Case study

How Konesso turns 1 in 5 chats
into an order

Konesso is a Polish specialty coffee retailer — beans and ground coffee, including house blends carrying its own name, sold alongside espresso machines, grinders, moka pots, water filters, descalers and the spare parts that keep all of it running.

4 min read

Konesso Assistant
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Scale

104,000 products

Years on market

18

Integrations

IdoSell

Results

Key results

Measured in AskSpot over a 30-day window. A typical chat-driven order runs to 2.3 items — a kilo of beans and the filter or the descaler that goes with it. More than one chat in five ends in a purchase, the highest rate we have measured on any merchant.

€47,600

in chat-driven orders a month

29.1%

of chats fill a basket

21.6%

of chats end in a purchase

68%

of chats resolved

“AskSpot doesn’t just answer questions — it sells. The numbers we’d hoped for from a chatbot, we got from AskSpot in the first month.”

Paweł Bieńko

★★★★★

Paweł Bieńko

Chief Operating Officer, Konesso.pl

Before AskSpot

Two shops sharing one basket

Konesso sells coffee, and coffee is chosen by taste: sweet, no bitterness, no acidity, and it still has to hold its own under milk. Nothing on a product page answers that. It is the question you ask a person behind a counter, and answering it well takes someone who has actually drunk the thing — which meant an adviser, one shopper at a time.

The other half of the catalogue does not work like that at all. Machines, grinders, moka pots, and behind them a long tail of parts that either fit or do not: a water filter for a Jura E8 in the EC revision, a 63 mm gasket for a four-cup Bialetti Venus, a milk tube set for a C8. That is a fitment problem, closer to car parts than to coffee, and getting it wrong means a return. Both kinds of question arrived on the same widget — and most of them after the shop had closed.

What decides a coffee sale

taste

What decides a parts sale

the model number

Where both answers lived

a trained adviser

Help after closing

none

Rollout

How they rolled it out

One module, three jobs, in the order a coffee purchase actually happens: pick the beans, pick the machine, then keep the machine alive.

AI Chat Agent

Step 01

Taste-Profile Grounding

Choosing coffee the way a barista would

The 103,730-product IdoSell catalogue was indexed on the things that actually decide a bag of coffee — roast level, acidity, body, tasting notes, the brewing method it was blended for, and the roast date. The agent also carries one service fact it can act on: Konesso grinds to order, so it asks how you brew before it recommends.

What shoppers ask

  • A sweet coffee, no bitterness, no acidity, for an automatic machine
  • Can you grind it for a V60 before you ship it?
  • Decaf beans for a dripper

AI Chat Agent

Step 02

Machine Advisory

The four-figure decision

Espresso machines are where the money is and where the questions get hardest — Jura against Melitta, a milk jug against a steam wand, one revision of a model against the next. The agent was given the comparison axes that genuinely separate them, plus the two facts that close the sale: warranty length, and where the nearest authorised service point is.

What shoppers ask

  • Nivona 820 or Melitta Barista SE F83 — which is better?
  • A machine for an office of 20, up to €1,150
  • What is the difference between the 820 and the 821?

AI Chat Agent

Step 03

Parts & Fitment Matching

The part that has to fit

Filters, gaskets, milk tubes, descalers and cleaning tablets were mapped to the machines they fit, down to the revision — a Jura E8 in the EC revision takes a chipped CLARIS Smart PLUS and will not accept the older cartridge. It is the same job a car-parts catalogue does, and it is why 57% of the chats where the agent recommends something end in a click on one of its picks.

What shoppers ask

  • Which water filter fits a Jura E8 EC?
  • Do you have gaskets for a 4-cup Bialetti Venus?
  • Do Kalita Wave 155 filters fit a Timemore Crystal Eye B75?

Measured in AskSpot

What the agent does in a month

57%

product recommendation click-through

2,102

conversations handled a month

54%

of chats come in after hours

6 s

average first response

From real conversations

What Konesso shoppers actually ask

Half of everything asked is one shopper choosing one thing — finding it, checking the spec, comparing two models, confirming a part will fit. Order status is 3.9%. This is a selling desk, not a support desk. The surprise is third place: discount codes and loyalty points are 15% of all conversations, and most of those are not "give me a code" but "why isn't my code coming off the total?" — a checkout problem the agent can actually solve.

Finding the right product26.7%
Specs & features18.3%
Discounts & loyalty points15.1%
Delivery & buying terms11.5%
Accessories & spare parts4.8%
Warranty & complaints4.3%
Order status & changes3.9%
Fit & compatibility3.6%
Comparing two products3.5%
Availability & restock3.0%

About 8 messages per conversation · 54% arriving outside business hours.

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Layer 01

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