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Case Study: How Notion transforms millions of customer inquiries with Decagon

Notion Case StudySourced & dated by Case Study Desk
Key facts · TL;DR
Company
Notion
Industry
Technology
Challenge
Choosing the right agent for ~1M inquiries a year
Headline result
Notion improves ticket resolution up to 34% and doubles deflection with Decagon

Key results

34%
Faster ticket resolution
up to
2x
Increase in deflection
3.4%
Ask-for-human rate
average

The challenge

Notion handles roughly one million customer inquiries a year and wanted to elevate CX beyond a transactional function. As an AI-first company, the challenge was selecting the right agentic solution rather than building capabilities in-house.

The solution

After a rigorous RFP evaluating native generative AI, tooling and UI, depth of integrations, product roadmap, and partnership, Notion selected Decagon's chat agent with intelligent routing to reach the right expert faster.

We conducted a rigorous RFP process, evaluating everything from interaction quality and user interface to the depth of integrations, product roadmap, and the caliber of engagement and partnership offered. Decagon stood out across the board.

EA
Emma Auscher
Global Head of Customer Experience, Notion

The results, in context

The case study reports ticket resolution time improved up to 34% and a 2x increase in deflection, with an average ask-for-human rate of 3.4%. First-touch resolutions rose while agent workload decreased, freeing the team for higher-value work.

Products used

Decagon Chat