AWS Media and Entertainment United States

FOX Corporation

Marketing Personalization

FOX wanted to reimagine how audiences, advertisers, and broadcasters interact with media by using generative AI, machine learning, and data to create more relevant experiences at the scale of hundreds of millions of monthly users.

It has over 300 million monthly active users, meaning over 300 million people see one or more pieces of content on FOX every month.

Reported impact

Business outcome

Using AWS, FOX is accelerating data insights to deliver AI-driven products in near real time that are contextually relevant and highly valuable to consumers, advertisers, and broadcasters alike.

  • Customer Experience
  • Revenue Growth
  • Speed to Market

Solution

FOX built AI-driven media products on AWS to turn audience and content data into near-real-time insights for consumers, advertisers, and broadcasters. The company uses Amazon SageMaker for machine learning workflows, Amazon Personalize to tailor experiences, and is exploring Amazon Bedrock and Amazon Titan for newer generative AI capabilities. This stack helps FOX accelerate data insight generation and produce contextually relevant products across brands such as FOX Sports, Tubi, FOX Entertainment, and FOX News. By combining personalization, predictive models, and emerging foundation-model services on AWS, FOX is treating data and AI as a core part of how viewers discover and engage with media.

Data flow

Audience and content data feeds machine learning and personalization services on AWS, which generate near-real-time insights and AI-driven experiences for FOX consumer, advertiser, and broadcaster products.

Solution architecture

4 components · 2 layers
  1. Compute
    • Amazon SageMaker Supports training, fine-tuning, and managed inference for machine learning workloads.
  2. Serving
    • Amazon Personalize Generates personalized recommendations and contextual experiences from behavioral data.
    • Amazon Bedrock Provides foundation-model access for generative AI features and assistants.
    • Amazon Titan Supplies foundation models for customer-facing generative AI features.

Architecture clues

  • FOX Corporation produces and distributes news, sports, and entertainment content through domestic brands, including FOX Sports, Tubi, FOX Entertainment, FOX Television, and FOX News Media Stations.
  • It has over 300 million monthly active users, meaning over 300 million people see one or more pieces of content on FOX every month.
  • Using services including Amazon SageMaker, Amazon Personalize, and exploring new technologies such as Amazon Bedrock and Amazon Titan, FOX is accelerating data insights to deliver AI-driven products in near real-time.

Evidence from the source

FOX Corporation produces and distributes news, sports, and entertainment content through domestic brands, including FOX Sports, Tubi, FOX Entertainment, FOX Television, and FOX News Media Stations.
In this video, Lindsay Silver, FOX’s senior vice president (SVP) of Data & Commercial Technology, describes how the company is reimagining how the world interacts with media by leveraging generative AI, machine learning, and data solutions on AWS to create more moments that matter.
It has over 300 million monthly active users, meaning over 300 million people see one or more pieces of content on FOX every month.
Using services including Amazon SageMaker, Amazon Personalize, and exploring new technologies such as Amazon Bedrock and Amazon Titan, FOX is accelerating data insights to deliver AI-driven products in near-real time that are contextually relevant and highly valuable to consumers, advertisers, and broadcasters alike.
We're extremely proud of the infrastructure we've built but also proud of the relationship and the technologies that Amazon provides us with.
Transparency Evidence & methodology 66/100 reference value · 85% 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 value66/100
  • 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 specificity16/20
    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 confidence85%
  • 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 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 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. AWS

    FOX Corporation customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/fox-summit-ny-2023-keynote/
    Source type
    Vendor-published customer story
    Published
    Jul 1, 2023
    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.