Reducing customer support pressure with AI-assisted workflows
A UK e-commerce retailer with a growing order volume needed faster customer responses without hiring a larger support team. We mapped repeat enquiries, designed guard-railed AI assistance and integrated it with their existing shop and CRM — cutting manual ticket handling by 40%.
Project Overview
Industry
E-commerce
Duration
10 weeks
Team Size
3 consultants
Client context
The client is a direct-to-consumer retailer selling through Shopify with a small in-house support team of four people. Order volume had roughly doubled over eighteen months, but headcount had not kept pace. Peak periods — Black Friday, January returns and new product launches — created backlogs that pushed first-response times beyond 24 hours and increased complaint rates on review platforms.
The Challenge
Support agents were spending most of their day answering the same questions: order status, delivery delays, return eligibility and product sizing. Tickets were handled manually across email and a shared inbox, with no structured knowledge base. Leadership wanted to improve response times and consistency without compromising brand tone or handing complex complaints to automation. They also needed clear escalation rules so sensitive cases always reached a human.
Our Solution
We designed an AI-assisted triage and response workflow connected to Shopify order data and their CRM. Common enquiries could be drafted automatically for agent review or sent directly when confidence thresholds were met. A structured FAQ and returns policy knowledge base grounded the assistant, and we defined explicit hand-off rules for billing disputes, damaged goods and VIP customers.
Our approach
- 1
Interviewed support staff and reviewed 500 anonymised tickets to categorise enquiry types, peak patterns and escalation triggers.
- 2
Prioritised five high-volume, low-risk workflows: order tracking, delivery ETA, return initiation, sizing guidance and subscription changes.
- 3
Built a retrieval-backed knowledge layer from existing policy documents, macros and product pages so answers stayed accurate and on-brand.
- 4
Integrated with Shopify and the CRM so the assistant could look up live order status rather than rely on static templates.
- 5
Ran a four-week pilot with human-in-the-loop approval before enabling selective auto-send for the highest-confidence categories.
- 6
Delivered playbook documentation and trained the team on monitoring, prompt refinement and when to override the system.
Manual support tickets
First response time
Customer satisfaction
Results achieved
- Reduced manual support ticket handling by 40% within the first quarter after launch
- Cut median first-response time from 24 hours to under 10 minutes for triaged enquiries
- Raised customer satisfaction scores from 72% to 92% on post-resolution surveys
- Created a maintained knowledge base that agents now use even outside automated flows
Technologies Used
Project Timeline
Discovery & Analysis
Shadowed the support team, mapped ticket categories and measured response times across peak and off-peak weeks.
Design & Development
Built the knowledge base, configured CRM and Shopify integrations, and tested draft responses against real historical tickets.
Deployment & Training
Ran phased rollout with daily monitoring, refined escalation rules and trained agents on review and override procedures.
Key takeaways for SMEs
- Start with enquiry analysis — automation ROI is highest where volume is predictable and policy is clear.
- Keep humans in the loop until accuracy is proven; auto-send only for well-bounded workflows.
- Ground AI assistants in live system data (orders, CRM records) rather than static copy alone.
Related service
This case study reflects the kind of outcomes we pursue through our ai & automation work for UK SMEs.
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