How Chime scaled chat and voice member support with Decagon
Chime, a US fintech, picked Decagon to automate member support on both chat and voice after comparing several vendors. The company reports high resolution rates, lower support costs and better satisfaction scores.
The problem
Chime's growth brought more support volume and higher member expectations. The team needed to scale quickly without losing its member-first approach. It evaluated technology partners for chat and voice on response accuracy, automation rate and support NPS.
How they did it
- Run a selection process for chat and voice vendors, scoring accuracy, automation rate and support NPS.
- Choose a single platform, Decagon, to cover both chat and voice.
- Have Decagon's team work with Chime's product and operations teams for fast feedback and iteration.
- Automate frequent, high-friction use cases first: card replacement, deposit status updates and SMS subscription management.
- Use Agent Operating Procedures so CX teams can adjust agent behavior in natural language while technical teams keep control of core logic.
- Use Insights and Ask AI to surface member feedback for product and support improvements.
Results
- As reported by Decagon: 70% chat and voice resolution, a 30 sNPS, and a 60% decrease in customer support costs.
- As reported by Decagon: chat consistently saw 70%+ resolution, with hundreds of thousands of messages automated in the first two weeks.
- As reported by Decagon: voice scaled to more than 1 million calls per month with no reliability issues, at consistently nearly 70% resolution.
- Chat and voice share one knowledge base and feedback loop, so improvements roll out across both channels faster.
As reported by the source (Decagon customer story); AgentGid did not measure these figures.
This setup suits a large support org with high chat and voice volume, a product and ops team to work with the vendor, and room for a sales-led purchase. Decagon has no public pricing, and the 70% resolution and 60% cost-cut figures are vendor-reported. Smaller teams could look at Rasa (Free), which has a free Developer Edition capped at 1,000 external conversations a month.
The agent used here
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