customer_support · ecommerce · workflow
eJam achieves 80% email support automation and 50% cost reduction with SigmaMind AI
eJam's customer support team was overwhelmed by high ticket volumes across email, chat, social media, and voice for five brands, resulting in extended wait times, inconsistent service from agent variability, and mounting costs from manual repetitive tasks.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Multi-channel ticket intake
Customer support requests arrive across email, chat, and social media channels.
Tools used
SigmaMind AISigma AI
Outcome
SigmaMind AI delivered 80% email support automation by day 60, 50% social media automation by day 30, 50% fewer chat tickets for agents, a 71% reduction in first response time, a 30% reduction in resolution time, a 50% reduction in support costs, and a 95% CSAT score.
Results
Time saved71%
Volume30%
Cost replaced50%
Grounding & classification
Source type: vendor customer story
36 fields verified against source quotes, 2 dropped as unverifiable.
agentic workflowconversational aisentiment analysissupport agentchat transcriptemailsocial media postsupport ticketfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedsource backedworkflow describedecommerceautomation ratecost reductioncustomer satisfactiondeflection rateresolution time reductionresponse time reductionvendor customer storycustomer supportecommerce opsautonomous resolutionextract classify routeintake to triage