spotIndustry
Scale
over 4,500 products
Modules
On the market
since 1992
Integrations
IdoSell
Results
Key results
Measured in AskSpot over a 30-day window. Casu sells footwear people can’t try on, so the whole sale hinges on fit — and the chat turns fit questions into carts and chat-driven orders, clearing seven in ten conversations with no human.
€6,900
in chat-driven orders a month
18.8%
of chats add an item to the cart
12.8%
of chats end in a purchase
70%
of chats resolved
Before AskSpot
Shoes are bought by fit, and mostly in the evening
Nobody buys shoes online the way they buy a T-shirt. The customer is picturing one foot — 25 centimetres long and a little wide, a bunion that a stiff court shoe will press on, a pistachio to match a dress already hanging in the wardrobe. The real questions are whether this exact pair fits that foot and whether the colour is right in the flesh. A product page prints a size chart; it can’t tell a shopper whether a 39 in this model will actually be comfortable.
Casu makes much of what it sells, so fit is specific to each last — a wider strap here, a softer leather there, a size that runs true in one model and snug in the next. Those are judgement calls, not clicks, and every one of them used to end at a product page that couldn’t make them, or in a message sent long after the shop had closed — which is when most of them arrive.
What decides the sale
fit & size
What shoppers send
foot length & width
When shoppers ask
63% after hours
Help after closing
none
Rollout
How Casu rolled out AI chat
One module, set up in the order a shoe purchase actually happens: get the size right first, then the fit and the look, then keep the sale when the size a shopper wants is gone.
Step 01
Catalogue & Size Grounding
Answering with the tape measure out
The IdoSell catalogue was indexed on the numbers that decide a shoe purchase — insole length in centimetres against each EU size, the width of the last, heel height, upper material — so the agent turns “my foot is 25 cm” into the right size in that exact model, not a generic size chart.
What shoppers ask
- My foot is 25 cm and a bit wide — what sandal size should I take?
- Trekking sandals in a 42, insole length 27 cm — do these fit?
- Is the width given for the heel on these block-heel sandals?
Step 02
Fit, Materials & Style Advice
Wide feet, bunions and the right shade
The materials and fit notes were given their own rules — which lasts run wide, which leathers ease over a bunion, what a colour looks like next to an outfit — so the agent recommends a pair that will actually be comfortable, matches a shade to a dress, and adds the right size to the cart.
What shoppers ask
- Are these court shoes OK for a wider foot? I have bunions.
- I’m after shoes in pistachio, low heel, to go with a dress.
- Will these boots be too narrow on a wide calf?
Step 03
Stock, Delivery & the Honest Alternative
Keeping the sale when a size is gone
Live stock and delivery were wired into IdoSell, so “when does it arrive?” returns a real dispatch window, InPost-locker and courier options, and cash on delivery. When a size is out with no confirmed restock date, the agent says so plainly and offers an in-stock pair in the same style — rather than promising a date it can’t keep.
What shoppers ask
- Will size 39 of these black boots be back in stock?
- Do you ship to InPost lockers? Can I pay cash on delivery?
- Can I swap for another size instead of returning?
Around the clock
Your busiest hours aren't nine to five
Each bar is the share of chats by hour of day, in local time. The shaded window is when a typical team is at their desks. Everything outside it is the agent working a shift no one else is on.
From real conversations
What Casu shoppers actually ask
Almost every question is one shopper solving the thing a website can't: will this shoe fit, and will it look right. Product advice and specs lead — and read closely, both are really about fit: foot length in centimetres, a wider or narrower last, room for a bunion. Delivery terms, returns and stock follow. It's a fitting room, staffed by chat.
Based on tagged conversations over a 30-day window · about 9 messages each · 63% arriving after hours.
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