The best voice of the customer example starts with asking the right questions that unlock genuine insights about your customers' experiences. In SaaS, voice of the customer (VoC) means going beyond feature checklists—it's about deeply understanding what drives usage, satisfaction, and loyalty. Capturing this feedback takes intention: you need to pose questions that open doors, not just check boxes.
This guide highlights the 20 best questions and shows how AI-powered follow-ups can turn every answer into action-ready insight. If you’re setting up conversational surveys, you can quickly create surveys with AI and start meaningful conversations with your users.
Questions to understand product value and adoption
Every strong SaaS product story starts with knowing why customers come—and why they stay. These questions zero in on product value and adoption dynamics. When you use tailored AI follow-up strategies, each answer becomes a launch pad for deeper discovery. Conversational surveys can turn what would have been a 2-line reply into a revealing dialogue, especially when you take advantage of automated follow-up logic.
What was the main reason you chose our product over others?
AI follow-up strategy: Probe for context (timeline, alternatives, key factors).
Can you share what other options you considered and specifically what made our product the best fit?
Insight: Reveals decision drivers and perceived value, helping refine positioning.
How does our product fit into your daily workflow?
AI follow-up strategy: Ask for examples of use cases and integration points.
Could you give a specific situation where our product helped you complete a task or save time?
Insight: Identifies “aha moments” and productive touchpoints.
What results have you experienced since using our product?
AI follow-up strategy: Probe for quantifiable outcomes or business impact.
Can you describe any measurable improvements or changes you’ve seen since adopting our solution?
Insight: Surfaces user-defined ROI and supports testimonial claims.
How did you first hear about us?
AI follow-up strategy: Ask for specifics on their journey from discovery to signup.
What motivated you to give our product a try after learning about it?
Insight: Optimizes marketing channels and word-of-mouth pathways.
Was there anything that nearly stopped you from signing up?
AI follow-up strategy: Probe for friction points and their resolution.
What almost made you decide against using us—and what changed your mind?
Insight: Uncovers last-mile objections that can be smoothed or preempted.
AI-driven, conversational formats have been proven to elicit more specific and clear answers than traditional survey forms, making these product-focused questions substantially more actionable. [3]
Identifying pain points and friction in the customer journey
To build a standout SaaS experience, you need to pin down exactly where customers hit roadblocks. The following are cornerstone best questions voice of the customer surveys must include for diagnosing issues that frustrate or slow users. AI follow-up lets you gently dig for details without sounding accusatory, surfacing actionable specifics buried beneath vague complaints.
What’s the most frustrating part about using our product?
AI follow-up strategy: Request a recent example and ask how it disrupted their workflow.
Can you walk me through the last time this frustration happened? How did you try to solve it?
Insight: Ties pain points to real workflows, giving both urgency and context for fixes.
Is there anything about our software that regularly confuses you?
AI follow-up strategy: Ask which screen or action triggers confusion and what they expect to see.
Which specific screen, button, or message causes confusion? Is there a change you wish we made?
Insight: Pinpoints usability fails and informs interface tweaks.
Have you ever considered switching to another solution?
AI follow-up strategy: Probe for triggers and what competitors offer that’s attractive.
If you have thought about switching, what triggered those thoughts? Was something missing?
Insight: Reveals churn risks and competitor watch-outs.
What takes longer than you think it should?
AI follow-up strategy: Drill into process steps, bottlenecks, and impact on their goals.
Can you describe a recent task that took longer than expected, and explain why?
Insight: Highlights workflow bottlenecks for engineering prioritization.
Has support ever failed to solve your problem?
AI follow-up strategy: Ask what happened, what they needed, and what ideal support would look like.
Can you share what you needed from support in that scenario? How could the process improve?
Insight: Diagnoses gaps in customer service and issue resolution.
Turning complaints into insights: With AI, even vague or emotionally charged answers get clarified on the spot, surfacing not just what went wrong but exactly how to resolve it. This is what transforms complaints into prioritized product or process fixes.
Overcoming these friction points is directly tied to measurable outcomes—increasing customer retention by just 5% can boost profits by 25–95%. [1]
Feature requests and product development insights
Understanding what users want next is critical for guiding SaaS development. The best feedback includes not just “which feature,” but “why” and “how” it would improve things. AI follow-ups help surface the deeper use cases, alignment with business goals, and must-haves versus “nice to haves.” The AI survey response analysis feature makes it simple to cluster and interpret these open-ended requests.
If you could add one feature to our product, what would it be?
AI follow-up strategy: Probe for intended benefits, process improvement, or specific frustrations prompting this wish.
How would adding this feature change the way you use the product?
Insight: Prioritizes features with clear user impact and narrative.
Are there any tasks you still do manually because our product doesn’t support them?
AI follow-up strategy: Ask about workarounds, time spent, or third-party tools used.
What tool or process do you turn to when our product can't deliver? How often does this come up?
Insight: Reveals workflow gaps and competitors by function.
If we could improve or automate one part of the experience, what should it be?
AI follow-up strategy: Drill into desired outcomes and the pain caused by current friction.
Can you share a recent example where a better solution would have made a difference?
Insight: Prioritizes roadmap opportunities for automation.
How do your needs for our product differ today from when you first signed up?
AI follow-up strategy: Explore evolving jobs-to-be-done and how the product stayed relevant—or fell behind.
Can you describe how your use of the product has changed over time? What would help now?
Insight: Ensures development addresses evolving user realities.
Is there a feature you never use? Why?
AI follow-up strategy: Clarify whether it’s missing value, discoverability, or relevance.
What would need to change to make you find this feature useful?
Insight: Avoids wasted resources and improves clarity in communication.
Surface-level feedback | Deep insight with AI follow-ups |
|---|---|
“I’d like a calendar sync” | “I have to manually update events in both tools. If your product synced with my calendar, I’d save an hour per week and reduce scheduling mistakes.” |
“Reporting is confusing” | “When I run quarterly reports, it’s unclear which data is current year versus last year. If the report tool could default to the last 90 days and clearly label timeframes, I’d avoid double-checking everything.” |
Clarity in user desires lets you build the features that actually move metrics, not just fill checkboxes of "requested."
Measuring satisfaction and predicting loyalty
Loyalty and satisfaction questions are the canaries in the SaaS coal mine—they spotlight who’s thriving versus who’s at churn risk. Combining classic metrics like NPS with AI follow-ups allows you to catch subtle shifts in customer mood. Specific’s AI automatically adapts probing based on each satisfaction level.
On a scale from 0-10, how likely are you to recommend us to a friend or colleague? (NPS)
AI follow-up strategy: Probe for specifics—reasons for high or low score, and missing factors.
What’s the main reason you gave this score?
Insight: Correlates loyalty scores to actionable themes—feature gaps versus platform bugs.
How satisfied are you overall with our product?
AI follow-up strategy: Dig for examples of positive impacts or lingering frustrations.
Can you share one thing that could increase your satisfaction by even one point?
Insight: Highlights quick wins and longer-term development opportunities.
What’s the most valuable thing about our product for you right now?
AI follow-up strategy: Ask when and how this value appears in their daily work.
Can you recall a recent moment when you relied on this value?
Insight: Distills “core product promise” so messaging and investment align with reality.
Is there anything about our service that you feel especially loyal to—or disappointed by?
AI follow-up strategy: Invite stories of wows and woes for deeper emotional context.
Was there a particular moment when you decided you’d stick with us—or almost leave?
Insight: Illuminates the emotional beats that create (or erode) retention.
Retention risk indicators: Robust VoC programs are rare—only 29% of SaaS companies incorporate voice of the customer in decision making, leaving a huge opportunity to shore up retention and turn feedback into rapid improvements. [2]
Understanding your competitive position
To grow and defend your SaaS, you need to understand not only what customers like, but why they might consider the competition. These questions—with conversational AI probing—cut through politeness to the real comparisons, switching costs, and decision drivers. Customizing your survey for competitive questions is simple in the AI survey editor.
What other solutions have you used before or alongside our product?
AI follow-up strategy: Ask what those options did better or worse.
Which features or experiences did you prefer with other tools?
Insight: Reveals differentiation and parity gaps.
If you were to stop using our product, what would you replace it with?
AI follow-up strategy: Dig into what the alternative must offer.
What is the most important thing you would look for in a replacement?
Insight: Identifies minimum viable expectations and switching factors.
What’s the one thing that could make you leave us for a competitor?
AI follow-up strategy: Probe for unmet needs or slow-moving trends.
What would a competitor have to offer to get you to switch?