eJam achieves 80% email support automation and 50% cost reduction with SigmaMind AI
The problem
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.
Workflow diagram · grounded in source
1
Multi-channel ticket intake
Trigger
Customer support requests arrive across email, chat, and social media channels.
▾ source quote
“eJam partnered with SigmaMind AI to automate key support workflows across email, chat, and social media”
2
Shopify store detection
Ai action
The AI detects which of eJam's five Shopify stores the incoming ticket belongs to.
▾ source quote
“the AI should be able to detect which store is the incoming ticket for. This is a difficult problem as the same customer can have orders across these stores”
3
Intent and sentiment analysis
Ai action
Sigma AI identifies the intent and sentiment of each incoming message.
▾ source quote
“Sigma AI excelled at identifying intents and sentiments, allowing it to hide negative or abusive comments and like positive ones”
4
Social media auto-response
Ai action
Sigma AI auto-responds to all social media comments on platforms like Facebook and Instagram, achieving 50% automation by day 30.
▾ source quote
“Sigma AI was successfully auto-responding to all social media comments on platforms like Facebook and Instagram, resulting in 50% automation by day 30”
5
Email workflow routing
Routing
Email tickets are routed to AI workflows for WISMO, subscription, or order cancellation requests, covering 80% of incoming email tickets.
▾ source quote
“built out AI workflows like WISMO, subscription, or order cancellation requests. These scenarios covered, 80% of the email tickets coming in”
6
Automated response and action
Ai action
Sigma AI responds to incoming tickets, takes relevant actions on the order, tags tickets for CSAT monitoring, and closes or re-assigns them.
▾ source quote
“Sigma AI responded to incoming tickets, took relevant actions on the order, tagged these tickets (for CSAT monitoring), and closed or re-assigned them”
7
Chat deflection and resolution
Ai action
For chat, the AI identifies intent and sentiment to deflect or resolve frequently asked questions in real time, resulting in 50% fewer chat tickets for agents.
▾ source quote
“identify the intent and sentiments of a question and either deflect them or resolve them if it is a frequently asked question. Result - 50% fewer chat tickets for agents to handle”
Reported 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.
Reported metrics
First Response Time (FRT) reduction71%
Resolution time reduction30%
Support cost reduction50%
CSAT score95%
Show all 7 reported metrics
First Response Time (FRT) reduction71%
Resolution time reduction30%
Support cost reduction50%
CSAT score95%
Email support automation rate80%
Social media automation rate by day 3050%
Chat tickets reduction for agents50%
Reported stack
SigmaMind AISigma AI
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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…
What tools did this team use?
SigmaMind AI, Sigma AI.
What results were reported?
First Response Time (FRT) reduction: 71%; Resolution time reduction: 30%; Support cost reduction: 50%; CSAT score: 95% (source-reported, not independently verified).
How is this customer support AI workflow structured?
Multi-channel ticket intake → Shopify store detection → Intent and sentiment analysis → Social media auto-response → Email workflow routing → Automated response and action → Chat deflection and resolution.
This case is one data point. Whether its pattern fits you depends on your volumes, your stack, and your exception load — that comparison is the step no case study can do for you.