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Google Cloud Retail Finland

Verkkokauppa.com

Marketing Personalization · Real-time Analytics · Sales Automation

We've been a long-time user of Google Cloud, so it made sense to build on our existing data foundation in BigQuery. Google Cloud is a leader in AI innovation, and if anyone understands search, it's Google, so we were confident it would help us deliver the intelligent search we needed. When it comes to online shopping, speed and convenience are at the heart of a positive customer experience.

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4% increase in products added to cart
8% improvement in conversion per visitor

Reported impact

Business outcome

8% improvement in conversion per visitor; 4% increase in products added to cart; Significant increase in search selection rate; Finland's top online retailer uses Vertex AI Search for commerce to personalize results, boost conversions, and drive sales..

  • Customer Experience
  • Revenue Growth
  • Risk Reduction
  • Scalability

Solution

To sustain its leadership in a competitive market, Verkkokauppa.com needed to transform its website from a simple catalog into an intuitive, intelligent shopping assistant. To do so, it searched for an AI technology provider to replace its basic search function with an AI discovery engine. With all its product data already housed in BigQuery , and a long history of using Google Cloud for compute, Verkkokauppa.com chose Vertex AI Search for commerce The solution also makes sophisticated use of data-driven personalization. By ingesting and analyzing real-time user events — such as clicks, views, and products added to cart — alongside its product catalog data in BigQuery, the data platform creates a single view of the customer.

Data flow

stored or unified in BigQuery, processed with Vertex AI, and surfaced through Vertex AI Search

Solution architecture

3 components · 3 layers
  1. Compute
    • Vertex AI Hosts and orchestrates machine learning and generative AI capabilities inside the solution.
  2. Storage
    • BigQuery Stores and queries the analytics and AI-ready datasets that power the workload.
  3. Serving
    • Vertex AI Search Provides search and retrieval over enterprise content for user-facing AI experiences.

Architecture clues

  • "Vertex AI Search for commerce delivers highly relevant results without us having to spend significant time on manual optimizations," says Paasi. "We can trust it to work effectively right out of the box."
  • # Verkkokauppa.com boosts conversions by 8% with Vertex AI Search for commerce
  • Finland's top online retailer uses Vertex AI Search for commerce to personalize results, boost conversions, and drive sales.

Evidence from the source

4% increase in products added to cart
8% improvement in conversion per visitor
Finland's top online retailer uses Vertex AI Search for commerce to personalize results, boost conversions, and drive sales.
Significant increase in search selection rate
The search for smarter discovery capabilities
Transparency Evidence & methodology 90/100 reference value · 79% 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 value90/100
  • 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 specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • 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
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • 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. Google Cloud

    Verkkokauppa.com customer story

    First-party
    https://cloud.google.com/customers/verkkokauppa
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Feb 7, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Jan 15, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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