Inference Scaffolding Framework for Advanced AI Reasoning

topic: declining customer retention rates, context_constraints: limited budget for new initiatives, target_outcome: actionable strategy to improve retention within 90 days

## STAGE 1: FOUNDATIONAL EVIDENCE MAPPING - Retention dropped 23% over 6 months (High confidence - direct data) - Customer support tickets increased 40% (High confidence - system logs) - Average resolution time doubled to 48 hours (Medium confidence - may include weekends) - Exit survey mentions 'poor communication' in 67% of responses (Medium confidence - self-selected sample) - Competitor launched loyalty program 4 months ago (Low confidence - timing correlation) Evidence gaps: No data on customer satisfaction scores, unclear which customer segments are leaving ## STAGE 2: HYPOTHESIS CONSTRUCTION Hypothesis A: Service quality degradation is primary driver Supporting: Ticket increase, resolution delays, exit survey feedback Contradicting: No direct service quality metrics available Hypothesis B: Competitive pressure from new loyalty program Supporting: Timeline correlation with retention decline Contradicting: Exit surveys focus on internal issues, not competitor attraction Ranked strength: A (stronger evidence base), B (circumstantial) ## STAGE 3: INFERENCE CHAIN BUILDING [Service capacity remained static] → [Customer base grew 15%] → [Support overwhelmed] → [Response delays increased] → [Customer frustration rose] → [Retention declined] Weak link: Assumption that support capacity remained static without verification ## STAGE 4: VALIDATION & REFINEMENT Testing against budget constraints: Focus on operational efficiency rather than new technology or additional staff. Additional needed: Support team capacity data, customer satisfaction trends, segment-specific retention rates. ## STAGE 5: STRUCTURED OUTPUT **Primary Conclusion:** Retention decline stems from support capacity mismatch with customer growth, creating systematic service delays that erode customer experience **Confidence Level:** Medium-High (strong supporting evidence, some gaps in operational data) **Key Assumptions:** Current support processes are inefficient, customers value quick resolution over other factors, internal capacity hasn't scaled with growth **Alternative Scenarios:** Competitor loyalty program may be contributing factor, product quality issues could be underlying cause **Recommended Actions:** 1) Implement support ticket prioritization system within 30 days, 2) Audit top 10 recurring issues for self-service solutions, 3) Establish weekly customer satisfaction pulse surveys to track improvement