Support Summary

Synthesizes resolved tickets into weekly/monthly patterns

// The problem

Support resolves 500 tickets/week but which product area has most issues, which features are most requested — gets lost. Product team doesn't get the signal.

// What it does

Analyzes all resolved tickets. Produces weekly reports of top 10 themes, most requested features, churn-related signals, agent performance.

// Value

Voice of Customer data-driven, feature prioritization moves beyond guessing

Support data feeds real input into product decisions; a semantic clustering layer on top of Zendesk Explore.

Setup time: 3-5 gün

// How it works

  1. 01

    Çözülen ticket'ları çek

  2. 02

    Theme clustering (LLM)

  3. 03

    Top 10 sorun + frekans

  4. 04

    Feature request extraction

  5. 05

    Agent performans (TTR, CSAT)

  6. 06

    Ürün/CS/management için ayrı rapor

// Real example

Bir SaaS PM Cuma sabahları rapor: "Bu hafta top 3 sorun: (1) SAML SSO config (42 ticket), (2) Mobile app crash iOS 17.2 (28), (3) Export CSV encoding (19). Top feature isteği: Bulk import (15 mention). Churn-related: "reporting yetersiz" 8 kez geçti."

// Trigger

Cron (haftalık + aylık)

// Output channels

EmailSlackNotionLinearPDF

// Integrations

ZendeskIntercomFreshdeskLinearJiraNotion

// Limits (honest)

  • ×Ticket çözüm kalitesi = pattern kalitesi
  • ×Çok küçük volume'de (<100 ticket/hafta) istatistiksel güç düşük

// SSS

01.Zendesk Explore'dan farkı?+

Explore sayısal raporlar. Biz semantic — "hangi konu artıyor" diye anlatır.

// Tech stack

OpenAI clusteringTicket APIs
~/order-support-summary

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