How OBI handled peak-season service calls with Parloa voice agents
European home-improvement retailer OBI deployed Parloa voice agents in several countries to answer routine calls and route complex ones. Parloa reports high monthly volume, solid CSAT and accurate routing.
The problem
The service team faced heavy daily call volume that tripled in the spring peak. Wait times were long, and limited capacity put both service quality and staff under strain.
How they did it
- Connected the agents to inventory, e-commerce, CRM and logistics systems through standard APIs.
- Launched local agents by country: 'Paul' in Germany, 'Marek' in Poland and 'Balász' in Hungary.
- Had agents answer routine questions and route complex ones to human experts with context.
- Worked with integration partner MUUUH! on the implementation.
Results
- Parloa reports 45,000 monthly inquiries handled by AI agents and 89% customer satisfaction (CSAT).
- 20% automation rate (calls resolved end-to-end) and 99% routing accuracy.
- In Poland, Marek has handled more than 100,000 inquiries, and shopping-by-phone volume rose by 30%.
As reported by the source (Parloa customer story); AgentGid did not measure these figures.
This suits a large retailer with multi-country call volume, API access to inventory, CRM and logistics systems, and an integration partner, as OBI used. Parloa publishes no rates (usage-based, demo required), and the 20% automation rate, 89% CSAT and 99% routing figures are vendor-reported, so budget with care. For a smaller team, Rasa is Free for one bot up to 1,000 conversations a month and is open source.
The agent used here
Similar use cases
How Sun & Ski Sports used a Sierra agent for service and product advice
Texas outdoor retailer Sun & Ski Sports launched a Sierra agent called 'Sunny'. It started with returns and order status and grew into giving product advice on product pages. Sier…
Sierra reports a 3x increase in product page conversion for customers who engage with the agent.
Triage support tickets with an n8n AI Agent and confidence-based routing
An n8n workflow cleans and validates incoming tickets with plain code nodes, uses an AI Agent only to classify urgency and type, and routes each ticket based on how confident the …
The suggested routing: above 0.85 processes autonomously, 0.6-0.85 is processed but flagged for review, and below 0.6 goes to a human.
How Substack automated Tier 1 reader and publisher support with Decagon
Substack used Decagon's AI agent to handle repetitive support requests such as cancellations and email imports. Decagon reports that most inquiries are now resolved without a huma…
Decagon reports that the agent resolves more than 90% of user questions without human intervention.