Preserving Individual Differences in Customer Narratives: Customer Interview Intelligence as a Transparent Chain of Inference From Expression to Action

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Abstract

Organizations increasingly retain large corpora of customer-interview transcripts, but an archive does not become decision evidence merely because it can be searched or summarized. Fragmenting accounts into decontextualized themes can erase individual trajectories, person-situation contingencies, contradictions, and rare but consequential signals. This problem is also germane to personality science, where inference from aggregate patterns to persons requires particular care. This article develops Customer Interview Intelligence (CII), a contextualist-pragmatic, codebook-based framework for transforming interview evidence into bounded findings, opportunity hypotheses, and defensible next steps. CII integrates directed and inductive qualitative content analysis, constant-comparative logic, case-by-code matrices, negative-case analysis, meaning-sufficiency assessment, and evidence-to-decision reasoning. Its 10-stage protocol begins with a decision-and-evidence charter and corpus audit, preserves a whole-case representation alongside cross-case coding, and ends with a recommendation calibrated to uncertainty and reversibility. Two linked artifacts make the inferential chain inspectable: an inference map that separates customer expression, coded observation, finding, explanation, opportunity, recommendation, and test; and a dual-layer evidence-to-action ledger that records excerpts, locators, context, rival interpretations, contradictions, confidence judgments, decision premises, and revision history. The framework treats corpus prevalence, expressed intensity, consequence severity, strategic fit, behavioral grounding, and potential business impact as distinct dimensions rather than a single score. A synthetic proof of concept illustrates how this separation changes the conclusion drawn from apparently conflicting preferences for product automation. CII does not estimate population prevalence, validate causal explanations, or guarantee decision quality. It offers a testable architecture for preserving individual differences while making qualitative synthesis more traceable, transparent, contradiction-sensitive, and useful for decisions.