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Google Cloud Retail UK

Toolstation

Enterprise Knowledge Search

Toolstation, one of Britain's fastest-growing tool suppliers, needed customers to be able to find products easily on its site. Migrating its search experience to AI Commerce Search on Gemini for customer experience, Toolstation gave customers highly accurate search results, driving significant increases in click-through rate and search-based revenue.

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5.5% increase in overall search-based revenue
And because more customers were finding what they were looking for, we saw overall search-based revenue increase by more than 5%.
And because more customers were finding what they were looking for, we saw overall search-based revenue increase by more than 5%."
Increasing search-based revenue by 5% with accurate search results

Reported impact

Business outcome

Following its success with Agent Search, Toolstation has decided to migrate all its discovery tools, including browse, recommendations and personalization features, to AI Commerce Search. Bringing all its search and discovery tools to Google Cloud will not only help to simplify its tech stack and deliver a better customer experience, it will also increase efficiency at Toolstation.

  • Customer Experience
  • Revenue Growth

Solution

Toolstation, one of Britain's fastest-growing tool suppliers, needed customers to be able to find products easily on its site. Migrating its search experience to AI Commerce Search on Gemini for customer experience, Toolstation gave customers highly accurate search results, driving significant increases in click-through rate and search-based revenue. Having maintained a strong relationship with the Google Cloud team since migrating to the platform in 2020, Toolstation requested a demonstration and was impressed with the ability of AI Commerce Search to return accurate results for terms that its third-party tool struggled with. With a culture of experimentation at the company, Toolstation was keen to move fast, and decided to put the technology to the test. The retailer uploaded its entire product catalog in one night and set about testing Agent Search on Gemini platform on live traffic on the website the very next day.

Solution architecture

2 components · 2 layers
  1. Serving
    • Agent Search on Gemini platform Serves grounded retrieval and search responses inside the end-user workflow.
  2. Orchestration
    • Gemini platform Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.

Evidence from the source

As soon as we moved to Agent Search on Gemini platform, the number of searches with no results fell to 0.1% of searches. Click-through rate was more than 10% higher every day. And because more customers were finding what
Built with Gemini platform machine learning, Toolstation's new search bar automatically recognizes customers' search terms, meaning the Toolstation merchandising team no longer needs to monitor failed searches and manual
Having maintained a strong relationship with the Google Cloud team since migrating to the platform in 2020, Toolstation requested a demonstration and was impressed with the ability of AI Commerce Search to return accurat
Percentage of searches with no results cut from 2% to 0.1% with Agent Search on Gemini platform
Whether it's a roofer in need of sealant, or a DIY enthusiast running low on paint, Toolstation knows that many of its customers don't come to browse. They know exactly what they want, and they have no time to waste. Tha
Transparency Evidence & methodology 80/100 reference value · 77% 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 value80/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth10/25
    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
    4 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
  • 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 specificity8/10
    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

    Toolstation customer story

    First-party
    https://cloud.google.com/customers/toolstation-product-search
    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 Jul 10, 2025 · 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 Oct 9, 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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