Following voice of the customer best practices means asking the right questions to shape your product roadmap—but most surveys barely scratch the surface.
Traditional surveys tend to miss the nuance behind why customers use your product, which features really matter, and how price influences adoption or churn.
Conversational AI surveys can dig deeper, surfacing true customer needs with intelligent follow-up and targeted branching.
Uncovering jobs-to-be-done through conversational surveys
One of the most powerful frameworks for product feedback is Jobs-to-be-Done (JTBD): the idea that customers "hire" products to solve specific problems in their workflow. Rather than settle for generic satisfaction scores, ask questions that target the real role your product plays in their work and life.
“What were you trying to accomplish when you first started using our product?”
“How does our product fit into your daily workflow?”
“What was your workaround before you started using this solution?”
Open-ended questions like these uncover the motivations and triggers behind adoption. But when a customer answers vaguely—like “I needed to save time”—AI-powered follow-ups shine. The survey can instantly probe with, “What specific tasks were taking up most of your time?” or “How did this impact your work overall?” (Explore the automatic AI follow-up question capabilities for activating these insights.)
Core JTBD questions identify what people are actually trying to achieve. Try prompts such as:
“What’s the core problem you hoped to solve with this product?”
“When was the last time you felt this workflow was ‘broken’? What happened?”
AI follow-up logic turns short answers into deeper stories. If someone answers “I use it for communication,” the survey can ask, “What communication challenges were you constantly running into?” Generative AI enables surveys to adapt just like a skilled human interviewer would (learn more about powerful follow-up conversations).
Create a customer survey to understand the core jobs our product does for users. Focus on uncovering their workflow challenges, what they were doing before our solution, and what success looks like in their role.
Getting to the “why” behind adoption doesn’t just inform surface-level features—it signals broader opportunities for innovation.
Identifying feature gaps that actually matter
Simply asking, “What features do you want?” almost always delivers a feature wishlist you could never build. A better approach is to identify true pain points and missing links in the customer’s workflow, then map those to potential solutions.
Conversational surveys, with dynamic question paths, can gently guide respondents toward actionable insights. Instead of gathering noise, you get signal on which gaps truly block value (refine your questions in plain English using the AI survey editor).
Problem-first questions dig for the "why" behind friction. For example:
“What’s the most frustrating part of your current workflow?”
“Tell me about a time you struggled to complete a task in our product.”
Traditional | Conversational approach |
|---|---|
What features would you like to see? | Walk me through your biggest challenges when using this product. |
Rate your satisfaction with feature X. | Which steps in your process still feel manual, slow, or risky? |
Workflow mapping questions clarify where your product helps—and where it drops the ball. Try questions like:
“What tools do you use alongside our product?”
“Describe a process you wish could be automated or improved.”
AI follow-up logic can spotlight specific moments of friction: if a customer mentions a tedious onboarding, the survey can ask, “What part of onboarding would you eliminate or change first?” Using AI-powered survey editing, you can quickly spin up and test variations to find which questions unlock the richest context.
The key is that you’re not just collecting more data—you’re collecting actionable insights aligned to real workflow barriers. And with only 4% of customers typically giving direct feedback through surveys[1], plugging these gaps efficiently makes every response count.
Measuring willingness to pay for roadmap features
Knowing what people “want” is easy; knowing what they’d actually pay for? That’s the real strategic edge. Before you invest months building a feature, ask targeted questions about value, budget, and impact.
Adapting the Van Westendorp pricing model helps prioritize features, not just products. Example questions:
“If this new capability were added, which other tools could you stop paying for?”
“How much extra would this save your company each month?”
“Would this feature be a must-have or a nice-to-have for your team?”
Value discovery questions get to the heart of real trade-offs:
“Which missing capability would have the biggest impact on your team’s productivity?”
“Compare this feature to the alternatives you’ve used—what stands out for you?”
Budget allocation questions are great for exposing how customers actually make spending decisions:
“Do you have a budget for solutions like this?”
“Who approves purchases over $X in your company?”
Dynamic AI can drill into specifics. If a user says, “There’s no budget,” the AI might follow up: “How do you typically justify new purchases?” Company size or role can trigger tailored probing, like, “Is price a deciding factor for your team, or are you focused more on efficiency?”
This nuance directs roadmap investments toward the highest leverage improvements—where willingness to pay overlaps with true user need. Remember, companies that use customer feedback to direct product development see up to 10% higher revenue growth[2].
Personalizing roadmap questions by customer persona
One size doesn’t fit all. The questions that engage a CEO will fall flat with an implementer or daily end user. Conversational surveys adapt in real time—branching by role, company size, or use case—with targeted follow-ups that feel personal (explore persona-based branching in survey creation).
Role-based branching delivers tailored question flows:
Managers get ROI and impact questions.
End users get daily workflow and usability questions.
Use case branching adapts to expertise, too:
Power users might get, “Which advanced features do you rely on daily?”
Casual users are asked, “What keeps you from using this product more often?”
Persona | Power User | Casual User |
|---|---|---|
Focus | Advanced features, integrations | Barriers to adoption, onboarding |
Example question | Which workflow automations are most valuable to you? | What would motivate you to use this more regularly? |
With the AI survey generator, you can instantly create surveys with custom branching logic for any persona.
Build a product roadmap survey that adapts questions based on whether the respondent is a manager focused on team productivity or an individual contributor focused on daily tasks. Include pricing sensitivity questions for decision-makers only.
Segmenting questions by persona surfaces micro-insights you’d never get with a static, one-size-fits-all questionnaire.
Turn customer conversations into roadmap clarity
When you ask better questions—and let AI probe for details—you go beyond guesswork to build products customers actually want.
Conversational surveys with automated, targeted follow-ups reveal the “why” behind feature requests and churn—delivering context that powers smarter product decisions and ensures a stronger product-market fit.
AI analysis spots patterns and themes from every conversation, so you can prioritize what matters most to every segment, every time.
Now’s the time to create your own survey and discover the roadmap-changing insights hiding in plain sight.