Microsoft Retail Poland

Żabka Group

Real-time Analytics · Supply Chain Optimization

As the largest modern convenience store chain in Poland, Żabka Group has made it its mission to understand and anticipate the needs of its customers—providing products and services that make their lives easier. Now, the company is launching Nano stores offering autonomous, frictionless, unmanned retail experiences designed to deliver the ultimate in convenience to customers.

Now, an estimated 15.5 million consumers live within 500 meters of the nearest Żabka store and, recently, the chain opened its 9,000th store. Żabka was also early to release a grocery shopping app which helped establish its online food shopping and quick commerce delivery service—something that proved critical for Żabka customers during the COVID-19 pandemic.
Offering an autonomous retail experience, Nano stores enable a seamless walk in, walk out approach to food shopping—no checkout lines, clerks, or cash—24 hours a day, 7 days a week.
“We've sold close to 2 million products using autonomous technology over the last year.

Reported impact

Business outcome

Blicharski agrees, adding, “We feel the Żabka, Microsoft, and AiFi partnership will help bolster Żabka expansion as we continue to scale our solution to more new store formats, venues, and locations. We are excited to continue to innovate in the retail space and, even more so, find more and new ways to make our customers’ daily live easier in these changing times.”

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

As the largest modern convenience store chain in Poland, Żabka Group has made it its mission to understand and anticipate the needs of its customers—providing products and services that make their lives easier. Now, the company is launching Nano stores offering autonomous, frictionless, unmanned retail experiences designed to deliver the ultimate in convenience to customers. Using AI-powered real-time computer vision technology from AiFi to track customer behavior and product selection, and Microsoft Cloud for Retail to support data centralization, analysis, and solution scalability, Żabka is creating personalized customer journeys that are receiving high customer satisfaction ratings. In addition, Żabka is increasing operational efficiencies, gaining more foot traffic, and passing on cost savings to its customers.

Data flow

The data collected can activate signals in the store to further enhance the customer experience with suggestions or exclusive offers.

Solution architecture

2 components · 2 layers
  1. Compute
    • Microsoft Azure Provides the cloud infrastructure, security, and scaling foundation for the solution.
  2. Serving
    • Microsoft Cloud for Retail Brings customer, inventory, and operations data together for retail analytics and decision-making.

Architecture clues

  • Based on Microsoft Azure , the AiFi deep learning solution tracks the entire customer shopping funnel in real-time—how customers interact with the products on the shelves, how they react to promotions and communications, and what they choose to take off the shelf and purchase.
  • In addition, the technology automatically informs inventory tracking and eliminates the checkout process.
  • Just as e-commerce has advanced the way retailers collect information and optimize the shopping experience for customers, physical Żabka Nano stores offer something similar, with AI-powered computer vision technology collecting and processing data as customers shop.
  • The data collected can activate signals in the store to further enhance the customer experience with suggestions or exclusive offers.
  • Using AI-powered real-time computer vision technology from AiFi to track customer behavior and product selection, and Microsoft Cloud for Retail to support data centralization, analysis, and solution scalability, Żabka is creating personalized customer journeys that are receiving high customer satisfaction ratings.
  • “Data collected by AiFi gives Żabka a unique deep dive into customer behavior in this new context of autonomous retail–we are gaining completely new understandings and customer insights,” Powierża explains.

Evidence from the source

As the largest modern convenience store chain in Poland, Żabka Group has made it its mission to understand and anticipate the needs of its customers—providing products and services that make their lives easier.
Based on Microsoft Azure , the AiFi deep learning solution tracks the entire customer shopping funnel in real-time—how customers interact with the products on the shelves, how they react to promotions and communications, and what they choose to take off the shelf and purchase.
Now, an estimated 15.5 million consumers live within 500 meters of the nearest Żabka store and, recently, the chain opened its 9,000th store. Żabka was also early to release a grocery shopping app which helped establish its online food shopping and quick commerce delivery service—something that proved critical for Żabka customers during the COVID-19 pandemic.
Now, the company is launching Nano stores offering autonomous, frictionless, unmanned retail experiences designed to deliver the ultimate in convenience to customers.
Offering an autonomous retail experience, Nano stores enable a seamless walk in, walk out approach to food shopping—no checkout lines, clerks, or cash—24 hours a day, 7 days a week.
“With Microsoft Cloud for Retail Smart Store Analytics we can pull the different kinds of data together, analyze it, and use it to create new ways to elevate the shopping experience for customers.”
Transparency Evidence & methodology 76/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value76/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Microsoft

    Żabka Group customer story

    First-party
    https://www.microsoft.com/en/customers/story/1589693036635058141-zabka-retail-microsoft-cloud-for-retail
    Source type
    Vendor-published customer story
    Published
    Jan 9, 2023
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jan 9, 2023 · Microsoft · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.