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Google Cloud Retail Richfield, Minnesota

Best Buy

Customer Service Automation

Best Buy is transforming its retail experience. The company aims to bring the quality personal service of its physical stores to digital and voice channels, so customers can purchase with confidence and quickly get help if they need it.

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CX Agent Studio makes it faster and easier to deploy the AI agents and cuts traditional 18-month software development timelines to 3-6 weeks.
Generative Knowledge Assist listens to conversations and presents troubleshooting documents on agents' screens in real time, so they can respond immediately.
The platform allows internal Best Buy teams to build conversational voice and chat agents that bypass legacy text-to-speech processing layers, making conversations feel more natural.
The result is an improved quality review structure, where managers provide personalized training to human agents.

Reported impact

Business outcome

Because all interaction data is stored in BigQuery, Best Buy can analyze the data to determine why customers call. Teams share insights across the business to fix product issues.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

Best Buy deployed Gemini Enterprise for Customer Experience to use AI to enable seamless customer journeys from purchase through to support. The platform allows internal Best Buy teams to build conversational voice and chat agents that bypass legacy text-to-speech processing layers, making conversations feel more natural. CX Agent Studio makes it faster and easier to deploy the AI agents and cuts traditional 18-month software development timelines to 3-6 weeks. Underpinning the environment is Gemini Flash, a voice-native audio model that drives Best Buy's automated computer troubleshooting experience. Gemini Enterprise for Customer Experience enables Best Buy to achieve two critical goals: deliver self-service to callers and support customers who need to speak to a person by ensuring they have the necessary background to quickly assist them.

Solution architecture

6 components · 4 layers
  1. Compute
    • Gemini Flash Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
    • Google Cloud Consulting Provides the cloud foundation that hosts the customer's data, application, and AI workloads.
  2. Storage
    • BigQuery Serves as the governed analytics and storage layer for high-volume operational data, features, and reporting.
  3. Serving
    • CX Agent Studio Powers conversational voice and chat experiences that automate customer or employee interactions.
  4. Orchestration
    • Gemini Enterprise for Customer Experience Powers conversational voice and chat experiences that automate customer or employee interactions.
    • Gemini Enterprise Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.

Evidence from the source

Best Buy deployed Gemini Enterprise for Customer Experience to use AI to enable seamless customer journeys from purchase through to support. The platform allows internal Best Buy teams to build conversational voice and c
Gemini Enterprise for Customer Experience Gemini Flash BigQuery CX Agent Studio Google Cloud Consulting
Gemini Enterprise for Customer Experience enables Best Buy to achieve two critical goals: deliver self-service to callers and support customers who need to speak to a person by ensuring they have the necessary background
How Best Buy is elevating customer care with Google Cloud AI
Underpinning the environment is Gemini Flash, a voice-native audio model that drives Best Buy's automated computer troubleshooting experience.
Transparency Evidence & methodology 71/100 reference value · 87% 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 value71/100
  • 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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 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 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

    Best Buy customer story

    First-party
    https://cloud.google.com/customers/bestbuy-ai
    Source type
    Vendor-published customer story
    Published
    Apr 9, 2024
    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 Apr 9, 2024 · Google Cloud Press Corner · 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.

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