How Whop deflects support tickets and guides product sprints with Decagon
Whop, a social commerce platform for creators, replaced human-only support and a rigid decision-tree chat tool with Decagon's AI agent. The company reports high ticket deflection and uses ticket tagging to steer product fixes.
The problem
Whop's support team handled 5,000-10,000 tickets every week, with spikes around seasonality and product updates. Human agents kept repeating answers to basic questions, and the old chat tool followed rigid decision trees and could not take action for customers. Hiring more staff to answer the same questions was not efficient.
How they did it
- Replace the decision-tree chat tool with Decagon Chat as the first line of support.
- Feed user and product-usage metadata to the agent so answers fit each persona (creators, buyers, communities).
- Work with the Decagon team to design the support experience for the marketplace.
- Let Decagon automatically tag every ticket to a product surface.
- Share the tag data with product managers, engineers and leadership to inform sprint planning and bug triage.
Results
- Ticket deflection of 65–70% soon after adoption, as reported by Decagon (vendor customer story).
- Payment-related tickets fell by 50% after insights from ticket tagging guided targeted fixes, as reported by Decagon.
- A spike in payout modal tickets exposed a bug that the team fixed quickly, before more users were affected.
- Many customers reportedly did not realize they were talking to AI.
As reported by the source (Decagon customer story); AgentGid did not measure these figures.
This suits a team with thousands of weekly tickets, product-usage data to feed the agent, and PMs who will act on tag reports. Decagon has no public pricing (contact sales), and the 65–70% deflection and 50% payment-ticket drop are vendor-reported. Smaller teams could try Chatwoot Captain (Free + $19/mo), which can also be self-hosted.
The agent used here
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