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Customer Support·2–6 weeks·3–4 weeks

Voice-of-Customer Analysis

Nobody on the team reads 50,000 tickets per quarter. This does. Clusters into themes, ranks by volume + sentiment, flags emerging issues before they hit the changelog. Output: a one-page weekly memo for product + a Slack alert when a new theme breaks 1% of volume.

Time to detect emerging issue
Days, not weeks
Product team unblocked
Weekly digest
Tickets analysed
100%
The problem

What this fixes.

  • Product team doesn't see what support sees

  • Bug clusters surface 6 weeks late

  • Roadmap decisions are loud-voice driven

How it works

Three jobs, on rails.

Cluster

Themes

Topic modelling across tickets / calls / reviews / NPS.

Rank

Volume + sentiment

Top issues weighted by hit rate and CSAT impact.

Alert

Emerging issues

Slack ping when a new cluster crosses 1% of weekly volume.

From signed proposal to live

The path.

01

Connect ticket history, call transcripts, app-store reviews and NPS open text.

02

Calibrate the topic model on three months of historical data.

03

Set the alert threshold for new clusters (we usually start at 1% of weekly volume).

04

Friday: the memo lands; bugs become P-tagged in the issue tracker the same day.

A real moment

One scenario, one outcome.

The scenario

Across Monday–Thursday, 87 customers mention 'mobile login loop' across tickets, App Store reviews and a Reddit thread.

The outcome

Friday memo surfaces it as cluster #1. Slack alerts trip at threshold mid-week. The bug ships to test on Monday — usually a 4-week lag, now 4 days.

Engagement

Scoped on a call.

Delivery

3–4 weeks

Engagement model

Pilot → retainer

Scope confirmed in a 30-minute call. Fixed scope, fixed timeline before you sign. We'll send a one-page proposal within 48 hours.

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