8+ failure patterns with diagnoses and cures.
The diagnosis. Likely feature-list-dump; no decision support.
The cure. Add recommendation. Cut decoration axes. Detail in references/recommendation-engine-design.md.
The diagnosis. Hidden-recommendation pattern; trust damage.
The cure. Honest recommendation discipline (references/honest-recommendation-discipline.md). Acknowledge competitor strengths.
The diagnosis. Comparison grids do not work well on mobile.
The cure. Mobile-first design. Stacked layout. Reduced axis count for mobile.
The diagnosis. Audience knows these features; tool may have used easy axes rather than decision-relevant.
The cure. Audit axes against real audience decisions. Replace marketing-checkbox axes with decision-relevant ones.
The diagnosis. Features changed; comparison stale.
The cure. Quarterly maintenance.
The diagnosis. Recommendation absent or weak.
The cure. Add recommendation with reasoning and clear next-step CTA.
The diagnosis. Dilution; no clear recommendation.
The cure. Take a stand. Recommendation should be opinionated; tradeoffs acknowledged.
The diagnosis. Crossed the cell-count threshold; cognitive overload.
The cure. Cut back. 8-12 axes is the working range.
The diagnosis. Tool's framing oversells; reality on demo does not match.
The cure. Honest tool produces honest leads. Audit framing for sales-pitch dynamics.
The diagnosis. Segment definitions based on assumptions rather than research.
The cure. Validate audience segments with research. Refine recommendations based on real needs.
The diagnosis. Competitor omitted from comparison.
The cure. Include relevant competitors. If intentionally excluded, disclose why ("we focus on X category; competitor Y is in different category").
The diagnosis. Methodology hidden or weakly defended.
The cure. Detailed methodology disclosure. Be ready to defend specific axis choices and weights.
The diagnosis. Tool optimizes for tool-conversion; downstream not aligned.
The cure. Track downstream metrics. Optimize for downstream conversion, not just tool engagement.
The diagnosis. Filters added but not promoted; users do not realize they exist.
The cure. Promote filters; or accept that defaults serve and reduce filter complexity.
The diagnosis. Filter logic does not constrain combinations to those producing comparable options.
The cure. Smart filter logic. When filters would produce empty result, surface alternatives.
The diagnosis. Hidden-recommendation; bias-flattering logic.
The cure. Test the recommendation engine across audience scenarios. Ensure competitors win when fit warrants.
The diagnosis. Comparison data accurate; recommendation engine logic does not match audience priorities.
The cure. Validate recommendation logic. Audience research on what they weigh vs what the tool weights.
The diagnosis. Crossed the cell-count threshold.
The cure. Maintain axis discipline. More is not always better.
The diagnosis. Tool optimized for first-time-decision audience; returning visitors have other needs.
The cure. Different surfaces for different stages. Returning visitors may want simpler reference rather than full tool.
Most comparison tool failures fall into one of three patterns.
Pattern 1: No decision support. Feature-list-dump, no-recommendation, dilution. The fix is clear, defensible recommendation.
Pattern 2: Bias damages trust. Hidden-recommendation, contradiction, omitted competitors, stereotypes. The fix is honest recommendation discipline.
Pattern 3: Friction. Over-filtered, mobile-broken, fatigue patterns. The fix is reducing cognitive load and matching audience use.
The metric pattern: comparison failures often look fine on engagement alone. The signal is in conversion, downstream metrics, audience trust.
The catalog of failure patterns with diagnoses and cures. The pattern across failures (no-decision-support, bias, friction). The principle that engagement alone is insufficient.
Specific failure cases the team has encountered. Specific multi-metric dashboards. Specific cures. The team's audit and retirement processes. These vary by team.