IBM Automotive global

Scuderia Ferrari HP

Marketing Personalization · Real-time Analytics

Scuderia Ferrari HP needed to turn its mobile app into a richer digital bridge for hundreds of millions of global fans by adding real-time race engagement, AI-powered content, personalization, and a scalable backend that could support both fan growth and editorial operations.

2x increase in daily active users
35% increase in average time spent in-app
36% increase in installed user base
56% increase in Average Race Active Users

Reported impact

Business outcome

Since the relaunch, Ferrari has materially increased fan engagement across daily and race-day usage while giving editorial and operations teams AI-driven tools to create content faster, personalize experiences, and run a more observable, scalable digital platform.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth
  • Scalability

Solution

IBM Consulting worked with Scuderia Ferrari HP to redesign and rebuild the team’s mobile app around fan personas, race-weekend journeys, and a hybrid cloud backend that could serve both lifelong enthusiasts and new followers. The resulting platform blends IBM watsonx.ai for AI-generated race summaries, historical insights, and newer free-text fan interactions with a Race Centre experience that surfaces telemetry, strategy insights, and live analysis. IBM watsonx.data unifies data across Ferrari’s racing, retail, and brand ecosystems so the app can personalize content, support commerce, and power future-ready engagement features. Underneath, IBM Cloud, AWS, and Red Hat OpenShift provide the scalable hybrid infrastructure for the app, while IBM Instana adds observability and automated IT operations. IBM also used AI-assisted development and design thinking to speed delivery, making the app a long-term digital hub rather than a one-off refresh.

Data flow

Fan interactions in the Scuderia Ferrari HP app feed live race, brand, and retail experiences through a hybrid IBM Cloud/AWS/OpenShift backend; watsonx.data unifies those data domains, watsonx.ai generates summaries and insights, and the resulting content powers personalization, Race Centre features, commerce, and community storytelling while Instana monitors platform health.

Solution architecture

8 components · 5 layers
  1. Compute
    • IBM watsonx.ai Generates race summaries, historical insights, AI fan interactions, and telemetry-driven analysis inside the app.
    • watsonx Code Assistant Accelerates development cycles for the evolving app experience through AI-assisted coding.
  2. Storage
    • IBM watsonx.data Unifies data across Ferrari’s racing, retail, and brand ecosystems for analytics and personalization.
  3. Serving
    • IBM Cloud Provides part of the hybrid cloud infrastructure that runs the fan engagement platform.
    • AWS Supplies additional hybrid cloud infrastructure capacity for the app platform.
  4. Orchestration
    • IBM Consulting Led persona research, experience design, architecture redesign, and delivery of the modernized fan app.
    • Red Hat OpenShift Supports the app’s scalable hybrid application architecture across cloud environments.
  5. Governance
    • IBM Instana Adds automated IT operations, reliability monitoring, and observability across the platform.

Architecture clues

  • Instana improves reliability and observability across the platform.
  • The 2026 season added free-text AI fan interactions, countdown quizzes, digital badges, and private prediction leagues.
  • The app runs on a hybrid architecture integrating IBM Cloud, AWS, and Red Hat OpenShift.
  • watsonx.data unifies information across racing, retail, and brand ecosystems.

Evidence from the source

“IBM watsonx.ai powers AI-generated race summaries while the interactive Race Centre delivers telemetry, strategy insights and race analysis.”
“IBM watsonx.data unifies information across Ferrari’s racing, retail and brand ecosystems.”
“Since the app launched in May 2025, the team has seen measurable improvements ... including a reported 35% increase in cumulative downloads and a 62% increase in page views.”
“There are 396 million Scuderia Ferrari HP fans around the world.”
“Under the hood, IBM created a scalable hybrid cloud architecture integrating IBM Cloud, AWS and Red Hat OpenShift.”
Transparency Evidence & methodology 100/100 reference value · 89% 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 value100/100
  • 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 impact25/25
    7 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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 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. IBM

    Scuderia Ferrari HP customer story

    First-party
    https://www.ibm.com/case-studies/scuderia-ferrari
    Source type
    Vendor-published customer story
    Published
    Aug 26, 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

Date evidence

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