Fetch Achieves 26% More Customer Support with Same Workforce and 3.9x ROI Using Forethought
Fetch's rapidly expanding user base drove recurring ticket surges whenever new app features launched. About 30% of tickets were easily answered FAQ-type questions and another 20% required agents to gather additional information from users, creating a high reopen rate. The team was deeply skeptical of chatbots after poor experiences with clunky keyword-based tools.
Fetch initially deployed Forethought Triage to predict ticket content and send automated email responses, but this approach only addressed less than 1/3 of the basic ticket types it was meant to deflect.
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After deploying Forethought Solve as their AI agent Scout, Fetch achieved 26% more customer support capacity with the same workforce and a 3.9x ROI, automating 316,000 ticket actions for $90,000 in 11 months, with CSAT scores for fully automated chats as good or better than those of human agents.
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Frequently asked questions
What did this team achieve with this AI workflow?
After deploying Forethought Solve as their AI agent Scout, Fetch achieved 26% more customer support capacity with the same workforce and a 3.9x ROI, automating 316,000 ticket actions for $90,000 in 11 months, with CSA…
What tools did this team use?
Forethought, Forethought Solve, Scout, Zendesk.
What results were reported?
Customer support capacity with same workforce: 26%; ROI: 3.9x; Ticket actions automated: 316,000; Cost of automation over measurement period: $90,000 (source-reported, not independently verified).
What failed first in this deployment?
Fetch initially deployed Forethought Triage to predict ticket content and send automated email responses, but this approach only addressed less than 1/3 of the basic ticket types it was meant to deflect.
How is this customer support AI workflow structured?
Customer submits ticket via email → Triage predicts ticket content → Automated email deflects simple tickets → Scout answers wide range of questions → Human assistance for complex cases.
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