# How ClassPass uses Decagon

**95%**: “95% cost reduction”

As published on [decagon.ai](https://decagon.ai/case-studies/classpass), July 2026. Captured by usedby on 2026-10-07.

- Company: [ClassPass](https://www.usedby.ai/companies/classpass.md)
- Tool: [Decagon](https://www.usedby.ai/tools/decagon.md)
- Industry: [Sports & Fitness](https://www.usedby.ai/companies/industry/sports-fitness.md)
- Teams: Customer Support, CX Operations

## What the story says

ClassPass uses Decagon's AI chatbot to answer member questions over chat and email, and Agent Assist inside Zendesk to help its support agents. Deflecting tickets let ClassPass extend its chat hours and reduce support costs.

Summary written by usedby from the source page, in English. The figures are those of Decagon and ClassPass, not ours.

> Our solutions enabled ClassPass to greatly increase the efficiency of the support operation through superior deflection. This resulted in large annual savings, significantly reducing operational costs while maintaining high service levels.

- **32%**: “32% Increase in deflection rate”
- **10x**: “we saw 10x higher deflection at launch than we anticipated”

> Though we already had a robust Voice of the Customer program and an understanding of customer inquiries we thought we could deflect, we saw 10x higher deflection at launch than we anticipated.
>
> Sarah Vanden Broek, Director, CX Operations Strategy and AI

## What usedby checked

We compared the story with its live page on 2026-10-07.

- Checked: the figure 95% is printed word for word on the page, near the name of ClassPass.
- Checked: the passage quoted above is copied word for word from the page, near the name of ClassPass.
- Checked: the publication date is read from the page’s own metadata, never guessed.
- Checked: ClassPass uses Decagon. Confirmed line. Latest check across sources: 2026-10-07.
- Not checked: the result itself. We quote it; we did not measure it.

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Source: https://www.usedby.ai/case-studies/classpass-decagon · How we check: https://www.usedby.ai/methodology