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Narrative-Driven Analysis: Making Meaning Measurable with AI
Humans think, remember, and decide through stories. LLMs now make interactive, scalable, and grounded narrative analysis feasible—transforming qualitative consumer data into first-class evidence that captures what hedonic scores miss.
By Dr. John Ennis, PhD — Aigora
Narrative-Driven Analysis: Making Meaning Measurable
Humans think, remember, and decide through stories. Decades of work show that narratives—actors, goals, obstacles, turning points—organize memory, transport attention, and shape judgment (Bruner, 1990; Schank & Abelson, 1977; Green & Brock, 2000; McAdams, 1993; Labov & Waletzky, 1967). If sensory science is the science of lived experience, then narrative is a first-class data type, not an anecdotal garnish.
“If sensory science is the science of lived experience, then narrative is a first-class data type, not an anecdotal garnish.”
What's New
Large language models (LLMs) make interactive, scalable, and grounded narrative analysis feasible. With retrieval-augmented generation (RAG), a researcher can ask the model to structure transcripts, diaries, and open-ends into explicit story grammars—with evidence links to the original text—and to propose rival narratives that can then be tested experimentally (Lewis et al., 2020).
The Narrative Continuity Test
An important caveat accompanies this power: the persistence and coherence of LLM-generated narratives should not be taken for granted. A proposed “Narrative Continuity Test” (NCT) identifies five axes that any persistent AI interlocutor must satisfy: situated memory (retaining context across sessions), goal persistence (maintaining objectives despite external pressure), autonomous self-correction (identifying internal inconsistencies without prompting), stylistic/semantic stability (preserving a consistent “voice” over time), and persona/role continuity (adhering to assigned roles) (NCT research, 2025). Current LLMs fail on most of these axes—their persona fidelity is fragile, they lack intrinsic self-repair mechanisms, and optimization pressures such as RLHF can fragment rather than refine an emerging identity. For narrative-driven sensory analysis, this means the researcher must remain the guarantor of narrative coherence and construct validity; the LLM is a powerful structuring tool, but its outputs require active curation to ensure that the stories it surfaces are grounded, stable, and genuinely illuminating rather than fluent artifacts of distributional drift.
Five Axes of the Narrative Continuity Test
- 1. Situated memory — Retaining context across sessions
- 2. Goal persistence — Maintaining objectives despite external pressure
- 3. Autonomous self-correction — Identifying internal inconsistencies without prompting
- 4. Stylistic/semantic stability — Preserving a consistent “voice” over time
- 5. Persona/role continuity — Adhering to assigned roles
A Practical Workflow
The Six-Step Narrative Analysis Workflow
- 1. Elicit mini-stories, not just opinions. In home-use tests or EMA, ask participants for the short story of their last use (setting, companions, goal, obstacles, outcome, feelings).
- 2. Structure with story grammar. Use the LLM (constrained by your corpus) to tag setting, characters, goals, obstacles, turning points, outcomes, affect, sensory imagery—and to cite verbatim IDs for every tag.
- 3. Surface rival narratives. Request 3–6 data-backed narratives (e.g., weekday calm-down ritual, weekend social flex, functional fix) with prevalence estimates, signature cues, enabling contexts, and contradictory evidence.
- 4. Quantify without flattening. Link narratives to hedonic/JAR/CATA and to instrumental data via mixed models. Track narrative coverage (share of corpus each narrative explains), transportation markers (imagery density, temporal connectives, first-person verbs), durability indicators (mentions of repeat intention; aftertaste/afterfeel over time), ritual fit (stable time/place/companions + positive affect), and provenance resonance (valence shift when origin/story is revealed).
- 5. Design counterfactuals. Turn narratives into testable changes: If we increase the “fresh start” volatile cluster and reduce stickiness, does the weekday-ritual narrative gain share without hurting weekend-social? Pre-register criteria; let experiments arbitrate.
- 6. Report transparently. Summaries should pair a concise narrative synopsis with 2–3 anchor quotes, the enabling cues and contexts, recommended design moves, and an uncertainty band. No evidence, no claim.
Key Metrics for Narrative Analysis
Quantifying narratives without flattening them requires tracking multiple dimensions simultaneously:
Narrative Coverage
Share of corpus each narrative explains—ensuring the identified stories account for the breadth of consumer experience.
Transportation Markers
Imagery density, temporal connectives, first-person verbs—signals of narrative engagement and immersion.
Durability Indicators
Mentions of repeat intention; aftertaste/afterfeel over time—measuring whether delight persists beyond the moment.
Ritual Fit
Stable time/place/companions + positive affect—capturing how products embed into daily routines and rituals.
Provenance Resonance
Valence shift when origin/story is revealed—measuring how the narrative context around a product shapes its perception.
Why This Matters
Narrative-driven analysis captures durability of delight and contextual fit that single-moment hedonic scores miss, and it aligns with our core mission: connecting sensory cues to human meaning. It also keeps the researcher—not the model—in charge of constructs and claims.
“Narrative-driven analysis captures durability of delight and contextual fit that single-moment hedonic scores miss, and it aligns with our core mission: connecting sensory cues to human meaning. It also keeps the researcher—not the model—in charge of constructs and claims.”
Frequently Asked Questions
What is narrative-driven analysis in sensory science?
Narrative-driven analysis treats consumer stories as first-class data rather than anecdotal garnish. Decades of research show that narratives—actors, goals, obstacles, turning points—organize memory, transport attention, and shape judgment. By using LLMs to structure transcripts, diaries, and open-ended responses into explicit story grammars with evidence links, researchers can capture durability of delight and contextual fit that single-moment hedonic scores miss. This approach connects sensory cues to human meaning, which is the core mission of sensory science.
How do LLMs help with qualitative sensory data?
Large language models make interactive, scalable, and grounded narrative analysis feasible. With retrieval-augmented generation (RAG), a researcher can ask the model to structure transcripts, diaries, and open-ends into explicit story grammars—with evidence links to the original text—and to propose rival narratives that can then be tested experimentally. The LLM acts as a powerful structuring tool, but its outputs require active curation by the researcher to ensure that the stories it surfaces are grounded, stable, and genuinely illuminating rather than fluent artifacts of distributional drift.
What is the practical workflow for narrative analysis?
The six-step workflow is: (1) Elicit mini-stories from participants about their last product use, including setting, companions, goals, obstacles, outcomes, and feelings. (2) Structure responses with story grammar using the LLM to tag narrative elements and cite verbatim IDs. (3) Surface 3-6 rival data-backed narratives with prevalence estimates. (4) Quantify without flattening by linking narratives to hedonic/JAR/CATA and instrumental data via mixed models. (5) Design counterfactuals by turning narratives into testable changes with pre-registered criteria. (6) Report transparently with anchor quotes, enabling cues, design moves, and uncertainty bands.
What is the Narrative Continuity Test and why does it matter?
The Narrative Continuity Test (NCT) identifies five axes that any persistent AI interlocutor must satisfy: situated memory, goal persistence, autonomous self-correction, stylistic/semantic stability, and persona/role continuity. Current LLMs fail on most of these axes—their persona fidelity is fragile and they lack intrinsic self-repair mechanisms. For narrative-driven sensory analysis, this means the researcher must remain the guarantor of narrative coherence and construct validity; the LLM is a powerful structuring tool but not an autonomous analyst.
How does narrative analysis differ from traditional hedonic scoring?
Traditional hedonic scoring captures a single-moment snapshot of liking, while narrative-driven analysis captures durability of delight and contextual fit. It tracks narrative coverage (share of corpus each narrative explains), transportation markers (imagery density, temporal connectives, first-person verbs), durability indicators (repeat intention, aftertaste/afterfeel over time), ritual fit (stable time/place/companions with positive affect), and provenance resonance (valence shift when origin/story is revealed). This richer framework aligns with the core mission of connecting sensory cues to human meaning.
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