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AWS Retail United States

Saks

Customer Service Automation

Founded in 1924, Saks Fifth Avenue is famous for luxury brick-and-mortar stores that offer a wide selection of premier clothing and accessories.

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Key metrics per interaction saved on after-call work 15 seconds with a few sentences Summarize a 20-minute call contact center analytics Access to near real-time
Saks can now distill a 20-minute call into a few sentences, reducing after-call work by 15 seconds per interaction, which is a significant time saving on the scale of millions of...

Reported impact

Business outcome

When a customer interaction ends, agents must complete after-call work, such as updating customer records and writing detailed notes about the interaction, which can be time-consuming.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement

Solution

Saks , a premier digital store for luxury fashion in North America, knows that understanding a customer's needs is key to delivering the level of service that its clientele expects. In 2022, the company turned to Amazon Web Services (AWS) and adopted Amazon Connect Customer , a contact center powered by artificial intelligence (AI). Thanks to the simplicity of use, seamless incorporation into existing tools, and pay-as-you-go pricing model, Saks gained the flexibility to test and use Connect Customer on its own terms. Using Connect Customer, Saks's contact center supervisors can access near real-time metrics to assist agents during difficult live calls, in addition to historical metrics to monitor agents' performance and trends.

Data flow

Using Connect Customer, Saks's contact center supervisors can access near real-time metrics to assist agents during difficult live calls, in addition to historical metrics to monitor agents' performance and trends. Using this solution, Saks can monitor customer calls to understand what drove a customer to contact customer service.

Solution architecture

4 components · 3 layers
  1. Ingest
    • Amazon Transcribe Converts voice interactions into text for the customer and agent support workflows.
  2. Compute
    • Amazon Bedrock Runs the foundation model layer for the customer and agent support workflows.
  3. Serving
    • Amazon Connect Handles omnichannel interactions for the customer and agent support workflows.
    • Salesforce Service Cloud Voice Provides the agent-facing CRM and voice experience used by the customer and agent support workflows.

Architecture clues

  • For example, Saks captures sentiment analysis data using Conversational Analytics , which provides near real-time contact center analytics and quality management powered by AI.
  • Using Connect Customer, Saks's contact center supervisors can access near real-time metrics to assist agents during difficult live calls, in addition to historical metrics to monitor agents'...

Evidence from the source

In 2022, the company turned to Amazon Web Services (AWS) and adopted Amazon Connect Customer , a contact center powered by artificial intelligence (AI).
Saks can now distill a 20-minute call into a few sentences, reducing after-call work by 15 seconds per interaction, which is a significant time saving on the scale of millions of customer contacts.
Thus, Saks can provide its agents with deeper insights from customer calls, improve agents' performance, and make informed decisions.
To automatically summarize these calls, Saks built an AI solution using Amazon Transcribe , which automatically converts speech to text, and Amazon Bedrock , a fully managed service that offers a choice of high-performing foundation models.
To enhance its customer service, Saks chose Connect Customer so that it can access the data it needed to improve agents' and supervisors' knowledge and interactions with customers.
With AWS services, we are empowering our agents with insights, enabling us to elevate the personalized, high-touch experience our customers expect.
Transparency Evidence & methodology 96/100 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 value96/100
  • 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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics 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
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • 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. AWS

    Saks customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/saks-case-study/
    Source type
    Vendor-published customer story
    Published
    Feb 20, 2025
    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

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

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