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Project: analyze Parcel Window pickup interviews

Last updated: 5 Oct 202618 min read
project
AdvancedBy AITrove Editorial

Analyze a fictional Parcel Window study about choosing parcel pickup times. There are 12 consented interviews and 47 coded excerpts. The allowed use is aggregate product research. Build a reproducible, human-reviewed analysis from bounded transcript segments, not a contact list or a marketing audience. The deliverable is a decision memo with evidence links, disagreement records, counterexamples, and a testable next step.

Prepare and code the records

Remove an unrelated third-party phone number from P-07's transcript before model analysis. Keep P03-S18 as one contextual segment because P-03 corrects an initial claim; mark its noisy word for audio review. Use codebook v2: WINDOW_CLARITY includes uncertain displayed intervals and excludes a slow page when the interval was understood. Coder A and coder B assign different codes to P03-S18; the review keeps both WINDOW_CLARITY and NOTIFICATION_TIMING, each tied to its own span. P09-S04 stays unresolved until the recording is checked.

Reconcile themes and propose a check

Nine window-clarity excerpts come from five distinct participants, including four repeated mentions by P-07. Two participants report a language barrier. P-11 prefers a broad pickup interval; the counterexample stays beside the proposed theme. State 'five of the 12 interviewed participants' only as a sample description, not a population rate. Propose a plain-language range with an optional narrower alert. Test comprehension and pickup planning before claiming the feature improves outcomes.

Output
Study: 12 interviews; 47 coded excerpts
WINDOW_CLARITY: 9 excerpts, 5 distinct participants
LANGUAGE_BARRIER: 2 distinct participants
Counterexample: P-11 prefers flexibility
P09-S04: unresolved audio
Decision: prototype range plus optional alert; measure before rollout

Performance and review cost

Scope screening and segmenting are O(N) in the transcript records; direct coding against K codes can be O(NK). Participant deduplication across M coded excerpts is O(M) expected time with a set. Human reviewers should inspect every disputed span and a sample of accepted labels. The project passes only when each theme has segment references, its contrary cases, a stable codebook version, reconciled counts, and an explicit boundary between interview evidence and product hypothesis.

Common Mistakes

  • Do not export incidental identifiers for an unrelated use.
  • Do not discard P-03's correction or P-11's counterexample.
  • Do not count nine excerpts as nine people.
  • Do not claim a prototype has already reduced missed pickups.

Related lessons

prompt engineering
qualitative research
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