Hospitality & Travel Case Interview Guide

Hospitality and travel cases reward candidates who can separate a rate problem from a volume problem and who understand why the industry gave up direct customer ownership to online travel agencies over the past decade. Based on our library of hospitality, airline, and travel cases, interviewers consistently anchor on one metric—Revenue per Available Room, or its equivalents in other travel sub-sectors—and then layer in a distribution or seasonality twist.

The RevPAR Framework

Every hotel and lodging case reduces to the same identity: RevPAR = ADR × Occupancy. Getting comfortable with this relationship before you walk in is non-negotiable.

The interview logic: when RevPAR is declining, the interviewer wants you to isolate whether ADR or occupancy moved first. A rate cut chasing occupancy in a soft market usually destroys more margin than it recovers, because rooms are a perishable, fixed-supply asset—an empty room tonight can’t be sold tomorrow to make up the loss. Full-service hotels defend RevPAR through ADR and premium positioning; budget chains defend it by pushing occupancy through distribution reach and loyalty-driven direct bookings.

Distribution Channel Economics

More than 50% of hotel bookings now happen through digital channels, and this shift from direct relationships to platform intermediation is the single most tested theme in hospitality cases.

flowchart LR
    A[Property/Operator] --> B{Booking Channel}
    B --> C[Direct: Website/App]
    B --> D[OTA: Booking.com/Expedia]
    B --> E[GDS: Corporate/Travel Agent]
    C --> F[0-3% cost, full data ownership]
    D --> G[15-25% commission, limited data]
    E --> H[10-15% cost, corporate volume]

Why this matters in a case: a property or chain considering a “reduce OTA dependency” strategy is really trading reach for margin and data control. The standard answer isn’t “cut OTA cold”—it’s a mix shift: build loyalty programs and direct-booking incentives to win back repeat guests while keeping OTAs for first-time discovery and off-peak fill. This “disintermediation and re-intermediation” tension—providers reaching customers directly while new platforms simultaneously insert themselves—is exactly the paradox major consulting firms cite as the industry’s defining structural shift.

Common Case Archetypes

Based on our analysis of the hospitality and travel case library, four patterns cover most of what you’ll encounter in a live interview.

1. Profitability Decline

Symptoms: RevPAR or load factor falling faster than the broader market or competitive set over 2-3 periods. First move: split performance by property, region, or route before touching portfolio-wide averages—one underperforming asset or market can mask an otherwise healthy book. Then check ADR versus occupancy separately: did rate get cut to chase volume, or did a new competitor or oversupply event pull occupancy down at a stable rate? Also check the cost side—labor and franchise fees are typically the largest controllable lines after fixed asset costs.

2. Market Entry / New Property or Route

Core question: build, acquire, or franchise into a new market?

flowchart TD
    A[New Market Decision] --> B{Established Demand Signal?}
    B -->|Yes, mature market| C[Acquire or Franchise Existing Asset]
    B -->|No, emerging market| D[Build New / Greenfield]
    C --> E[Check Brand Fit and Integration Cost]
    D --> F[Check Demand Ramp Timeline]
    E --> G[Go/No-Go]
    F --> G

Franchising and acquisition move faster and de-risk demand uncertainty but compress margin through franchise fees or acquisition premiums. Greenfield development captures full economics but exposes the operator to a multi-year demand ramp—this is the same build-versus-buy logic that shows up across industries, but the perishable-inventory nature of hospitality (an unsold room tonight is gone forever) makes the ramp period especially costly if demand forecasts are wrong.

3. Loyalty Program and Direct-Booking Strategy

What to probe: incremental cost of the loyalty program (points liability, tier benefits) versus the OTA commission it displaces, and whether loyalty members actually book more frequently or just book the same trips through a cheaper channel. The standard framework compares three levers—price incentive for direct booking, exclusive member perks, and app/website friction reduction—against the blended OTA commission rate being avoided.

4. Seasonality and Demand Management

Situation: how should a resort, cruise line, or seasonal destination manage a business with sharp demand peaks and troughs?

Approach:

1. Quantify the peak-to-trough demand swing and identify which costs are fixed (staff, facility upkeep) versus variable (seasonal labor, F&B)
2. Evaluate dynamic pricing to smooth demand—shoulder-season discounting versus peak-season premium capture
3. Consider diversification: conferences and events, off-season packages, or a secondary demand segment (e.g., business travel filling weekday gaps left by leisure travelers)
4. Weigh capacity flexing (seasonal staffing, temporary closures) against the fixed-cost drag of keeping full capacity open year-round

Key Data Points to Anchor Your Numbers

• OTA commissions typically run 15-25% of booking value—the single largest addressable margin lever in most hospitality profitability cases
• RevPAR below breakeven occupancy for more than 2-3 consecutive periods signals a structural, not seasonal, problem worth flagging early in your framework
• Direct bookings cost a property 0-3% versus 15-25% through OTAs, which is why loyalty and app investment shows up in nearly every distribution-strategy case
• Perishable inventory (unsold rooms, empty seats, unfilled cruise cabins) means occupancy-boosting tactics have a hard deadline—there’s no “sell it next week” option

Key Takeaways

• Hospitality profitability cases resolve to the RevPAR identity—separate an ADR problem from an occupancy problem before proposing a fix
• Distribution channel mix (direct vs. OTA vs. GDS) is the recurring strategic tension: reach and discovery versus margin and data ownership
• Market entry decisions hinge on build-vs-buy-vs-franchise tradeoffs, sharpened by the perishable-inventory nature of the asset
• Loyalty and direct-booking strategy cases require comparing program cost against the OTA commission being displaced
• Seasonality cases test whether you can separate fixed from variable costs and propose demand-smoothing levers rather than just “raise prices in peak season”

Practice Hospitality and Travel Cases with AI Feedback

Ready to apply these frameworks under real interview pressure? Browse our Transportation industry cases or filter by Profitability, Market Entry, and Pricing case types to find hotel, airline, and travel scenarios that match what you’re studying. Our Airline & Aviation Case Interview Guide covers the closely related RASM/CASM framework if your prep spans both sub-sectors.

For structured practice, CasesCoach offers three ways to train: Guided Mode walks you through a hospitality case with interviewer hints when you get stuck—ideal for building your first mental model of the RevPAR identity. Expert Mode simulates full interview pressure with no hand-holding, closer to what you’ll face at a real MBB or Big 4 final round. Chat with Case lets you work through a real hospitality or travel case interactively across five stages—background, framework selection, data analysis, synthesis, and recommendation—so you practice the full arc, not just the opening structure.

Every session scores you across five dimensions—Structure, Analysis, Communication, Business acumen, and Math—so you know exactly which part of your answer needs work. Sign up free to try 3 full cases plus an AI Mock Interview session; upgrade to Pro to unlock all 835+ cases in the library along with multiple Mock Interview sessions. Start with the AI Mock Interview or see plans and pricing to get started.

Understanding RevPAR and distribution economics won’t just help you pass a case interview—it’s the same lens consultants use walking into a real hotel, airline, or travel client engagement on day one.