PayShepherd cuts response times by 40% and achieves 100% CSAT with Intercom
PayShepherd's existing Jira Service Management platform was overwhelmed by a fast-growing customer base, causing delayed responses, decreased customer satisfaction, poor reporting visibility, manual workflows, and difficulty tracking complex tickets that frequently duplicated effort across agents.
Jira Service Management lacked robust reporting, required manual workflows, and had no clear mechanism to track complex long-running tickets, resulting in duplicated agent effort and customer frustration.
Implementing Intercom delivered a 40% reduction in response times, 100% CSAT in March 2024, a 15% improvement in operational efficiency, a 30% decrease in duplicate tickets, a 20% increase in help center engagement, and 30% of routine support tasks now handled by workflow automations.
Show all 7 reported metrics
Frequently asked questions
What did this team achieve with this AI workflow?
Implementing Intercom delivered a 40% reduction in response times, 100% CSAT in March 2024, a 15% improvement in operational efficiency, a 30% decrease in duplicate tickets, a 20% increase in help center engagement, a…
What tools did this team use?
Intercom, Jira Service Management, Fin AI Agent.
What results were reported?
Response time reduction: 40%; Help center engagement increase: 20%; CSAT score: 100%; Operational efficiency improvement: 15% (source-reported, not independently verified).
What failed first in this deployment?
Jira Service Management lacked robust reporting, required manual workflows, and had no clear mechanism to track complex long-running tickets, resulting in duplicated agent effort and customer frustration.
How is this customer support AI workflow structured?
Customer contacts via help center or chatbot → Workflows route and automate conversations → Agents handle escalated tickets → Reporting dashboards output metrics.