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Case Studies/Retail

How an E-Commerce Retailer Automated 87% of Support Tickets and Recovered NPS

E-commerce retailer automates 87% of support tickets with AI. Response time drops from 14 hours to 3 minutes. NPS recovers 14 points. Real case study.

Client:UK e-commerce retailer60-day measurement window2026
How an E-Commerce Retailer Automated 87% of Support Tickets and Recovered NPS01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?: 01 10 </> ai() {x} == fn 0x1F :: [] => λ #! void 101 110 ++ -- && || 1010 ai </> 00 11 0b1 ?:
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Results at a glance

87%

Tickets auto-resolved

tier-1 categories

3 min

Auto response time

down from 14 hours

138x

Year-one ROI

EUR 1K invested, EUR 137.5K avoided

Sector context

Bottom line

A fast-growing UK e-commerce brand receiving 850+ support tickets per week during peak season automated 87% of tier-1 tickets using a single AIQorex Customer Support Agent. Average response time dropped from 14 hours to 3 minutes. NPS recovered 14 points within 45 days. The investment of EUR 1,000 returned an estimated EUR 137,500 in annual avoided FTE cost.

DetailValue
CompanyD2C E-Commerce brand (20–80 employees)
LocationUnited Kingdom
Peak season ticket volume850+ tickets/week
Support team3 full-time agents
Pre-deployment response time14 hours (peak)
Pre-deployment CSAT72%
Pre-deployment NPS trendDropping (-18 points over 6 months)

The challenge

Peak season was breaking the support team. With 3 agents handling 850+ tickets per week, each agent was managing 280+ tickets - nearly 40 per day. Response time stretched to 14 hours. Customers who waited that long were already frustrated. NPS dropped 18 points in 6 months. Refund and returns requests were the worst category.

The hidden cost: The support team had no capacity for proactive work - no FAQ improvements, no self-service expansion, no process documentation. They were trapped in reactive mode, and NPS was the evidence.

Constraints

  • 14-hour average response time during peak season
  • 850+ tickets/week against a team of 3
  • NPS dropped 18 points over the prior 6 months
  • Zero capacity for proactive FAQ or self-service work
“NPS improved most sharply when AI handled tier-1 quickly AND humans had more time for tier-2 cases. Speed + depth, not speed-vs-depth.”
Head of Customer Experience
UK e-commerce retailer

Our solution

Agent deployed: AIQorex Customer Support AI Agent (n8n workflow + GPT routing + Shopify API). Connected to Gorgias, Shopify, Gmail, and the internal FAQ knowledge base.

ProcessBeforeAfter
Ticket categorisationManual: agent reads and classifiesAutomatic: AI classifies into 13 categories
Order status queriesManual: agent checks Shopify and repliesAutomatic: AI pulls order data + sends tracking
Returns processingManual: 5-step workflow (email → check policy → approve → label → refund)Automatic: eligibility check → label generation → refund trigger
Refund statusManual: agent checks finance systemAutomatic: real-time status via API
Common complaint responsesManual: agent types each responseAutomatic: AI drafts response, agent approves
Sentiment monitoringNoneAutomatic: flags negative sentiment for priority human handling
Escalation routingManual: whoever is free picks it upAutomatic: routes by category → correct specialist

Auto-resolved (8 categories)

Order status, tracking, delivery timeframe, size guide, return eligibility, refund status, product availability, password reset.

Human-required (5 categories)

Damaged item (inspection), fraudulent transaction (security), complex complaint (judgement), VIP customer query (relationship), refund escalation (manager approval).

Implementation timeline

WeekActivityOwner
Day 1–2Ticket taxonomy mapping · 13-category classifier configuredAIQorex + Support lead
Day 3Gorgias + Shopify + Gmail integration · sandbox routing testsAIQorex
Day 4Live deployment with shadow review on every auto-resolved ticketSupport team
Week 2Sentiment thresholds tuned based on first 100 escalationsSupport lead
Week 6Peak season stress test - 1,100 tickets/wk maintained at 85% auto-resolveSupport lead

Results delivered

MetricBaseline90-day12-monthMethod
Tier-1 tickets auto-resolved0%87%-+87 pp (Gorgias auto-flag)
Auto response time14 hours3 minutes--99.6% (timestamp diff)
Human response time14 hours45 minutes--95% (Gorgias SLA report)
Tickets reaching human agents850/week110/week--87% (Gorgias volume)
NPSDropping (-18 pts/6mo)+14 points recovered-+14 pts in 45 days (post-purchase survey)
CSAT (auto-handled)N/A92% positive-New baseline (Gorgias CSAT)
CSAT (human-handled complex)72%88%-+16 pts (Gorgias CSAT)

ROI calculation

Year-1 savings

EUR 137,500

Payback

~3 days

Multiple of return

~138x

TCO breakdown

  • AIQorex Customer Support AgentEUR 500 (one-time)
  • Installation add-onEUR 500 (one-time)
  • Total investmentEUR 1,000
  • Avoided FTE requirement2.5 full-time agents
  • Annual cost per UK support agent (loaded)EUR 55,000
  • Total avoided costEUR 137,500/year

Methodology: UK support agent cost includes salary, employer NI, pension, and overhead. Based on London / Southeast UK benchmarks. Does not include the value of NPS improvement, reduced churn, or freed management capacity.[^4]

This fits you if…

  • Categorise your tickets by complexity before deploying - you need to know which types AI can handle and which need humans
  • Set up sentiment monitoring from day one - it tells you when the AI is underperforming
  • Review auto-resolution accuracy weekly for the first 30 days
  • Do not remove the human approval step for refunds or complaints - AI drafts, the human decides

References & Sources

  1. 1AIQorex composite analysis - Support agent deployment. Internal (2026). https://aiqorex.com/research
  2. 2Gorgias + Shopify API integration documentation. gorgias.com, shopify.dev (2026). https://shopify.dev/
  3. 3UK customer support salary benchmarks for e-commerce. industry averages (2026). https://aiqorex.com/research
  4. 4AIQorex ROI methodology - UK support EUR 55K loaded, 60-day window. Internal (2026). https://aiqorex.com/research

AIQorex deployed

  • Customer Support AI AgentEUR 700 + installation
  • Maintenance SaaS - Professionalfrom EUR 200/mo

Technology stack

n8nGorgiasShopifyGPT routingGmail
GDPR · NDA & DPA

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