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Enterprise research

Expert transcripts turned into a quantitative product map

We converted the transcript archive into a scored product matrix, built a multi-dimensional map from those scores, and chose a product set that covers the space for the consumer test.

Printed transcript pages arranged on a table with clustering marks

Evidence at a glance

Evidence type
Delivered analysis
Organization scope
A major CPG company
Documented boundary and output
This case covers the delivered analysis: transcript synthesis, attribute scoring, the product map, and product selection. The company and category stay confidential.

The situation

Expert evaluations lived as transcripts and free-text notes. The team needed scores they could compare across products before consumer testing.

The archive held expert qualitative evaluations across a large product set. The open technical problem was how to turn that text into a stable scoring matrix, a map of the product space, and a selection method that maximizes coverage for a later consumer test.

The approach

  1. Synthesize the expert record

    Each product got one audited description built from the source transcripts, with every claim checked against the text before acceptance.

  2. Find attributes and score them

    The attribute set came from the expert language itself. Every product was then scored on a shared scale, with a compliance audit on each attribute.

  3. Map the space and pick the set

    The score matrix became a multi-dimensional product map. From that map we selected the subset that best covers the space for testing.

System or method for this case

Flow from transcript synthesis through scoring and mapping to test-set selection

What we delivered

  • One audited product description per sample, grounded in the source transcripts
  • Attribute catalog induced from expert language rather than a fixed lexicon
  • Shared-scale score matrix with compliance checks on every attribute
  • Multi-dimensional product map from the score matrix
  • Spread-optimal product subsets for consumer-test design

Technical pieces

Transcript synthesis with expert-in-the-loop auditAttribute induction from source languageRoster-anchored scoringDimensionality reduction for the product mapCombinatorial selection for coverage

What changed

The team left with a scored product matrix, a multi-dimensional map from those scores, and a product set chosen to cover the space for the consumer test.

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