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For example, Cloudbeds Intelligence analyzes billions of future-looking data points, including competitor rates, events, holidays, and search information, combined with a property’s own data to understand how every combination impacts profit.
Cancellation data: How types of guests and times of year affect cancellation and no-show rates. Outlier factors: How special events and unusual occurrences have historically affected demand (e.g. bad weather, surprise events or eventcancellations).
Public holidays and popular local events will also affect how much you can charge per room. These might follow the four seasons of the year, or they could be defined by school holidays, festive seasons or major local events. Market demand Most hotels see a spike in demand on weekends when compared to weekdays.
It’s not just about how much a guest pays for a room, but also about how their entire stay can be enhanced and monetised. This includes offering tailored packages, suggesting room upgrades, promoting in-house dining experiences, and even curating special entertainment events.
Hotel statistics may include occupancy rates, revenue figures, guest statistics, cancellation rates, booking channel statistics and more. The average booking lead time for hotels is 29.7 The averagelength of stay is 1.93 The averagecancellation rate is 20%.
As recently as 2016 STR measured the impact of Airbnb on hotels by impact on ADR during special events; for example, during the Boston Marathon hotel rates grew 5% year over year when unit supply on Airbnb had grown by 76%. 9flats has a similar focus on renting and staying in someone else’s apartment.
For example, if you receive a lot of group bookings, you may decide to divide them into social (weddings, events, etc.) Analyze booking data for each segment to look for patterns such as: City of origin Booking channels Lead times Stay patterns (E.g., and corporate (meetings, conferences, etc.) Don’t overcomplicate things.
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