/voice-of-customer-miner

Voice-of-Customer Miner — Worked Example

All companies, quotes, URLs, and figures are fictional — including the verbatims, which are invented here purely to show the format. (In a real run, an invented verbatim is the cardinal sin; in a fictional worked example, the fiction is declared.) Same scenario as the suite's other examples: Fieldlight, expanding into FSM, needs to know whether quoting is really the right lead move — and what Wrenchline's customers are angriest about.

Invocation: Mine voice-of-customer for Wrenchline and DispatchCrow — informs whether quoting is the right first module for our FSM expansion. Open sweep, but flag anything about quoting.

Voice-of-Customer Snapshot

1. Scope

Products mined: Wrenchline, DispatchCrow Decision supported: whether quoting is the right first module for Fieldlight's FSM expansion Sources swept: two B2B review sites (skew: negative, ops-manager heavy), both iOS/Android app stores (skew: technician anger after updates), one trades-owner forum (skew: owner-operators, small shops) As-of date: 2026-07-31

2. Need Themes

Theme: quote the job before leaving the driveway

• Frequency: recurring — across both review sites and the forum, 20+ mentions in 6 months
• Verbatim: "my guy finishes the walkthrough and then sits in the truck for 25 minutes building the quote on his laptop" — [review site, URL]
• Verbatim: "if I don't get the quote out same-day I lose the job to whoever does" — [forum thread, URL]
• Who says it: owners and office managers at 10-50 tech shops — Inference (reviewer titles where shown)
• Reading: speed-to-quote is a revenue event, not an admin task — Inference; a quoting tool that starts from the scheduled job's data (already in the calendar) attacks the delay directly.

Theme: techs need the app to survive a parking lot

• Frequency: recurring — dominant complaint cluster in both app stores
• Verbatim: "three taps to close a job became seven after the update. my techs just stopped closing jobs" — [app store review, URL]
• Verbatim: "works great in the office wifi. useless in a parking garage" — [app store review, URL]
• Who says it: field technicians directly (app stores are the one source where techs, not buyers, speak) — Fact (reviewer context)
• Reading: the buyer evaluates the dashboard, but the tech decides adoption — Inference; app-store voice is the adoption early-warning channel the review sites miss.

Theme: getting money in without chasing it

• Frequency: concentrated — one long forum mega-thread, some review-site echo
• Verbatim: "the work is done in June, the check clears in September" — [forum thread, URL]
• Who says it: owner-operators — Fact (forum section)
• Reading: invoicing/payment friction reads as a cash-flow problem, not a software feature — Inference; supports payments as the follow-on to quoting, but the concentration (one thread) caps confidence.

3. Competitor Weak Points

• Wrenchline: "my techs won't use it" — recurring, 40+ reviews since January across both review sites — [URL] (consistent with the snapshot's weakness read; now in customers' own words)
• Wrenchline: implementation pain — "we paid for eight weeks of setup and still did the data import ourselves" — recurring — [URL]
• DispatchCrow: dispatch depth at scale — "fine until we hit 40 techs, then the board falls over" — concentrated in upmarket-switcher reviews — [URL]

4. Switching Triggers

• Push (off Wrenchline): a failed update or forced tier migration is the named event in most switch stories — Inference (review mining, 8 switch narratives)
• Pull (to DispatchCrow): "we were live the same afternoon" — same-day self-serve start recurs as the pull — Fact (their reviews, URLs)

5. So What?

• Opportunity hypotheses:
  1. Shops lose winnable jobs in the gap between walkthrough and quote — Inference, confidence: high (recurring across source types)
  2. Tech-app usability decides adoption before any feature comparison does — Inference, confidence: high
  3. Cash-flow pain makes payments the natural second module — Inference, confidence: low (single-thread concentration)
• Battle-card-ready weaknesses:
  1. Wrenchline tech-app resistance — evidence quality: high (recurring, two source types)
  2. Wrenchline implementation pain — evidence quality: medium (recurring but review-site only, and reviewers skew negative)
• Assumptions to validate in real interviews:
  1. The quote delay is caused by data re-entry (not by pricing judgment, which software can't fix)
  2. Techs actually influence purchase/renewal decisions the way app-store anger implies
  3. The cash-flow thread generalizes beyond owner-operators

Why this example works

• Theme names contain no features. "Quote the job before leaving the driveway" — not "quote builder." The need framing leaves discovery room for solutions the reviews never imagined (voice quoting? photo-to-line-items?).
• Each source's skew is named and used. App stores skew toward technician update-anger — and that's exactly why they're the one channel where the tech, not the buyer, speaks. Bias notes aren't disclaimers; they're reading instructions.
• Frequency honesty changed a conclusion. The cash-flow theme is vivid but concentrated in one mega-thread, so the payments hypothesis ships at low confidence — one articulate thread is not a theme. That's the discipline the Common Pitfalls warn about, applied.
• The output lands as hypotheses, not verdicts. The decision ("is quoting the right lead?") gets strong support, but the last section routes everything to real interviews — the bridge from competitive intelligence to discovery that this skill exists to build.