Case study · E-Commerce · Ticket Routing
From Minutes to Seconds: Ticket Routing in E-Commerce
Ticket Routing was the step everything else waited on. It now runs itself on the same stack — live in 90 minutes, judgement calls still going to a person.
Average Routing Time
A e-commerce customer just opened a ticket — handle it.
Customer
“Hi — I need help with my e-commerce account, it's been a few days with no update. What's going on?”
Agent draft · in your tone
Hi Alex — sorry for the wait! I've pulled up your account, resolved the issue on our side, and you're all set. I've also added a note so this is handled automatically next time.
3 AI agents · 5 tools connected · live in 90 minutes · no code
- Company
- Multi-channel ecommerce retailer
- Team size
- 15-60 employees
- Industry
- E-Commerce
- Time to live
- 90 minutes
- Agents deployed
- 3 AI agents
- Tools connected
- 5 integrations
The context
Why Ticket Routing is hard in E-Commerce.
Nothing about ticket routing is complicated on a single instance. What makes it expensive in e-commerce is volume arriving through orders, returns, carrier events, marketplace messages, and review platforms, against the shipping cutoff and the return window. Miss the window and the cost is not the minutes — it is the reply that arrives after the customer has already opened a chargeback.
Constraints the build had to hold
Order state is the context
No reply is drafted before the order, fulfilment status, and carrier scan are pulled.
Peaks are the real test
Sized for the worst week of the year, because that is the week manual queues never recover from.
Brand voice is fixed
Drafts run against the same tone guide the team writes to, so a reply is not recognisable as automated.
The change
Same job. Two chains.
Every handoff in the left-hand chain is somewhere Ticket Routing used to wait. The right-hand chain has the same steps and none of the waiting.
By hand
- Tickets land unassignedone queue, no order
- Senior agent triagesthe most expensive hour of the day
- Category and priority guessedfrom the subject line
- Assigned, often wrongly
bounces for days
With agents
- Work arrives on any channelpicked up in seconds
- Classification agenthanded straight on
- Priority agenthanded straight on
- Routing agent
logged, and reviewable
When the work can happen
Before and after
What Ticket Routing cost them, and what replaced it.
The challenge
Support at this multi-channel ecommerce retailer was drowning in a problem that had nothing to do with support quality. Their agents were good; the tickets just kept arriving at the wrong ones. Categorisation was manual and subjective, priority was mostly guesswork, and the SLA clock started running long before anyone had read the ticket.
The full background
The e-commerce customer base spanned wildly different contract tiers, and none of that context reached the person picking up the ticket. A key account's outage looked identical in the queue to a routine question from a free trial. With 15-60 employees across the support function, the team was absorbing the cost of that blindness in overtime and in escalations that should never have escalated.
What they built
Rather than replacing their helpdesk, this e-commerce team connected Shopify and the team channel to DeskFerry and let agents own the first ninety seconds of every ticket's life.
How it was wired
The change that mattered most was that context now travels with the ticket. When an agent opens it, the account, the contract tier, the recent history, and the reason for the assigned priority are already attached — so the customer stops repeating themselves and the agent stops hunting through three systems before replying. Re-routes are handled by the same agent that assigned it, using the outcome as a signal, which is what stopped tickets ping-ponging between teams for days. The SLA clock starts on arrival rather than on first read.
The impact
What changed, measured the same way on both sides.
Before and after across the metrics that matter for E-Commerce Ticket Routing.
Average Routing Time
Near-instant
First-Contact Resolution
Significant improvement
Misrouted Tickets
Major reduction
Customer Satisfaction
Notable increase
Support Cost per Ticket
Significant savings
How these were measured
- Baseline
- The "before" column is the team’s own measurement of their manual ticket routing process, taken over the four weeks before anything was connected.
- Comparison
- The "after" column is the same measurement repeated on the same process once the agents were live, so both sides count the same things in the same way.
- Why no percentages
- These are composite scenarios built from patterns across many deployments, not one audited customer’s books. Directional language is the honest way to report that — your own numbers will depend on your volume, your process, and your starting point.
A day, either side
The same day, before and after.
What Ticket Routing actually looked like for this E-Commerce team — the version they described in the first call, and the version they run now.
Before DeskFerry
8:00
Ninety tickets overnight, all sitting in one unassigned queue.
8:45
Triage by hand. Read, categorise, guess at priority, assign.
10:30
Three tickets have bounced between two teams since yesterday.
13:00
A P1 that arrived at 06:12 is only now being looked at.
17:00
First-contact resolution on e-commerce tickets is down again.
After DeskFerry
8:00
The queue is already categorised, prioritised, and assigned.
8:02
The 06:12 P1 was routed and acknowledged within seconds of arriving.
8:45
Agents start on tickets, not on sorting tickets.
13:00
Misroutes are rare enough to be worth investigating individually.
17:00
Every ticket has an owner and the SLA clock was never blind.
The build
The 3 agents that run it.
One job each, with an explicit handoff between them. Splitting Ticket Routing this way is what makes a failure legible — you can see which step it went wrong at instead of debugging one agent that does everything.
- 01
Classification agent
Trigger
A ticket is created in any channel
Reads the whole ticket, categorises it against the e-commerce taxonomy, and detects the language and the real question underneath the subject line.
Agent 1 of 3 in the E-Commerce workflow.
- Passes a classified ticket to prioritisation.
- 02
Priority agent
Trigger
A ticket is classified
Weighs customer tier, contract SLA, sentiment, and blast radius to set a priority that means something, then starts the right clock.
Agent 2 of 3 in the E-Commerce workflow.
- Hands priority and reasoning to routing.
- 03
Routing agent
Trigger
A ticket has a priority
Assigns to the queue or person with the right skill and capacity, attaches account history, and posts urgent items to the team channel.
Agent 3 of 3 in the E-Commerce workflow.
How they did it
From nothing to production in 90 minutes.
No code, no IT ticket, no vendor implementation team. These are the steps in the order this team took them.
Step 01
Mapped the current workflow
Every step of the manual ticket routing process, including exceptions — and which of them a person should keep.
Step 02
Built it in DeskFerry
Shopify and ShipStation as sources, e-commerce decision logic, automated actions and alerts.
Step 03
Ran it in parallel
One week alongside the manual process. Edge cases flagged for review rather than actioned.
The stack
Nothing was replaced. Everything was connected.
The E-Commerce team kept the tools they already ran — DeskFerry sits between them.
Shopify
Order, fulfilment, and customer state behind every reply
Stripe
Billing state — what a customer pays, and whether they still do
ShipStation
Fulfilment and carrier events that drive proactive notifications
Mailchimp
Campaign delivery and the engagement signal that comes back
Google Analytics
Behavioural signal the workflow reacts to
Ticket Routing handled end to end · seconds, every time
What stayed human
The parts they deliberately did not automate.
Automating Ticket Routing end to end was never the goal. Removing the volume so the judgement calls got proper attention was.
The resolution
Routing is automated; solving the ticket is not. The gain is that agents open a ticket that is already categorised, prioritised, and carrying its account history.
Ambiguous tickets
Low-confidence classifications go to a triage queue rather than being assigned confidently to the wrong team, which is the failure mode that costs the most time.
The customer lifetime value question
Asked first by every e-commerce team. Agents run on the access the staff account already had, every action is logged, and any step can be stopped without unwinding what ran.
Takeaways
What transfers to your team.
The parts of this that are not specific to one company's tooling or volume.
- 01
No technical expertise needed — the people who own the ticket routing process built it.
- 02
Capacity scaled without headcount, which changed the unit economics.
- 03
Every decision is logged, so the workflow can be audited rather than trusted.
- 04
Leadership got ticket routing numbers in real time for the first time.
In their words
“What impressed me most was the setup speed. I expected a months-long implementation, but we had AI agents handling our e-commerce ticket routing workflow within a single afternoon. The no-code approach meant our team could configure everything themselves without waiting on IT.”
Composite — written from what teams running this workflow report, not a single named customer.
FAQ
Questions people ask about this build.
Automating Ticket Routing in E-Commerce — what it takes, and where it stops.
How long does it take to set up ticket routing automation for a e-commerce business?
This team was live in 90 minutes. Pre-built e-commerce templates cover the wiring, so most of that time goes on your business rules rather than on connecting things. No code.
How many AI agents does ticket routing automation actually need?
3 here: classification agent, priority agent, routing agent. The split matters more than the count — one job and one handoff each means a failure tells you which step broke. One agent doing everything does not.
What results can a e-commerce business expect?
The figures here are directional, not audited — composite scenarios, not one customer's books. What transfers is the shape: routine volume stops needing a person, exceptions surface instead of sinking, and nothing waits for office hours. Your numbers depend on your volume and starting point.
How does DeskFerry handle e-commerce data and access?
Agents run on the same access the staff account already had — throughput widens, permissions do not. Every action is logged with what it read and changed, and any step can be stopped without unwinding what ran. DeskFerry holds no formal e-commerce certification, so scope it as you would any other system in your control environment.
What still needs a person?
More than most automation pages admit. Anything outside the rules stops and goes to a named owner with context attached, rather than being guessed at. The rules themselves are changed by people — agents never widen their own tolerances. The carve-outs this team kept are named above.
What tools does this connect to?
1,500+ integrations. This build used Shopify, Stripe, ShipStation, Mailchimp and Google Analytics; most e-commerce stacks are a variation on that. CRM, email, chat, databases, and industry-specific software all connect without code.
Run this in your own stack.
Describe how Ticket Routing should work at your E-Commerce business, in a sentence. DeskFerry builds the agents, wires your tools, and takes the routine volume from there. Start free — no credit card.
Or start from a template — Ticket Routing agent for E-Commerce.
Keep exploring
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Ticket Routing in other industries
Composite scenario — built from patterns across many E-Commerce Ticket Routing deployments rather than one customer's books. Figures are directional; your own depend on your volume, process, and starting point.