Accenture Retail North America

Best Buy

Customer Service Automation

Best Buy wanted to improve customer support and employee assistance at a time when shoppers expected faster, more personalized service, but the company needed a scalable self-service channel and better tools for care agents without losing empathy or relevance in support interactions.

Read the original story Explore all use cases

Reported impact

Business outcome

Best Buy created a more personalized and convenient support experience for customers while giving agents faster access to recommendations and information, helping the company improve service efficiency and let employees focus on more meaningful interactions.

Solution

Best Buy worked with Accenture and Google to deploy generative AI across both self-service and agent-assisted support journeys. The front-end experience is a gen AI-powered virtual assistant available on BestBuy.com, in the Best Buy app, and through customer-support channels, where it helps people troubleshoot products, change delivery or scheduling details, and manage software, Geek Squad, and membership-related tasks. When a request needs a human, the assistant passes the interaction to care agents who use gen AI tools that analyze conversations in real time, recommend relevant responses, suggest solutions, and surface the next best guidance for the case. Additional AI capabilities automate routine work such as navigating knowledge bases and detecting sentiment from calls so recurring issues can be prevented. The architecture blends self-service automation, human escalation, and real-time agent assist to make service more convenient for customers and more efficient for support teams.

Data flow

Customers start support requests through the website, mobile app, or contact channels, where the gen AI-powered virtual assistant handles common tasks and questions. If a case needs escalation, the interaction is handed to care agents whose AI tools analyze the live conversation, pull guidance from knowledge bases, detect sentiment, and recommend next actions so the issue can be resolved faster and more consistently.

Solution architecture

4 components · 3 layers

Compute
- gen AI tools Assist care agents in real time by recommending responses, guidance, and solutions during live conversations.
- sentiment detection Analyzes support calls to identify tone and recurring issues that can be addressed more proactively.
Storage
- knowledge bases Supply reference information that AI tools navigate automatically so agents can spend less time searching for answers.
Serving
- gen AI-powered virtual assistant Provides self-service support across digital and customer-contact channels for troubleshooting, order changes, and subscription management.

Architecture clues

Agent-assist tools analyze conversations in real time.
Knowledge-base navigation is automated for routine information retrieval.
Sentiment and call data are used to prevent similar issues in the future.
The virtual assistant is available on the website, app, and support channels.

Evidence from the source

"Other tools automate routine tasks such as navigating knowledge bases"

"The company has teamed up with Accenture and Google to develop innovative, personalized gen AI solutions that enhance the support experience for both customers and employees."

"The tools assess customer conversations in real-time and provide relevant recommendations and guidance."

"They can also detect sentiment and use data from calls to prevent similar issues in the future."

"a new gen AI-powered virtual assistant ... when they shop on BestBuy.com, use the Best Buy app, or call Best Buy customer support"

Transparency Evidence & methodology 75/100 reference value · 90% confidence · 2 sources

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

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 value75/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth25/25
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 impact0/25
0 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence90%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability23/25
11 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 transparency9/10
2 source references document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity8/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

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

Accenture
Best Buy customer story

First-party
https://www.accenture.com/en/case-studies/ai-data/best-buy-humanizes-customer-experience-genai

Source type
Vendor-published customer story

Published
May 22, 2026
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
Best Buy Investor Relations
Corporate Overview

First-party
https://investors.bestbuy.com/overview/default.aspx
The overview states that in fiscal 2026 Best Buy operated more than 1,000 retail stores in North America; with no headquarters country found in the opened source, North America is the closest defensible operating region.

Source type
Official investor relations
Supports
- Region

Date evidence

Published on source page May 22, 2026 · Accenture · First-party
Open date source

Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Similar use cases

See all like this

Accenture May 22, 2026 Bricorama Retail France Bricorama wanted to help DIY customers make better painting decisions online, but shoppers often struggled to choose colors, estimate quantities, and visualize results confidently without expert guidance or a seamless… 17 messages per discussion Customer Service AutomationMarketing Personalization Azure OpenAI Service · ChatGPT services · pAInt

OpenAI Modified · Oct 16, 2026 Grupo Boticário Retail Latin America Grupo Boticário needed to support a complex multi-brand reseller and franchise ecosystem across many channels while reducing repetitive operational work and preserving the human relationships that drive sales and… 100% of conversations generated in appchat and WhatsApp Enterprise covered since November 2025 Customer Service AutomationSales Automation OpenAI foundation models · WhatsApp Enterprise · appchat

Google Cloud Modified · Oct 9, 2026 Central Retail Food Retail Thailand Replaced ~2,000 monthly manual Excel reports with a single source of truth on BigQuery through Looker dashboards Central Retail Food unifies its ecosystem on Google Cloud, moving from fragmented data to conversational… Addresses 50% to 60% of customer service queries with automated chatbots built on Conversational Agents Customer Service AutomationMarketing Personalization BigQuery · Gemini · Gemini platform