GLOBAL RESULTS
In three out of four cases, consumers find lower prices on Online Travel Agencies (OTAs) compared to hotel websites. This disparity is largely attributed to the significant bargaining power held by larger OTAs, enabling them to negotiate more favorable rates with hotels,and consequently, offering more competitive prices to consumers.
Graph 1. Global BML in all 61 Locations.
The competitive landscape of hotel pricing is heavily influenced by OTAs. In 75% of cases, at least one OTA offers lower rates than hotel websites, highlighting intense competition in the OTA market.
However, this dynamic changes with the established duopoly of Booking and Expedia groups, where the occurrence of lower OTA rates drops to 49%. This reduction can be attributed the added value provided by majors OTAs, such as loyalty programs and broader market reach.
Examining individual OTAs reveals notable differences: Established platforms typically undercut hotel rates by 23% to 35%. These OTAs can compete with hotels by offering the same or higher prices because they also add value with other factors such as branding or loyalty programs. Conversely, smaller and newer OTAs like Vio and Super, adopt more aggressive pricing strategies, with ‘Lose’ rate often exceeding 40% – Vio at 56% and Sper at 47% – using low prices to gain market share and visibility.
Graph 2. BML by OTA (Top 15 OTAs by Impression share).
ORIGIN
Two approaches:
In markets like Buenos Aires, international competition is substantial, while other markets show a trend of ‘fighting for locals’ with less pronounced differences.
In the United States, increased competition for the local market leads to a higher ‘Lose’ rate for locally originated bookings.
Overall, these trends balance out, showing no significant overall differences. The U.S has a higher ‘Lose’ rate in the international market, resulting in a nearly uniform global average.
Graph 3. BML by Origin (local market vs international market).
ANTICIPATION
The disparity between OTA prices and direct hotel rates is significantly influenced by booking timing. The ‘Lose’ rate is most pronounced with same-day bookings, bolstering OTA performance, especially during periods of last-minute bookings and increased demand.
Default searches for same-day or same-week stays impact the earliest stages of the customer journey, driving more traffic to OTAs.
Booking anticipation is a critical factor in travel pricing dynamics.. OTAs gain a
competitive advantage in last-minute bookings, where the price disparity is notably heightened. This strategic edge allows OTAs to attract customers seeking the best deals at the eleventh hour.
Graph 4. BML by Anticipation.
LENGTH OF STAY
The pricing disparity between OTAs and direct hotel bookings is closely linked to the length of stay. Shorter stays, especially one-night bookings, exhibit significantly higher price differences compared to longer stays, such as week-long bookings. This trend is most pronounced in one-night bookings.
The greater price disparity for shorter stays can be attributed to the higher volume of searches for 1- or 2-night stays. Frequent searches and bookings for these shorter stays amplify the observed pricing differences between OTAs and hotels.
Graph 5. BML by Length of stay.
TRAVELER TYPE
Bookings made by families, especially those with children, exhibit 35% less price disparity
between OTAs and direct hotel rates compared to bookings made by couples and solo travelers.
This trend suggests hotels offer more competitive pricing to families, likely to accommodate their broader needs and higher overall travel costs. Conversely, OTAs seem to focus more on the higher demand from couples and solo travelers, who face steeper price disparities.
Graph 6. BML by Traveler Type.
DIFFERENT OTA TYPES, DIFFERENT PATTERNS
Different types of OTAs employ distinct strategies. Mainstream OTAs use a nuanced approach, tailoring their tactics to specific situations and market conditions, demonstrating a sophisticated market understanding. In contrast, smaller OTAs consistently aim to offer the lowest prices, focusing on cost over context.
This distinction is evident in how major OTAs respond strategically to various factors, while smaller OTAs take a straightforward, price-focused approach. These patterns highlight the complexity and diversity within the OTA market, emphasizing the importance of strategic adaptability in the competitive travel industry.
HOTEL CATEGORY
Pricing competition between hotels and OTAs intensifies for higher-category establishments, particularly 5-star hotels. Notably, there’s a 6% greater price disparity in searches for 5-star hotels compared to 3-star hotels. This “5-star hotel disparity bonus” appears linked to the higher Average Daily Rate (ADR) and consequently high earnings per booking.
Graph 7. BML by hotel category.
DEVICE
Larger OTAs like Booking.com and Expedia group commonly offer different prices on mobile platforms , as shown on the right graph. This practive is noticeably absent among many smaller OTAs on the left one (see the Graph 8. BML by Device).
Graph 8.1. BML by Device (Booking.com and Expedia).
Graph 8.2. BML by Device (Other OTAs).
LOCATION
Lose rates vary significantly across destinations, ranging from 60% to 92% in Las Vegas. A consistent trend across all destinations is Booking.com’s dominance, featuring a sponsored offer 85% of the time—50% more frequent than the second-leading OTA, Expedia.
Regional OTAs, such as Wotif in Australia and Check24 in Germany, also play a significant role. Major groups strategically use ‘second brands’ like Priceline and Agoda for Booking, and Hotels.com, Orbitz, Wotif, and Travelocity for Expedia. These brands are particularly aggressive, showing higher lose rates in specific local markets.
Between December 2023 and January 2024, the top three cities with the lowest lose rates changed dramatically. Dublin jumped to first place from the sixth, Palma moved from third to second, and Shanghai climbed twenty-five positions to enter the top three.
Figure 1. TOP 3 position for December 2023.
Figure 2. TOP 3 position for January 2024.
CONCLUSIONS
This report highlights the distinct strategies employed by OTAs of various sizes and their significant impact on the competitive dynamics within the hotel industry.
Mainstream OTAs adopt a sophisticated approach, meticulously tailoring their strategies to specific situational demands and market conditions. They focus on high-demand market segments, especially during peak periods.
In contrast, smaller OTAs embrace a more straightforward tactic, consistently offering the lowest prices without considering contextual factors. This approach is prevalent in high-demand, high-priced searches, leading to higher ‘Lose’ situations for direct hotel pricing. Major OTAs, however, demonstrate more selective and nuanced behavior.
The influence of major OTAs extends beyond pricing strategies. Their dominance in sponsored links and top listings, significantly shapes the market. Top hotel chains achieve higher ‘Meet’ rate and lower ‘Lose’ or ‘Beat’ rates, suggesting that their direct channels don’t need as aggressive pricing strategies as OTAs to remain competitive.
These insights provide a nuanced understanding of the diverse strategies and competitive behaviors exhibited by OTAs, shedding light on the complex interplay between pricing, market positioning, and consumer behavior in the hotel booking ecosystem.
METHODOLOGY NOTES
The “World Parity Report” delivers a commanding analysis of pricing strategies across the top 60 tourism destinations globally. This report, based on rigorous data collection and analysis, aims to establish our authority in hotel pricing strategies and parity.
Using a stringent methodology and demand data, we carefully curated a representative sample of properties from each destination, achieving a confidence level of 95% in most cases and a minimum of 90% in others. Our research is anchored in an extensive dataset acquired by scraping information from Google Hotels, ensuring alignment with actual consumer behavior and industry practices.
Central to our analysis are the Key Performance Indicators (KPIs) known as BML—beat, meet, and lose—where we benchmark Online Travel Agency (OTA) prices against direct hotel prices.
