Customer Support Transformation for a National Retailer
Fenwick & Cole, a UK retail chain with 85 stores, was drowning in support tickets. We deployed a cloud AI system that triages, routes, and resolves enquiries — cutting response times from hours to minutes.
Results
Impact delivered
82%
Faster first response
Average first-response time reduced from 4.1 hours to 44 minutes
41%
Tickets auto-resolved
Enquiries resolved without any human agent involvement
+18pts
Customer satisfaction increase
CSAT score improved from 67 to 85 within three months
35%
Agent turnover reduction
Reduced repetitive workload improved agent retention
£320K
Annual cost saving
Reduced need for seasonal temporary support staff
The Challenge
What they faced
Fenwick & Cole receives over 12,000 customer enquiries per week across email, live chat, and social media. Their support team of 45 agents was overwhelmed, with average first-response times exceeding four hours during peak periods. Customers were leaving negative reviews citing slow responses, and agent turnover was climbing due to repetitive workload. The existing ticketing system offered no intelligent routing — enquiries about returns, stock availability, and delivery tracking all landed in the same queue regardless of complexity or urgency.
Our Solution
How we solved it
We built an AI-powered customer support pipeline that sits between the customer and the existing support team. Incoming enquiries are classified by intent, sentiment, and urgency using a fine-tuned model. Simple queries (order tracking, return policies, store hours) are resolved automatically with personalised responses. Complex or sensitive cases are routed to the most appropriate agent with a pre-prepared context summary. The system integrates directly with Fenwick & Cole's Shopify and Zendesk stack, requiring no changes to existing agent workflows.
Approach
Step by step
Enquiry analysis and classification design
Analysed three months of historical tickets (over 150,000) to identify the top 25 intent categories and build a classification taxonomy.
Automated response generation
Developed response templates for the 12 most common query types, powered by GPT-4o with access to real-time order and inventory data via API.
Intelligent routing engine
Built a routing layer that considers intent, sentiment, customer lifetime value, and agent expertise to assign complex tickets optimally.
Integration with existing systems
Connected the AI layer to Shopify (orders, inventory), Zendesk (ticketing), and the company's loyalty platform for personalised context.
Phased rollout with safety nets
Launched with automated responses on low-risk query types only, expanding coverage as confidence thresholds were validated over four weeks.
“Our agents used to dread Monday mornings — the backlog was demoralising. Now the AI handles the routine stuff and our team focuses on the conversations that actually need a human touch. Customer feedback has been overwhelmingly positive.”
James Whitfield
Head of Customer Experience, Fenwick & Cole
Technology
Stack used
FAQ
Frequently asked questions
Yes, for pre-approved query types where accuracy is consistently above 98%. All automated responses are branded and follow the company's tone of voice guidelines. Sensitive topics (complaints, refund disputes) are always routed to human agents.
The initial classification model was trained in under a week using historical ticket data. Fine-tuning for response generation took an additional two weeks, including review cycles with the customer experience team.
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