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Measure Survey Analysis: Brainshelf NPS Pulse

Scenario

Brainshelf runs a quarterly NPS survey. This quarter, 1,200 subscribers responded, including an open-text “what would you change?” follow-up. The PM wants the analysis to mine open-text for roadmap signal. This demonstrates the tracking analysis pattern plus open-text clustering, and it models the discipline that NPS is a trend metric, not a roadmap. The survey is illustrative; invented numbers and quotes are tagged [fictional].

Source Notes:

  • Fred Reichheld, “The One Number You Need to Grow” (Harvard Business Review, 2003) - the origin of NPS and the explicit framing of it as a loyalty/growth tracking metric, which underpins the “NPS does not tell us what to build” caveat here.
  • Virginia Braun and Victoria Clarke, “Using thematic analysis in psychology” (Qualitative Research in Psychology, 2006) - the thematic-clustering method applied to the open-text responses, including the discipline of grounding themes in actual response excerpts.

Prompt

/survey-analysis
analyze our Q2 brainshelf NPS survey. 1200 subscribers responded. standard
NPS question (0-10) plus an open text "what's the one thing you'd change?"
last quarter's NPS was 18. mine the open text for what we should build next.

Output

Survey Analysis: Brainshelf Q2 NPS Pulse

Executive Summary

NPS rose to 22 this quarter from 18 last quarter (+4), driven by a drop in detractors [fictional]. With N=1,200 the trend is reliable. But NPS tells us sentiment is improving, not what to build - that signal is in the open text, where the dominant theme is recommendation quality consistency (the recs are great sometimes and generic other times). Confidence: High on the trend, Medium on the roadmap implications. The most important caveat: NPS is a tracking metric; the roadmap signal comes from the open-text themes, not the score.

Survey Methodology Summary

  • Sample size (N): 1,200 responses (response rate ~14% of active subscribers [fictional])
  • Recruitment method: Email to all active subscribers
  • Who responded vs. who was invited: Slight skew toward more engaged subscribers (typical for email NPS)
  • Selection bias risks: Churned subscribers are absent; the most dissatisfied may have already left, so NPS is likely flattering
  • Question-design risks: Standard NPS wording; the open-text “one thing you’d change” biases toward complaints over praise

These choices affect interpretation: the trend is trustworthy, but the absolute score is optimistic because churned detractors are gone.

Per-Question Analysis

Q#QuestionDistributionConfidenceWhat it showsWhat it does NOT show
Q1Likelihood to recommend (0-10)Promoters 42% / Passives 38% / Detractors 20% -> NPS 22 [fictional]High (N=1200)Sentiment improving vs. last quarter (NPS 18)Why; or what to build
Q2One thing you would change (open text)870 responses [fictional]MediumRoadmap themes (below)Magnitude of each theme at full-base scale

NPS trend: 18 -> 22 over one quarter [fictional]. With N=1,200 both quarters, a 4-point move is suggestive of a real shift, but NPS is only comparable when response composition is consistent quarter-over-quarter, which is hard to verify here (response rate ~14%, churned subscribers absent). Treat it as directional improvement, not a confirmed significant change.

Persona / Segment Breakdown

SegmentnNPSNote
Monthly plan740 [fictional]19Lower loyalty
Annual plan460 [fictional]27More committed

Annual subscribers are meaningfully more loyal, consistent with self-selection (people who pre-commit a year are more bought-in). Both segments exceed n=30, so the split is defensible.

Open-Text Thematic Clustering

AI-assisted clustering of 870 open-text responses; quotes are illustrative [fictional] stand-ins for what real excerpts would look like. Mention counts approximate.

ThemeApprox. mentionsRepresentative quotesValenceContradicts NPS trend?
Recommendation consistency~310”Some weeks are spot on, some are random” [fictional]MixedNo - explains the passives
Want more niche/indie picks~190”Stop recommending obvious bestsellers” [fictional]NegativeNo
Reading-format options (audiobooks)~150”I mostly listen now, give me audiobook recs” [fictional]RequestNo
Price sensitivity~120”Good but borderline too expensive” [fictional]NegativeMild
Love it / keep going~90”Honestly don’t change anything” [fictional]PositiveReinforces

The top theme - recommendation consistency - is the roadmap signal NPS alone would never reveal. It explains the large passive bucket (38%): people who like Brainshelf but are not promoters because the quality wobbles.

Hypothesis Validation

HypothesisStatusEvidenceConfidence
Sentiment is improving quarter-over-quarterSUPPORTEDNPS 18 -> 22, N=1200 both periodsHigh
We know what feature to build nextNOT TESTED BY THIS SURVEYNPS is not diagnostic; open text gives direction, not validation-

What the Data Does NOT Show

  • Population not represented: Churned subscribers (the most dissatisfied are gone), so NPS is optimistic
  • Questions not answered: Whether fixing consistency would actually move NPS; willingness to pay (despite the price theme)
  • Confounds: The open-text prompt skews toward complaints
  • Follow-up that would close the biggest gap: A targeted survey or behavioral analysis on recommendation-quality variance, and a churned-user study

Prioritized Recommendations

#RecommendationEvidenceConfidenceCounter-evidenceResearch that would strengthen it
1Invest in recommendation consistencyTop open-text theme; explains passivesMediumNPS already rising without itVariance analysis of rec ratings
2Test audiobook recommendationsThird theme, growing format shiftMediumCould dilute focusA demand test on the audiobook segment
3Hold price; monitor the price themePrice is a minority themeLowAnnual NPS is healthyA pricing/willingness-to-pay survey
4Run a churned-subscriber studyNPS misses churned detractorsMediumNoneInterviews with recent churners

Next Steps

  • Treat NPS as a trend dashboard, not a roadmap input; act on the open-text themes
  • Validate the consistency theme with behavioral rec-quality data before committing engineering
  • This analysis can inform where to look next; it cannot, on its own, confirm a feature will move loyalty