Pulse Insights Playbook
Using Feedback to Create Products That Your Customers Love
Capture Product Intelligence That Drives Continuous Improvement
Traditional feedback methods capture a tiny, biased sample too late to matter. Post-experience surveys get 3-8% response rates. Email surveys a week later get vague answers from users who don't remember what frustrated them Tuesday.
Pulse feedback capture asks contextual questions at friction moments, success points, and decision moments—building continuous intelligence that reveals what's working and what needs fixing.
This complements action agents (Conversion, Wayfinding, Support) that solve individual problems in real-time. Feedback identifies systemic patterns that improve the experience for all future users.
How It Works
Detect → Learning opportunity (struggle moment, completion of key action, success, exit intent)
Ask → Contextual question captures their experience, goal, or suggestion
Capture → Feedback is logged, categorized, reveals patterns for product teams
The intelligence captured informs what to fix, build, or improve to prevent future friction for thousands of users.
The Big 3 Learning Moments
1. Goal & Intent Discovery
Understanding real goals, not assumed ones.
Signals: User arrives at key pages (product pages, pricing, help center, category pages)
Question: "What brings you here today?"
What you learn:
Actual goals users have ("see if this solves [problem]" vs your assumed "learn about solution")
Language they use to describe needs (informs messaging and copy)
Goals you're not supporting (product gaps to address)
Intelligence value: High-traffic pages with low success rates reveal messaging/product mismatches
2. Task Success Measurement
Success rate by goal is your product health metric.
Signals: User completed a flow (checkout, signup, onboarding) or is leaving
Question: "Did you accomplish what you came to do?"
What you learn:
Which pages/flows succeed or fail at their purpose
What users couldn't accomplish (unmet needs to prioritize)
Patterns by segment or use case (who struggles where)
Intelligence value: Low success rates reveal friction that needs fixing before more users abandon
3. Friction Point Detection
Captures struggle in the moment before silent abandonment.
Signals: Repeated actions, errors, time without progress, exit intent after minimal engagement
Question: "Running into any issues?"
What you learn:
Specific features/steps that confuse or frustrate users
Where product doesn't match expectations (design gaps)
Technical issues users encounter (bugs to fix)
Intelligence value: Users who struggle but persist can tell you exactly what's wrong—users who quit can't
Five More Learning Opportunities
Feature Value Assessment
After using a feature. Ask: "How useful was [Feature] for your task?" Learn: Which features deliver value vs disappoint, what's missing to increase usefulness.
Competitive Intelligence
Comparison moments, arrival from competitor sites. Ask: "What made you choose us?" or "What's holding you back?" Learn: Real competitive advantages, what competitors do better.
Improvement Suggestions
High engagement or power users. Ask: "If you could change one thing, what would it be?" Learn: Feature requests from actual users, priorities, unanticipated use cases.
Exit Reason Capture
Exit intent without conversion. Ask: "What's stopping you?" Learn: Common objections, missing information, deal-breakers to address.
Success Pattern Discovery
After successful completion. Ask: "What helped most?" Learn: Success patterns to replicate, what's working well to preserve.
How Intelligence Builds
The continuous loop:
Capture - Contextual questions at key moments
Categorize - Tag feedback by theme, severity, page/feature
Prioritize - Impact × frequency = what to fix first
Implement - Make changes based on patterns (not individual comments)
Measure - Did the change reduce reported friction?
Communicate - Tell users their feedback drove changes
Critical: Feedback without action is worse than no feedback. Users who give input and see nothing change feel ignored.
What Makes This Different
In-context - Captures at moment of friction/success, not days later via email
Pattern-focused - Reveals systemic issues affecting thousands, not individual complaints
Action-oriented - Informs what to fix, not just dashboards to analyze
Measurement
Response rate - % who answer when asked (target: 15-30%)
Action rate - % of feedback that influences product decisions
Friction reduction - Decreased "this is confusing" after fixes
Products people love aren't lucky—they're listening at the moments that matter.