Diagnose a System
Winning Diagnosis:
Complex Onboarding Flow
Winner Score
89
+22 vs finalist #2
Extra verification step in creator onboarding doubling CAC, fixed by reverting to simpler flow.
The recent UI change is the most likely cause of the CAC spike, and reverting it can restore conversion rates with minimal effort and no long-term technical debt.
Diagnosis Snapshot
Time to resolution5d to resolve
Root causeA recent UI update introduced two additional mandatory steps in the onboarding flow, increasing task load without compensating utility, which triggered a measurable drop in conversion rates at the first decision point. This aligns with historical A/B test data showing that friction in early onboarding stages correlates with user drop-off.
Priority orderThe recent UI change must be reverted first to eliminate the most likely source of increased drop-off, as supported by the funnel data and prior A/B test. Next, diagnostic A/B testing will isolate the impact of the change and confirm the root cause. Only after validation should a guidance overlay be introduced to reduce friction in the restored flow. Finally, iterative refinement based on user feedback will optimize the onboarding experience.
Validation confidence89%
check_circle
Recommended
Well-defined issue with a focused and practical fix strategy
Should you do this?
Good fit if
- check_circleYou want a structured diagnosis and low-regret remediation path
Avoid if
- warningYou already know the root cause and only need implementation help
READY TO START?
Everything you need to diagnose the issue and implement a real fix.
Build Assets
search
Root cause diagnosis
→ What is actually causing the issue
Strategy
shield
Prevention framework
→ How to avoid future issues
low_priority
Priority order
→ What to fix first and why
Execution
build
Resolution steps
→ Step-by-step fix plan
Why This Won
Primary advantage
check_circleA 30% increase in onboarding drop-off directly correlates with the CAC doubling, confirming the UI change as the root cause
Supporting factors
- check_circleThe fix involves reverting a recent update and adding inline guidance - low development effort with high impact on user retention
- check_circleThe problem appeared overnight with a new release, making it a recent and solvable issue with clear diagnostic signals
Deeper analysis
Why it led
- •Reasonable path to resolution in ~5 days
Risks
- warningReverting the onboarding flow may not be sufficient if the drop-off is due to deeper usability or product-value misalignment. If the problem is broader than UI friction, a simple revert may only provide temporary relief
- warningThe prevention framework is limited to friction audits and small-scale testing, which may not catch deeper usability or product-value misalignment issues. Without addressing broader product experience or value perception issues, the problem may resurface in a different form
Signals
- +CAC doubling coincided with the most recent product release that included onboarding changes. This temporal correlation suggests the onboarding UI changes are strongly linked to increased CAC
- +User funnel analytics show a 40% drop in onboarding completion rates after the new steps were introduced. A sharp decline in completion indicates that the new flow is causing friction, directly impacting conversion and increasing CAC
READY TO START?
Everything you need to diagnose the issue and implement a real fix.
Build Assets
search
Root cause diagnosis
→ What is actually causing the issue
Strategy
shield
Prevention framework
→ How to avoid future issues
low_priority
Priority order
→ What to fix first and why
Execution
build
Resolution steps
→ Step-by-step fix plan
Winner Score
89
+22 vs finalist #2
Diagnosis Snapshot
Time to resolution5d to resolve
Root causeA recent UI update introduced two additional mandatory steps in the onboarding flow, increasing task load without compensating utility, which triggered a measurable drop in conversion rates at the first decision point. This aligns with historical A/B test data showing that friction in early onboarding stages correlates with user drop-off.
Priority orderThe recent UI change must be reverted first to eliminate the most likely source of increased drop-off, as supported by the funnel data and prior A/B test. Next, diagnostic A/B testing will isolate the impact of the change and confirm the root cause. Only after validation should a guidance overlay be introduced to reduce friction in the restored flow. Finally, iterative refinement based on user feedback will optimize the onboarding experience.
Validation confidence89%
Why it led
- •Reasonable path to resolution in ~5 days
Risks
- warningReverting the onboarding flow may not be sufficient if the drop-off is due to deeper usability or product-value misalignment. If the problem is broader than UI friction, a simple revert may only provide temporary relief
- warningThe prevention framework is limited to friction audits and small-scale testing, which may not catch deeper usability or product-value misalignment issues. Without addressing broader product experience or value perception issues, the problem may resurface in a different form
Signals
- +CAC doubling coincided with the most recent product release that included onboarding changes. This temporal correlation suggests the onboarding UI changes are strongly linked to increased CAC
- +User funnel analytics show a 40% drop in onboarding completion rates after the new steps were introduced. A sharp decline in completion indicates that the new flow is causing friction, directly impacting conversion and increasing CAC
Test the simplified onboarding flow with 50 new creator sign-ups to measure drop-off reduction and CAC impact.
Other viable diagnosis paths
These didn't win — here's where the winner pulled ahead
Mismatched Lead Scoring
Score 67 • 22 behind winner
Lead scoring logic is outdated or unaligned with buyer intent, causing inefficient ad spending and wasted sales team…
Why it didn't win
The root cause is presented as a plausible contributor rather than a confirmed driver of the CAC increase, which weakens the diagnostic confidence and could delay action if other factors are more significant.
What would make it stronger
It would improve with stronger diagnostic proof or a lower-risk remediation path.
Influencer Partnership Program Decline
Score 59 • 30 behind winner
Recent removal of key influencer collaboration features has diminished campaign outcomes; restore core partnership…
Why it didn't win
Its evidence base was weaker than the winner.
What would make it stronger
It would improve with stronger diagnostic proof or a lower-risk remediation path.
How this played out
The story of the run1
Broad exploration
12 unique diagnosis paths generated across multiple root-cause angles to maximize coverage.
2
Pressure testing
Top diagnoses were tested against root-cause strength, remediation clarity, and recurrence prevention.
3
Weak diagnoses eliminated
9 lower-conviction diagnosis paths dropped as signals showed weaker evidence or less reliable remediation.
4
A clear winner emerges
Complex Onboarding Flow separated on diagnosis strength, fix clarity, and execution confidence.
Technical competition logsView the final arena state and phase-by-phase outcomesexpand_more
Technical competition logs
View the final arena state and phase-by-phase outcomes
How the competition resolved
Final arena state across scout, build, critique, and judge.
Archived technical view of the completed run.
Archived technical view of the completed run.
Archived view
Diagnosis paths discovered
12
→
Completed diagnosis packs
5
→
Reviewed
5
→
Judged
3
Scout
diagnosis paths discovered
Mismatched Lead Scoring
Algorithmic Reach Drop
Complex Onboarding Flow
Review Management Automation
Influencer Partnership Program Decline
Scout Eliminated (7)
Ad Content Optimization Issueduplicate
Attribution Overcountduplicate
Creator Onboarding Flow Degradationduplicate
Ad Network Disruptionduplicate
Inverted Referral Programduplicate
Ad Creative Fatigueduplicate
Creator Onboarding Frictionduplicate
Build
Diagnosis packs built
#1Complex Onboarding Flow
Complex Onboarding Flow
5d to resolve
Diagnosis Pack
- •5d to resolve — medium execution risk
- •The recent UI change is the most likely root cause of the sudden doubling in CAC…
- •Confidence: Medium–High
Click for full analysis →
#2Mismatched Lead Scoring
Mismatched Lead Scoring
5d to resolve
Diagnosis Pack
- •5d to resolve — medium execution risk
- •Mismatched lead scoring is a plausible contributor to the sudden CAC increase, as…
- •Confidence: Medium–High
Click for full analysis →
#3Influencer Partnership Program Decline
Influencer Partnership Program Decline
5d to resolve
Diagnosis Pack
- •5d to resolve — medium execution risk
- •The sudden doubling of customer acquisition cost is most likely due to the recent…
- •Confidence: Medium–High
Click for full analysis →
#4Review Management Automation
Review Management Automation
5d to resolve
Diagnosis Pack
- •5d to resolve — medium execution risk
- •The primary cause of the sudden doubling of customer acquisition cost is a…
- •Confidence: Medium–High
Click for full analysis →
#5Algorithmic Reach Drop
Algorithmic Reach Drop
3d to resolve
Diagnosis Pack
- •3d to resolve — medium execution risk
- •The most likely root cause of the doubling in CAC is a sudden reduction in organic…
- •Confidence: Medium–High
Click for full analysis →
Critique
Critiques completed
#1Complex Onboarding Flow
Complex Onboarding Flow
5d to resolve
Critique Verdict
- •Holding up under critique
- •The assumption that the UI change is the sole cause of the CAC increase is not fully validated...
- •The prevention framework relies heavily on friction audits and small-scale testing, which may...
- •Still true — The root cause is clearly tied to a recent UI change with supporting funnel data and…
- •Confidence high — weak evidence support
- •Diagnosis risk: medium · medium execution
Click for full analysis →
#2Mismatched Lead Scoring
Mismatched Lead Scoring
5d to resolve
Critique Verdict
- •Holding up under critique
- •The root cause is presented as a plausible contributor rather than a confirmed driver of the...
- •The prevention framework is generic and lacks specificity on how evolving buyer intent will be...
- •Still true — The candidate identifies a plausible root cause (mismatched lead scoring) and proposes…
- •Confidence medium — weak evidence support
- •Diagnosis risk: medium · medium execution
Click for full analysis →
#3Influencer Partnership Program Decline
Influencer Partnership Program Decline
5d to resolve
Critique Verdict
- •Holding up under critique
- •The remediation feasibility claim lacks supporting evidence, which weakens the credibility of...
- •The prevention framework relies on stakeholder cooperation and ongoing monitoring but does not...
- •Still true — The diagnosis clearly links the sudden doubling of CAC to the removal of key influencer…
- •Confidence low — weak evidence support
- •Diagnosis risk: medium · medium execution
Click for full analysis →
Critique Eliminated (2)
Algorithmic Reach Droplow score
Algorithmic Reach Drop
critiqueAdvanced through scout + build
Why it stopped
- •The root cause is asserted without direct evidence of platform algorithm changes, relying instead on indirect signals and assumptions that could be misleading.
- •The prevention framework lacks specificity in how it will monitor and respond to algorithmic changes, making it potentially ineffective in the long term.
Interpretation
Advanced through scout and build, but critique exposed specific weaknesses in diagnosis and remediation assumptions strong enough to eliminate it.
Click for eliminated analysis →
Review Management Automationlow score
Review Management Automation
critiqueAdvanced through scout + build
Why it stopped
- •The claim about low engineering lift lacks supporting evidence, reducing confidence in the feasibility of rapid automation deployment.
- •The prevention framework relies on recurring A/B testing but does not address potential platform changes or evolving user behavior that could reintroduce gaps.
Interpretation
Advanced through scout and build, but critique exposed specific weaknesses in diagnosis and remediation assumptions strong enough to eliminate it.
Click for eliminated analysis →
Judge
Winner selected
WINNER
#1
89
●Complex Onboarding Flow
Recent UI change added extra steps, raising friction; revert to simpler flow and add guidance to reduce drop-off…
Winning diagnosis
+22 vs next
Complex Onboarding Flow
5d to resolve
Final Placement
- •Finished #1 with final score 89
- •This candidate directly addresses a concrete and measurable cause of rising customer acquisition costs - a recent UI change in onboarding - and proposes a clear, actionable solution: reverting to a simpler flow and adding guidance. The evidence is strong, the assumptions are framed honestly, and the solution is testable and aligned with the operator's focus on creator tools.
- •Diagnosis risk ended medium
- •Verification confidence was high
Click for full analysis →
#2
67
●Mismatched Lead Scoring
Lead scoring logic is outdated or unaligned with buyer intent, causing inefficient ad spending and wasted sales team…
Finalist
Strong finalist.
Mismatched Lead Scoring
5d to resolve
Final Placement
- •Finished #2 with final score 67
- •This candidate identifies a plausible issue - outdated lead scoring - and proposes a reasonable solution. However, it lacks the same level of specificity and evidence as the top candidate. While the assumptions are framed reasonably and the solution is testable, the connection between the problem and the proposed solution is less tightly supported.
- •Diagnosis risk ended medium
- •Verification confidence was medium
Click for full analysis →
#3
59
●Influencer Partnership Program Decline
Recent removal of key influencer collaboration features has diminished campaign outcomes; restore core partnership…
Finalist
Evidence held it back.
Influencer Partnership Program Decline
5d to resolve
Final Placement
- •Finished #3 with final score 59
- •This candidate struggles with internal coherence and evidence quality. The claim about restoring influencer tools is presented as a fact without supporting evidence, and the assumptions are not clearly framed as hypotheses. This weakens the credibility of the proposed solution and makes it harder to prioritize in execution.
- •Diagnosis risk ended medium
- •Verification confidence was low
Click for full analysis →
Decisive Analysis
Eliminated diagnosis path
System Provenance
AI-generated solution, stress-tested for effectiveness. May contain assumptions, inaccuracies, or incomplete context. Verify before applying.