How 1-800-Flowers.com improved customer care CSAT with Decagon chat and voice agents
1-800-Flowers.com ran a live proof-of-value pilot of Decagon against another finalist and selected it to automate customer care across chat and voice. The pilot closed with CSAT nearing 93%, ahead of the company's earlier automation.
The problem
1-800-Flowers.com wanted to deliver a personalized 'gifting concierge' experience at scale, with tone suited to the occasion, such as sympathy versus birthday purchases. Its homegrown automation produced low CSAT and weak containment, and rigid IVR systems often misrouted callers whose wording did not match expected inputs.
How they did it
- Define two evaluation metrics up front: containment rate and CSAT.
- Run a 45-day live A/B proof of concept against one other finalist through the Harry & David peak season.
- Work with customer care, brand and marketing stakeholders to define tone, language and specific agent behaviors.
- Write the agent's policies as Agent Operating Procedures in plain natural language so non-engineers can read and audit them.
- Use Watchtower to continuously audit agent outputs and flag issues in real time.
- Keep the vendor team involved after launch in AOP updates and quality reviews, then extend to more brands.
Results
- As reported by Decagon, the pilot closed with CSAT nearing 93%, double-digit points higher than previous automation.
- As reported by Decagon, Decagon outperformed the other finalist on both containment and CSAT during the 45-day evaluation.
- Agents now support 1-800-Flowers.com, Harry & David and Shari's Berries, with expansion planned to Cheryl's Cookies and other brands.
As reported by the source (Decagon customer story); AgentGid did not measure these figures.
This suits a mid-to-large support team with seasonal peaks, chat and voice volume, and care and brand staff who can write and review the agent's policies. Note that Decagon has no public pricing and the CSAT and containment wins are vendor-reported, so run your own head-to-head pilot. For a cheaper start, Rasa (Free) is open source and ranks first in Support, though it needs engineering work.
The agent used here
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