Use Real Problem Evidence to Make Better Product Decisions
PDE helps builders, product teams, researchers and operators turn scattered complaints, workarounds and demand signals into structured problems they can investigate and validate.
PDE does not tell you what to build. It helps you understand what is actually worth investigating.
Four Roles. One Evidence-First Standard.
Each role faces different questions — but the method is the same: start with evidence, not assumptions.
Founders & Independent Builders
Stop guessing what to build.
Current Problem
- Which problems are real vs noise?
- Which problems repeat across sources?
- Is this worth weeks or months of effort?
- How do I separate pain from commercial proof?
Current Workaround
Why It Fails
- Fragmented — no single view
- Hard to compare across sources
- Easy to overreact to isolated complaints
- Difficult to trace back to evidence
- Pain and commercial proof get confused
What PDE Provides
- Structured Problem Signals
- Repeated Problem Clusters
- Evidence-backed Problems
- Documented workarounds
- Switching & payment evidence (where available)
- Unknowns & missing proof clearly stated
How You Use It
- Browse a Problem
- Review evidence and signals
- Check what is supported
- Check what remains unknown
- Run customer validation outside PDE
Decision Supported
Product Teams
Turn scattered feedback into clear problem patterns.
Current Problem
- Reviews, support tickets, community posts, feature requests pour in from every channel
- Which are isolated complaints vs repeated problems?
- Which feature requests hide deeper jobs or problems?
Current Workaround
Why It Fails
- Feedback is fragmented across tools
- Loud users dominate the conversation
- Context gets lost in aggregation
- Repeated symptoms mistaken for different problems
- Feature requests mask underlying jobs
What PDE Provides
- Actor + Context + Desired Result + Blocking Step
- Repeated Signals across sources
- Problem Clusters with evidence basis
- Full evidence traceability to source
How You Use It
- Review repeated signals
- Separate symptoms from underlying problems
- Compare actors and contexts
- Inspect source evidence
- Identify where more user research is needed
Decision Supported
Researchers & Market Analysts
See recurring problem patterns before they become obvious.
Current Problem
- Scattered complaints, changing behavior, new workarounds
- Product abandonment and alternative-seeking behavior
- Information is dispersed and hard to structure
Current Workaround
Why It Fails
- Signals lack consistent structure
- Isolated mentions easy to overinterpret
- Source context gets lost
- Commercial proof confused with attention
What PDE Provides
- Structured signal records
- Cluster relationships
- Problem definitions with evidence provenance
- Commercial proof status
- Unknown / insufficient states explicitly recorded
How You Use It
- Compare repeated signals
- Inspect cluster formation
- Distinguish recurring problems from noise
- Track supporting evidence
- Identify research gaps
Decision Supported
Operators & Business Owners
Understand where existing solutions are failing.
Current Problem
- "This workflow is painful" — but is it the process, the price, the tool, or a structural friction?
- Problems are felt but not structured
Current Workaround
Why It Fails
- Symptoms treated individually
- Workaround cost stays hidden
- Repeated friction gets normalized
- Root problems remain unclear
What PDE Provides
- Structured problem statements
- Evidence-backed workarounds
- Blocking steps clearly identified
- Repeated signals from external sources
- Proof gaps explicitly stated
How You Use It
- Compare observed friction with external evidence
- Identify whether the problem repeats elsewhere
- Review known workarounds
- Understand what is proven vs unknown
- Decide whether deeper operational research is justified
Decision Supported
At a Glance
| User | Their Question | What PDE Gives | Decision |
|---|---|---|---|
| Founders | What problem is worth validating? | Evidence-backed problems | Investigate · Validate · Stop |
| Product Teams | Which feedback reflects a real pattern? | Structured signals + clusters | Research · Prioritize · Ignore |
| Researchers | What problem pattern is emerging? | Traceable evidence + problem structure | Study · Monitor · Reject |
| Operators | Where is workflow friction actually from? | Blocking steps + workarounds + evidence | Investigate · Change · Defer |
Real Example: One Problem, Four Perspectives
The same evidence-based problem looks different from each role's viewpoint. Using the current problem: Metered Automation Cost Predictability
Founder view
Is this problem worth validating further? The evidence shows 3 independent sources, real payment signals, and a recurring metering pain. But commercial significance for a new product remains unproven.
→ INVESTIGATE or DEFERProduct team view
Which aspect of metered automation creates repeated friction? The blocking step is structural: no one-off top-up, invisible consumption, fail-closed pause. Each signal independently confirms the same friction.
→ RESEARCH the metering designResearcher view
What evidence supports the pattern? Three independent sources (2023–2026) from Zapier's community, each with raw quotes and money signals. The pattern recurs across different authors and contexts.
→ STUDY the metering predictability patternOperator view
Which blocking step causes operational risk? The fail-closed pause halted production Zaps across a weekend — even after a paid mid-cycle upgrade. The operator paid and the automations still stopped.
→ CHANGE PROCESS or TEST ALTERNATIVEWhat PDE Does Not Decide for You
Explore Real Problems
See what is supported, what remains unknown, and what deserves further validation.
Explore Problems →