Customer Feedback Synthesis

Synthesizes support tickets + NPS + app reviews by theme

// The problem

Customer feedback is everywhere (Zendesk, NPS, App Store, social). Which themes are rising, what to do — manual analysis takes weeks.

// What it does

Combined collection of support tickets + NPS verbatims + App/Play Store reviews + Twitter mentions. Theme-based clustering: 'reporting inadequate', 'mobile app crash', 'price too high'. Shows trend and customer quotes for each theme.

// Value

Voice of Customer analysis 1 week → 1 hour

Product + CS teams prioritize data-driven, evidence instead of intuition.

Setup time: 5-7 gün

// How it works

  1. 01

    Çoklu kaynak çekme (ticket + NPS + reviews + social)

  2. 02

    Embed + clustering (semantic similarity)

  3. 03

    Tema isimlendirme (LLM)

  4. 04

    Her tema için: frekans, trend (haftalık), örnek müşteri quote'ları

  5. 05

    Ürün ekibine prioritize edilmiş feedback raporu

  6. 06

    Aylık otomatik + manuel custom date range

// Real example

Bir SaaS PM Berkay. Aylık rapor: "Top tema bu ay: (1) SSO kurulum zorluğu (42 mention, 3x artış), (2) Mobile crash iOS 17.2 (28), (3) Export CSV encoding Türkçe karakter (19). Müşteri quote: 'SSO setup için 3 saat support ile uğraştık, dokümantasyon yetersiz.'"

// Trigger

Cron (aylık + manuel)

// Output channels

PDFEmailSlackNotionLinear

// Integrations

ZendeskIntercomTypeformApp Store ConnectGoogle PlayTwitter APILinear

// Limits (honest)

  • ×İlk 30 gün baseline kurulum
  • ×Az volume'da (< 100 feedback/ay) clustering zayıf
  • ×Çok dilli feedback için ek konfig

// SSS

01.Support Summary ajanından farkı?+

Support Summary sadece ticket. Bu multi-source (NPS, App Store, sosyal dahil).

// Tech stack

OpenAI embeddingsVector DBMulti-source APIs
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