Ausgangslage: Überlastetes Support-Team
The client, a specialized online retailer for office supplies with 45 employees, was struggling with rising ticket volumes. After introducing new payment methods and completing a Magento 2 migration, ticket volume increased by 35% within six months.
The existing team of eight support agents had reached its capacity limit. The average first response time was 4 hours, leading to declining customer satisfaction scores. Manual triage was the biggest bottleneck: 60% of tickets were routine inquiries that were answered identically by every agent.
The goal was clear: reduce manual preliminary work and focus the team on complex cases without hiring additional staff.
Umsetzung: KI-gestützte Ticket-Triage
We developed a custom AI solution integrated directly into the Magento back office and the ticketing system. The implementation took place in three phases over a period of eight weeks.
- Phase 1 (Weeks 1–2): Data Analysis. We analyzed over 10,000 historical tickets to identify patterns and frequent questions.
- Phase 2 (Weeks 3–6): Model Training. A Large Language Model was trained using the client’s specific product catalog data and support scripts.
- Phase 3 (Weeks 7–8): Integration & Pilot. The AI was implemented as a “First-Response Bot” that automatically categorizes tickets and generates answer suggestions.
The AI immediately checks incoming tickets for urgent outages, order status inquiries, or returns. Routine inquiries are answered automatically or forwarded to the human agent with a precise answer suggestion.
Ergebnis: 40 % weniger manuelle Arbeit
After full implementation, a clear success in efficiency gains was evident. The figures after three months of operation confirm that the targets were met.
| Kennzahl | Vorher | Nachher |
|---|---|---|
| Manuelle Triage-Zeit | 100 % | 60 % |
| Erstreaktionszeit | 4 Stunden | 15 Minuten |
| Tickets pro Tag pro Agent | 25 | 40 |
| Kundenzufriedenheit (CSAT) | 4.1 / 5.0 | 4.6 / 5.0 |
40% of support work was eliminated through automated triage and answering of routine questions. The team was able to focus entirely on complex customer inquiries and return cases, which significantly improved the quality of handling.
Learnings: Mensch und Maschine
The success of the solution is based on the clear separation of routine and complexity. The AI is not a replacement for human empathy, but a tool for relief.
- Data quality is crucial. The cleaner the historical ticket data, the more precise the AI suggestions.
- Feedback loops are necessary. The team reviews the AI’s answers daily to continuously improve the model.
- Change Management. Acceptance within the team increased once it became clear that the AI does not cut jobs, but reduces workload and takes over tedious tasks.
The investment in AI triage paid for itself within four months by avoiding the hiring of two new support employees.
Results
- -40 % – Manuelle Support-Arbeit
- 15 min – Durchschnittliche Erstreaktionszeit
- +60 % – Tickets pro Agent pro Tag
- 4.6/5.0 – Kundenzufriedenheit (CSAT)