How Hunter Douglas turned AI support chats into revenue with Decagon
Hunter Douglas Group, a window coverings maker with ecommerce brands in 11 countries, replaced rigid phone menus and manual support with localized Decagon AI agents across chat, email and voice. The company reports that the agents handle a share of inbound volume and are linked to higher order values and new revenue.
The problem
Customers bought online but still had to call or email for help with measurements, delivery and installation, which slowed resolution and raised costs. Existing voice systems used rigid IVR menus that felt outdated. Any automation also had to work across several countries and brands, each with its own languages and tone.
How they did it
- Evaluate vendors and test the platform within weeks.
- Define agent behavior with Agent Operating Procedures, supported by built-in testing and analytics.
- Start in the UK with chat, then add email and voice, scaling from 10 percent of traffic to full volume in a week.
- Deploy localized agents per market, with named personalities, native accents and local tone.
- Connect the agents to the existing stack: Zendesk, NICE CXone and Shopify.
- Have local teams run daily quality checks and write their own procedures for their market.
Results
- As reported by Decagon: more than $1 million in revenue came from conversations fully handled by AI without escalation to a human.
- As reported by Decagon: customers who engaged with an agent had an average order value 85% higher than customers who did not.
- As reported by Decagon: agents deflect 40% of inbound volume on average across markets, with CSAT described as strong.
- The agent also guides customers step by step through installing blinds at home.
As reported by the source (Decagon customer story); AgentGid did not measure these figures.
This fits a mid-to-large retailer with multi-market support, existing Zendesk or Shopify tooling and local staff to write procedures and review quality daily. Decagon has no public pricing (custom, via sales), and the revenue, order-value and 40% deflection figures are vendor-reported. A smaller team could try Rasa (Free), which is open source but needs developer time.
The agent used here
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