Use case · Customer service
At forty thousand conversations, it is no longer a question of effort
A retailer runs three brands and 210 stores. Twenty-six advisers handle around 40,000 conversations a month — order tracking, in-store availability, returns, warranty. Each brand has its tone, its promised lead times and its return conditions. Mixing them shows immediately.
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The starting point
The problem is not answering, it is routing
Twenty-six advisers know how to answer. What costs is that a message lands in a shared queue, an adviser for brand A opens a brand C enquiry, knows neither its tone nor its return conditions, and spends six minutes finding out before replying — or handing it to somebody else.
- A shared queue: everyone opens enquiries that are not theirs
- Return conditions differ per brand, learned case by case
- 60% of volume on ten reasons, handled one at a time
- No commitment worth making, for lack of measurement
What gets deployed
Route first, answer second
- 01
Three brands, three workspaces
Each brand has its workspace, tone, reply templates and conditions. An adviser can serve several brands; at any moment they only see the one they are working on.
- 02
Queues distribute by skill
Round robin across advisers trained on that brand and that reason. The message no longer goes into a shared pile: it goes to someone who can answer without looking things up.
- 03
The AI agent takes the sixty per cent
Order tracking, availability, return conditions: backed by that brand's knowledge base and by the customer record, it answers alone. An unhappy customer, a dispute or a warranty claim goes straight to a human.
- 04
Different commitments, measured separately
Two hours on the premium brand, eight elsewhere. A breach is visible before it happens, and analytics read by brand, by channel and by adviser — not as a global average, which means nothing here.
What it moves
What the setup targets
- ≈ 60%
- of volume handled without an adviser
- 3
- brands separated, one team
- 0
- enquiries opened by someone who cannot handle them
- 2 h / 8 h
- distinct commitments, measured separately
Go further
The building blocks used
Tickets & SLAs
Requests tracked, deadlines kept, delays made visible.
DiscoverShared inbox
Every channel in one team inbox, wired to the CRM.
DiscoverAI chatbot
The conversational agent that answers on your site, 24/7.
DiscoverKnowledge base
Your company's knowledge, serving the chatbot and your teams.
DiscoverDynamic segments
Living lists that follow your data in real time.
DiscoverFAQ
Frequently asked questions
How do you guarantee one brand cannot see another's data?
Separation is by workspace, at the data level and not the display level. An adviser cleared on two brands switches between them; they never see them mixed.
Can the AI agent use order data?
Yes, if you give it access: order status, dispatch date, tracking. That is what takes automatic resolution from "mediocre" to "useful" — answering "where is my order" requires knowing.
What happens at the November peak?
The share handled by the AI agent absorbs most of the variation, and queues rebalance themselves. That is the subject of the "seasonal retail" scenario, if you want the detail.
How do 26 advisers hold 40,000 conversations?
Because a large share is handled by chat and the voice agent, from each brand's own knowledge base, and what reaches an adviser is already qualified and attached to the customer. The volume does not fall; what reaches a human does.
Can we track quality by brand and by store?
Yes: first response time, resolution rate without human intervention, SLA compliance and the overdue queue, filterable by brand, store and adviser. The 210 stores compare without mixing.
Another question? Write to us — a human will answer.
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