you started looking for a new solution?"
Reveals: Situational triggers driving urgency.
Specific’s AI can dynamically follow up: If a user answers, "We just want smoother project delivery," the AI asks, "What causes delays for you today?"—moving from generic goals to specific unmet needs with zero manual setup.
Question Type | Surface-level JTBD Insight | Deep JTBD Insight with AI |
|---|---|---|
Main job | "Manage tasks" | "Coordinate remote teams, reduce missed deadlines, and automate status updates" |
Previous attempts | "Used email" | "Tried three different project management tools, but each lacked mobile notifications and real-time collaboration" |
What makes this work? AI doesn’t just record answers—it spots repeating job themes across hundreds of responses and summarizes minority insights that matter. You’ll quickly know what truly motivates customer adoption.
Conversational JTBD question sets are easy to create; just describe your audience and focus:
Draft a JTBD survey for new SaaS users discovering our project management tool. Include follow-up prompts on pain points and desired outcomes.
Conversational AI surveys can yield 50-100x more responses than static forms for exploratory research like JTBD [2]. That means richer context, less effort, and faster learning loops.
Pricing analysis questions that reveal true willingness-to-pay
Pricing research requires more than just asking, “What would you pay?”—real willingness-to-pay is shaped by context, alternatives, and perceived value, making smart follow-ups essential.
Value perception: "On a scale of 1-10, how valuable do you find our product compared to alternatives?"
Budget fit: "What would make our product feel expensive or out of reach?"
Price threshold: "What is the maximum you would pay—and why?"
Alternatives considered: "Which solutions did you compare us against?"
Follow-up: "How did their pricing influence your decision process?"
AI-powered follow-ups unpack why someone hesitates: If a respondent says, "It’s a bit pricey," the AI can clarify, "Is that compared to a specific tool or your overall budget?"—illuminating true barriers and trade-offs.
Van Westendorp pricing questions—the gold standard for price sensitivity—become much more revealing when made conversational:
"At what price would you start to feel the product is too cheap to trust?"
"At what price would it start to feel too expensive?"
AI can ask why those thresholds matter, or what feature would justify a higher price, providing context you simply cannot get with static forms.
Traditional Pricing Survey | Conversational Approach with AI |
|---|---|
Choose a price range | Share your sense of value, and discuss trade-offs. AI probes personal context and use cases. |
Tick box: "Too expensive" | If you say "too expensive," AI asks, "Is it due to budget or because you saw something cheaper?" |
Specific’s AI can group and segment responses by price sensitivity—so you immediately understand differing attitudes by persona or customer type—via automated AI survey response analysis.
Generate a pricing survey for SaaS with Van Westendorp questions and conversational follow-ups about perceived value and alternatives.
Modern AI-driven surveys have been shown to triple or quadruple pricing survey completion rates compared to static forms, dramatically expanding data quality and sample size [3].
NPS and satisfaction questions that capture the full story
The Net Promoter Score (NPS) is a global standard for measuring loyalty, but without context, it’s just a number. The magic happens when you add smart, tailored follow-ups to every score.
NPS Standard Question: "How likely are you to recommend us to a friend or colleague?"
AI-powered follow-up logic:
Promoter (9-10): "What did you love most about your experience?"
Passive (7-8): "What’s one thing we could improve to make you more likely to recommend us?"
Detractor (0-6): "What disappointed you most, and how could we fix it?"
Satisfaction with support: "How did you feel about the help you received recently?" (AI follow-up: "What made the support great or not so great?")
Feature usefulness: "Which features have been most/least helpful to you?" (AI follow-up: "Can you explain why or give an example?")
This blending of structured scoring and flexible follow-up enables you to see not only satisfaction trends but also the reasons behind every rating—especially when using in-product conversational surveys triggered right after key actions.
Recurring NPS surveys give you satisfaction trends over time. With advanced targeting, you can schedule surveys at optimal touchpoints without spamming loyal users.
Create an NPS and satisfaction survey for in-app users. Add custom follow-ups for promoters, passives, and detractors—plus questions about support and features.
Frequency controls and AI-powered scheduling prevent survey fatigue, so you capture authentic feedback without overwhelming your audience.
And for international brands: multilingual survey support means you hear the full voice of your global customer base—no translation lag required.
Implementing your customer analysis strategy
Start by choosing the right survey placement for each goal.
Landing page surveys work best for:
Market research before launch
Lead qualification (enrich sales intake with contextual details)
Broad persona or segment discovery
In-product surveys are ideal for:
Feature-specific feedback
Churn analysis (triggered at risk points)
Real-time satisfaction checks after user actions
The right timing and targeting matter. For example, trigger in-product surveys after feature adoption or at churn signals, and send landing page surveys to new visitors or high-intent leads. For B2B, combining both methods uncovers market blind spots and product issues.
Landing page surveys | In-product surveys |
|---|---|
Great for market validation, audience research, discovery, and lead qualification. | Perfect for contextual feedback, usage pain points, NPS, and ongoing experience monitoring. |
Typically broader, less context-rich per respondent. | Laser-focused, leveraging real user behavior for nuanced insights. |
Specific’s AI survey editor makes it easy to adjust wording, targeting, and flow on the fly in plain English. Testing surveys with an interactive demo before launch helps you refine tone and probe depth.
If you’re not running these conversational surveys, you’re missing nuanced insights that static forms can’t capture—especially the “why” behind customer answers.
Start building your customer analysis framework
Transform your customer understanding with truly conversational surveys and lightning-fast AI-powered analysis. Respondents love the natural chat—and you’ll save hours of manual work. Create your own survey.