Case study · Consumer e-commerce
E-commerce Brand Survives Peak Season Support: Case Study
Peak season handled without a backlog
An ecommerce customer service outsourcing case study from AssistBPO: peak season handled without a backlog for a consumer e-commerce client, anonymized at their request. The desk was staffed by a named assistant with a trained backup and a team lead, working the client's own hours inside the client's own systems. What follows is the operational account: what the desk looked like before, what the assistant took over, and what the client sees every day now.
By Nimra Khalid · · Reviewed by Issabela Masters
What was happening before?
A direct-to-consumer brand selling on Shopify did most of its year in six weeks. The rest of the year two people cleared the queue comfortably. In peak season the same two people were answering chat, email, Instagram messages and returns at once, first response times stretched into days, and shipping-delay questions arrived faster than anyone could answer them. Temporary hires had never worked out, because training finished about the time the season did. AssistBPO built a peak-season pod on the support desk: named agents with a team lead, trained in October, live from the start of November and stood down in January. The pod worked inside the brand's own Gorgias help desk using macros written with the in-house team. Tickets were tagged by reason so the operations team could see which problems were worth fixing at the source.
What did we do?
In the order it happened, on the client's own systems.
Trained the pod before the season, not during it
Onboarding ran in October: product range, sizing, shipping carriers and cut-off dates, the returns policy in plain words and the brand's tone in writing. Agents worked live tickets under review while volume was still normal, so the pod arrived in November already knowing the catalog rather than learning on a queue.
Rewrote the macros with the in-house team
The top twenty ticket reasons got a Gorgias macro each, written with the two permanent agents so the voice matched what customers already knew. Order status, shipping delay, address change, size exchange, damaged on arrival and discount code failure covered most of the queue and cut the typing per ticket sharply.
Split the queue by channel and difficulty
Chat and social messages went to the fastest responders, email to agents working in batches, and anything involving a refund over the agreed threshold or a carrier claim went to the in-house pair. The team lead watched the backlog through the day and moved people between channels as the queue shape changed.
Put a tag on every ticket reason
Every ticket was tagged with its cause, and the daily report showed the top reasons by volume. A confusing size chart and a carrier with a bad week produced more mail than any promotion did, which turned a support metric into two fixes the operations team could actually make.
Planned the stand-down as carefully as the ramp
January volume was projected week by week and the pod was reduced on a schedule, with the last agents kept for the returns wave that follows the season. Macros, tagging and the reporting rhythm stayed with the in-house team, so the improvements did not leave when the pod did.
The desk
What changed on the desk?
Three moments on one desk. Every line here is something the client can check against their own systems.
- Before
- For ten months of the year two people cleared the queue comfortably and the brand had no support problem to solve. For six weeks it had a different company. The same two people were answering chat, email, Instagram messages and returns at once, shipping-delay questions arrived faster than anyone could type, and a queue that was not cleared on Monday was still there on Friday with Monday's customers now angry. Temporary hires had been tried twice and had not worked, because training finished at about the point the season did.
- What the assistant took over
- A named pod on the support desk took chat, email and social messages, order status, shipping delays, address changes, size exchanges and the first pass on returns, all inside the brand's own Gorgias help desk. The pod was recruited and trained in October, worked live tickets under review while volume was still normal, went full time from the start of November and was stood down on a planned schedule in January. Refunds over the agreed threshold and carrier claims stayed with the two in-house agents, and a team lead watched the backlog through the day and moved people between channels as the queue changed shape.
- What the client sees now
- Peak weeks start with the pod already knowing the catalog, the carriers and the shipping cut-offs, so nobody is learning the returns policy on a live chat in front of a customer. The top twenty ticket reasons have a macro each, written with the in-house pair so the voice is the one customers already know. Every ticket carries a tag for its cause and the daily report ranks those causes, which is how a confusing size chart and one bad carrier week turned into two fixes the operations team could make at the source rather than answer forever. The macros, the tagging and the reporting rhythm stayed with the in-house team when the pod stood down.
What does the weekly review cover?
The same measures every week, read off the client's own systems with the team lead in the room. They are what this desk is answerable for, which is why they are named here.
- First response time in peak week, read per channel rather than as one average
- Tickets carried into the next day, and how old the oldest one is
- Tickets resolved on the first reply rather than after a back and forth
- The ranked list of ticket reasons, and which of them belong to operations rather than support
Measures, not claims. AssistBPO publishes a before and after value only once the owner has checked it against the client's own export, and this page shows the work instead.
How is this study verified?
The ramp, the macros and the stand-down above are the plan the brand signed off and worked to, not a write-up assembled afterwards. No figure appears on this page, because AssistBPO publishes a number only once the owner has checked it against the brand's own help desk reports. Real client, anonymized at their request. Never marked up as a review or rating. See how we count.
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