How Our Cruise Pricing Data Works

Transparent methodology for institutional-grade cruise pricing intelligence.

All Aboard Analytics tracks cruise pricing across RCL, CCL, NCLH, and VIK to provide equity research teams and investment funds with independent signals on yield management, promotional activity, and demand trends.

This page explains how we collect, process, and analyze that data—and where the limitations are.

Intended audience: Institutional investors, equity research teams, and alternative data evaluators. This methodology is not designed for consumer booking decisions or real-time trading signals.

At a Glance

*Year-over-year comparisons require 12 months of historical data for statistically meaningful analysis. Current period-over-period analysis is limited to month-over-month and week-over-week.

Data Collection

Source

We collect pricing data from online travel agencies (OTAs) that sell cruise inventory. This captures observable market prices—what consumers actually see when booking—rather than internal cruise line data or promotional list prices.

Why OTA Data Matters for Investment Analysis

Cruise line investor relations decks show what happened last quarter. OTA pricing shows what's happening now.

When demand strengthens and yield management holds price, that's visible too.

OTA pricing also reflects actual bookable fares, not promotional claims. A cruise line can announce a "30% off sale" while OTA prices show minimal movement. We track the latter.

What We Capture

For each sailing:

• Price by cabin type (Interior, Ocean View, Balcony, Suite)
• Ship and cruise line
• Departure date and itinerary length
• Embark and disembark ports
• Port stops and itinerary

Refresh Frequency

We update pricing 4x daily across the full fleet. This cadence captures intraday price changes and ensures analytics reflect current market conditions rather than stale snapshots.

Coverage

This mapping aligns with public equity tickers for direct application to investment analysis. The dashboard supports brand-level drill-down within each ticker—useful when Royal Caribbean and Celebrity show divergent pricing behavior within the RCL portfolio.

Data Processing

Raw pricing data requires cleaning before analysis. We apply several processing layers to ensure institutional-grade data quality.

Sailing Deduplication

We identify unique sailings using a composite key: ship + departure date + itinerary length + embark port. This prevents the same cruise appearing across multiple OTAs from inflating counts.

All departure dates normalize to UTC to prevent timezone-related duplicates.

Outlier Filtering

Pricing data from web sources occasionally contains errors—mispriced cabins, scraping artifacts, or placeholder values. We filter these at multiple stages:

• Invalid prices (zero or negative values) rejected at collection
• Prices far below historical baselines flagged and excluded
• Large price swings between consecutive scrapes filtered from analytics

Our approach is conservative: excluding questionable data beats polluting the signals analysts rely on.

Port Standardization

Port names normalize to a consistent format and map to regions. "Ft. Lauderdale" and "Fort Lauderdale" resolve to the same port. This enables destination-level analysis (Caribbean, Alaska, Mediterranean, etc.) across the fleet.

Core Metrics

PPPN (Price Per Person Per Night)

Definition: Per-Person Fare ÷ Number of Nights

OTAs display cruise fares as per-person prices assuming double occupancy—this is industry standard. We capture that per-person fare and divide by itinerary length to normalize across the fleet.

Example: A 7-night Caribbean cruise showing $700 per person: $700 ÷ 7 nights = $100 PPPN

Investment relevance: PPPN normalizes pricing across itinerary lengths, enabling fleet-wide yield comparisons. When an operator's average PPPN rises 3% month-over-month while capacity stays flat, that's a direct yield signal.

Week-over-Week Change

Calculation: (Current 7-day avg − Prior 7-day avg) ÷ Prior 7-day avg × 100

Investment relevance: WoW change surfaces pricing momentum. Consistent weekly gains suggest demand strength and yield optimization. Consistent declines may signal inventory pressure or competitive discounting.

We calculate WoW change at three levels:

• Market-wide: All operators combined
• Ticker level: RCL, CCL, NCLH aggregates
• Cruise line level: Individual brands within each ticker

Example: RCL ticker shows +0.8% WoW, but drilling down reveals Royal Caribbean International at +7.2% while Celebrity Cruises is at -5.8%. The ticker-level figure masks meaningful brand divergence.

Fleet Pricing Activity

What it measures: Percentage of an operator's tracked sailings with 10%+ price movement within the selected window (7 days, 30 days, or QTD).

Signals:

Note: "Signals" refer to threshold-based pricing movements derived from our data, not investment recommendations.

Investment relevance: Fleet-wide signals indicate coordinated pricing action rather than isolated sailing adjustments. A drop signal across 15% of CCL's fleet suggests deliberate promotional strategy, not random noise.

Example: RCL shows 12.8% of sailings dropping 10%+ while 13.6% are rising 10%+. This bidirectional movement—both signals triggered—suggests active inventory rebalancing, possibly discounting near-term Caribbean sailings while raising prices on peak summer departures.

Promotional Discipline

What it measures: Percentage of sailings per cruise line with 10%+ price drops, tracked monthly by brand.

Interpretation:

Investment relevance: Promotional discipline directly impacts yield and margin. An operator showing 25% promo intensity in November jumping to 45% in December signals a shift in strategy—either demand softened or they're chasing volume over yield.

Note: This metric tracks drops only, not bidirectional volatility. It answers: "How much of their fleet is this operator discounting?"

Update frequency: Monthly. First week of each month reflects prior month's complete data.

Example: Royal Caribbean drops from 82% promo intensity in November to 27% in December. Celebrity moves from 80% to 25%. Both brands tightened discipline heading into wave season—a positive yield signal for RCL.

Booking Curve

What it measures: Average PPPN segmented by days until departure.

Buckets: 0-7, 8-14, 15-30, 31-60, 61-90, 91-180, 180+ days out

Window: Rolling 30 days

Investment relevance: The booking curve reveals inventory management and demand patterns. Typical cruise pricing rises as departure approaches—operators extract premium from late bookers as cabins fill. Deviations from this pattern signal either:

• Last-minute discounting: Inventory pressure, sailing won't fill at current price
• Flat or inverted curve: Demand softness across booking windows

Example:

The 8-14 day window at $174 is the lowest—a 6% discount to last-minute pricing. Operators aren't heavily discounting distressed inventory; they're holding price close-in. The 31% premium on 180+ days reflects confidence in forward demand.

Cabin Yield

What it measures: Average PPPN by cabin category with premium percentages relative to Interior.

Categories: Interior, Ocean View, Balcony, Suite

Window: Rolling 30 days

Investment relevance: Cabin yield spreads reveal demand mix. Expanding suite premiums indicate strong high-end demand (positive for revenue per passenger). Compressing premiums suggest operators are discounting premium inventory to fill ships.

Example:

Suite premium over 100% indicates strong demand at the high end. If this compresses to 85% next month, it may signal softness in premium inventory—worth monitoring against management commentary on mix.

Yield by Itinerary Length

What it measures: Average PPPN by cruise length bucket.

Buckets: Short (2-4 nights), Week (5-7 nights), Extended (8-10 nights), Long (11+ nights)

Window: Rolling 30 days

Investment relevance: Shows relative value and demand across itinerary types.

Example:

Longer cruises ($182/night) price below short cruises ($187/night) on a per-night basis. This reflects typical industry dynamics: longer sailings attract price-sensitive cruisers and require volume to fill.

Limitations

Current Constraints

Historical depth: Data begins October 2025. Year-over-year analysis requires 12 months of history (available October 2026).

OTA vs. direct pricing: OTA prices may differ slightly from cruise line websites. We're building direct API integrations to enhance accuracy.

Inventory visibility: We capture prices, not remaining cabin counts. We see that a Balcony is priced at $200/night but not whether 5 or 500 cabins remain at that price.

Repositioning cruises: Sailings where embark ≠ disembark are included and tagged. Pricing dynamics differ from round-trip itineraries.

On the Roadmap

• Direct cruise line API integrations
• Destination-level filtering (port mapping complete, UI in development)
• Historical backfill via data partnerships

Data Quality Philosophy

Operators adjust prices constantly based on demand, competition, and inventory.

Principles:

1. Exclude rather than pollute. Questionable data points are filtered, not footnoted.
2. Document methodology publicly. Institutional analysis requires knowing how metrics are calculated.
3. Acknowledge limitations. When a question falls outside our coverage, we say so.

Best Applications

Use this data for:

• Tracking yield trends week-over-week and month-over-month
• Comparing promotional intensity across operators
• Identifying brand-level divergence within tickers
• Spotting demand signals before earnings

Less suited for:

• Predicting exact EPS (we don't model costs or capacity)
• Cabin-level inventory analysis (we see prices, not remaining inventory)
• Real-time trading signals (4x daily supports trends, not intraday)

Related Reading

• Understanding PPPN — Deep dive into our core pricing metric
• Fleet Pricing Signals — How we detect coordinated price movements
• Regional Pricing Signals — Geographic breakdown of pricing activity

Questions?

For methodology questions or data requests: hello@allaboardanalytics.com

To explore the dashboard, use the Request Access button in the header above.

Disclaimer

All Aboard Analytics provides pricing data and analytics for informational purposes only. This information does not constitute investment advice, a recommendation to buy or sell any security, or an offer to provide investment advisory services.

Data is derived from third-party sources believed to be reliable but is provided "as is" without warranty of accuracy, completeness, or timeliness. Users should independently verify any information before making investment decisions.

All Aboard Analytics is not a registered investment advisor, broker-dealer, or financial institution.