Peak season: returns double, the team does not

The question this file answersDo we have to hire a temporary returns team every peak season just to check policy and print labels?

Fits: retailers with 15–30% return rates (retail playbook) whose returns still pass through an agent for eligibility, label and refund; in the composite, a mid-size shop absorbing double the ticket volume in peak.

Not for: shops whose returns already run through a self-service portal that checks policy and issues labels — the queue you would be shortening is already gone.

Typical day

What the desk looks like today

Typical, from the retail and e-commerce playbook — not a client's day. In fashion and general retail, somewhere between one order in seven and one in three comes back. Each return request waits for an agent to open Zendesk or Gorgias, check the policy, issue a label, watch the parcel, wait for inspection, refund, restock and tell the customer. It hurts in peak season, when the queue doubles and half the team is new, and a fraud attempt and a damage dispute queue behind a routine size swap.

What changes

What Monday looks like after

A weekday in late November. The returns agent opens a short list rather than a queue: a customer disputing a coat's condition, a damage photo, an account on its fourth return this month. The routine size swaps and changed minds have their labels, their tracking and — once the warehouse scanned them in — their refunds, without anyone's hand. The people you have are on the calls that need a person; the seasonal-hiring conversation is about the warehouse, not the help desk. The help desk shows how many returns closed without an agent, and why the rest did not. Narvar's 2023 handling-time figure is quoted above; it covers standard returns only.

Typical, not a measured client result. Every figure here comes from the playbook source named below.

~50–65%

less returns handling time — Narvar (2023)

Before: every return waits for an agent to check policy, label and refund. After: Narvar (2023) reports 50–65% less handling time on automated returns — fraud and disputes stay human.

Where this number comes from

Narvar "State of Returns" (2023): automated returns processing reduces handling time by 50–65%; Happy Returns/UPS data: an automated eligibility check removes 30–40% of returns-related support tickets. Industry figures, not our measurement. Playbook range 40–60%.

What we install

What we put in front of the systems you already run

We work through the APIs your shop platform and help desk expose — Shopify, Magento, WooCommerce or BigCommerce; Zendesk, Gorgias, Freshdesk or the one you run. Between the request and the refund:

the request is matched to the order and checked against your written policy — days since delivery, condition declared, final-sale flags
an eligible return gets a label, the customer a status message, the parcel tracking
once your warehouse logs inspection, a standard case is refunded through the platform and stock updated in the OMS — NetSuite, Brightpearl, SAP or yours
anything damaged, disputed or fraud-flagged goes to an agent with order history, photos and the rule tripped.

First scope: your most common return reasons.

What stays human — and what this will not do

Damaged goods. Suspected fraud. Policy exceptions. The refund request that is really a complaint about the product.

What can go wrong — and what we do about it

If the policy lives in the team's heads, the first weeks go on writing it down and arguing edge cases — skip that and the system applies a rule nobody agreed. A refund before the warehouse has scanned the parcel is money gone, so refunds wait on inspection, and a warehouse that logs late slows everything. Fraud rules too loose let serial returners through; too tight, honest customers land in review — you set the threshold. The Narvar 50–65% is handling time on standard returns; damaged, disputed and fraud cases are outside it.

How long it takes, and what we need from you

Audit, about two weeks (€1.5–3K): we read a month of return tickets, count them by reason and outcome, and write your policy down as rules — it often is not. Pilot, 3–5 weeks — medium complexity in the retail playbook (€10–20K): the top return reasons, eligibility and labels first, refunds after inspection, an agent reviews a daily sample. Production: the remaining reasons, then exchanges. From you: help-desk and platform API access, and the person who decides policy edge cases.

The path: free 60-second estimate → free 20-minute review → paid audit of this one process (€1.5–3K, typically two weeks) → pilot with your people in the loop (€10–20K, weeks, not quarters). No transformation programme. Prices are public, on the services page →

This is about you if…

Does every return request wait for an agent to check policy and issue the label?
Do routine size swaps sit in the same queue as damage claims and suspected fraud?
Is your return policy written down as rules — days, condition, exclusions — or decided case by case?

What does this mean in euros?

That depends on your volumes and wage costs — this page will not invent the number. The free 60-second estimate runs that calculation from your answers, with every multiplier sourced.

Get your free savings estimate 60 seconds · no sales call Or write first → Map an ops process like this one — free, 60 seconds →

Not a named Aperanda client. Process file · Ops.

Deep-dive process file. Volumes, weeks and sources come from the industry playbook; nothing here is a named client.

All process files