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EY Travel and Hospitality global

Royal Caribbean

Customer Service Automation · Marketing Personalization

Royal Caribbean's guest journey was still largely analog - limited web visibility, a minimal app, printed daily newsletters and manual event signups - even as customers expected seamless digital experiences across a fleet that operated both onshore and at sea.

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across a fleet of 40 ships

Reported impact

Business outcome

Royal Caribbean turned digital guest interactions into a richer, lower-friction cruising experience that opens new revenue opportunities, gives guests more control over their trips and helps crews deliver more individualized service without relying on manual processes.

  • Customer Experience
  • Revenue Growth

Solution

Royal Caribbean worked with EY Studio+ and EY technology teams to rebuild the cruise experience around a mobile, data-driven guest platform. The program started by reconfiguring legacy IT systems so the company could recognize each guest individually, authenticate them and personalize their itinerary across ship and shore. EY and Royal Caribbean then created a personalization engine that uses machine learning and analytics to learn guest preferences from boarding onward and recommend activities, food and services through the mobile app. The same digital stack added facial recognition for faster boarding, text notifications for luggage delivery, app-based activity booking and integrated automation that lets guests order from menus or alert crew to food sensitivities. Behind the scenes, the teams solved connectivity and orchestration challenges across fiber and satellite environments so the experience could run consistently across a multi-ship fleet.

Data flow

Guests check in through the app and facial recognition, generating profile data that flows into the personalization engine. Machine learning uses behavior and itinerary signals to recommend activities, while integrated automation pushes notifications, orders and service requests to crews over the ship-and-shore connectivity layer.

Solution architecture

5 components · 3 layers
  1. Compute
    • machine learning Learns from guest behavior to improve recommendations and personalized service actions.
  2. Serving
    • mobile app Acts as the guest-facing surface for check-in, planning, recommendations, ordering and activity booking.
    • facial recognition Speeds guest boarding by authenticating travelers as part of the digital journey.
    • beacon technology Helps route app-based orders and location-aware service delivery to guests on board.
  3. Orchestration
    • personalization engine Tracks preferences and orchestrates individualized recommendations and itinerary experiences.

Architecture clues

  • Facial recognition, notifications and recommendations are all delivered through the same mobile experience.
  • Integrated automation and beacon technology connect app activity to crew-side fulfillment.
  • The experience combines shipboard and shoreside connectivity across fiber and satellite links.
  • The personalization engine sits on top of a rebuilt IT platform that recognizes guests as individuals and customizes itineraries.

Evidence from the source

"EY and Royal Caribbean professionals used data and analytics to create a personalization engine that gives guests mobile control over many different touchpoints in their cruising experience."
"From having guests check in using facial recognition to facilitate faster boarding ... the EY team is helping Royal Caribbean create a seamless customer experience."
"The app uses integrated automation and intelligent personalization to let them explore activities they'd be likely to enjoy."
"The teams used mobile, cloud, machine learning, and data analytics technologies to detangle a complex, layered legacy technology infrastructure."
Transparency Evidence & methodology 70/100 reference value · 83% 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 value70/100
  • 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 scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence83%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency10/10
    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. EY

    How digital transformation opened new channels for growth | EY - Global

    First-party
    https://www.ey.com/en_gl/insights/consulting/how-digital-transformation-opened-new-channels-for-growth
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Industry
    • Region
    • Company size
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • Business outcome
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Apr 22, 2024 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source

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