spotIndustry
Scale
3 storefronts on 3 platforms
Modules
Integrations
Shopify · Custom integration
Results
Key results across three stores
Measured in AskSpot over a 30-day window, pooled across all three storefronts. Footwear sells on advice and fit, so the agent’s job is to get the right shoe into the basket – and roughly one chat in nine goes on to check out.
16.5%
of chats add an item to the cart
58%
product recommendation click-through
11.7%
of chats end in a purchase
67%
of chats resolved
“Easy to use, with an intuitive panel – it takes the manual work off our team and delivers good results.”
Before AskSpot
Three brands, three platforms, one support problem
Running gear and sneakers don’t sell themselves off a spec sheet. A runner wants to know which shoe suits their distance, their terrain and their foot; a sneaker buyer wants to know whether a size is coming back in stock and which store has it today. Those are expert questions, and they arrive at all hours – around half of them after the shop has closed.
Szopex sells across three separate storefronts on three different platforms – sklepbiegacza.pl on its own custom Szopex build, warsawsneakerstore.com on Shopify, and skstore.eu – each with its own catalogue, its own orders and, until now, its own support surface. And footwear is returned as often as it is advised on, so the same desk that recommends a shoe has to handle the exchange when the size turns out wrong.
Storefronts
three brands
Platforms
three, all different
What decides the sale
the right shoe, the right size
Help after closing
limited
Rollout
How Szopex rolled out AI chat
One agent, deployed across all three stores in the order a footwear purchase actually happens: match the shopper to the right shoe, get the size and the stock right, then carry the order and the return.
Step 01
One Agent Across Three Storefronts
Same agent on Szopex, Shopify and skstore.eu
The same AI Chat Agent was connected to all three stores – sklepbiegacza.pl on its custom Szopex platform, warsawsneakerstore.com on Shopify, and skstore.eu – each reading its own live catalogue and orders, so one configuration serves three brands rather than three separate build-outs.
What shoppers ask
- I’m looking for narrow women’s running shoes
- Looking for New Balance 993 / 1500 / 574 in size 45
- Are the Nike Ja 3 coming back in stock?
Step 02
The Running-Shoe & Sneaker Advisor
Recommending by distance, terrain and fit
The catalogue was grounded on the things that actually pick a shoe – cushioning, stability, drop, width and use-case – so the agent turns “a wide women’s shoe with lots of cushioning for trail” into specific models, and can compare two of them side by side, instead of returning a category page.
What shoppers ask
- Which wide running shoes would you recommend?
- A women’s trail shoe with lots of cushioning
- Would these be good for a half marathon?
Step 03
Size, Stock & the Post-Sale Queue
Fit, in-store pickup, orders and returns
EU and US sizing including half sizes, live stock with in-store pickup by city, and order status, cancellations and returns were wired in across all three stores – so “EU 44 2/3 in Kraków?”, “where’s my order?” and “when do I get my refund?” are each answered end to end, not handed off.
What shoppers ask
- Do you have EU 44 2/3 in the Kraków store?
- What’s happening with my order?
- When will I get my refund?
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 shoppers across the three stores ask
Half the desk is a running-shoe expert and half is the post-sale queue. Choosing the shoe leads – which model for this distance, this terrain, this foot – with order status and returns close behind, because footwear is bought and sent back on fit. Sizing, price and stock fill the rest. One agent handles all of it, in the shopper's own words, across all three stores.
Derived from conversation content across the three storefronts over a 30-day window – tagging is off, so the mix is inferred from the opening message. About 8 messages each · around half arriving after hours.
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