Peak season without the peak season hiring
This retailer tripled support volume every November and hired seasonal staff who were competent by the time the season ended. We built a support agent that handles the three ticket types making up most of that spike, and it kept working in January.
E-commerce & Retail
- Services
- Customer Support Agents, Marketing Agents
- Duration
- 5 weeks
- Year
- 2026
What was actually wrong.
Three ticket types — order tracking, returns and sizing — made up 78% of volume, and all three were fully answerable from data the company already held.
Seasonal hiring meant four weeks of training for eight weeks of work. Quality dipped exactly when visibility was highest.
The existing chatbot deflected by frustrating people into giving up. Its 'resolution' rate was high and its CSAT was the lowest number in the business.
How we went at it.
Read eighteen months of tickets
Before designing anything, we clustered the historical ticket set to find what people actually asked, in their words. Two of the top ten intents were not in the company's macro list at all.
Grounded every answer in a source
The agent answers from the order record, the product data and the published policy — and cites which one. It has no authority to state a policy that is not written down.
Made escalation a feature, not a failure
When the agent escalates it writes a summary, attaches the order, and states what it already tried. Agents rated handoff quality higher than handoffs from other humans.
Added the marketing agent afterwards
Once support was stable, the same context layer fed a marketing agent writing product descriptions across a 12,000-SKU catalogue that had been half-blank for years.
The build, in five panels.
Scroll the deck, or use the arrows. Everything here is also written out above and below.
What moved, and over what period.
Reported against the metric agreed before the engagement started.
“We had been told for two years that our support volume was a hiring problem. It was a routing problem.”
Built with
Month-end from nine days to two
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