Here are my answers to the questions:
What forecasting procedure would you recommend for making the Tuesday afternoon
forecast of each day’s demand for the following Saturday through Friday?
I recommend using a time series forecasting model, such as Exponential Smoothing (Holt-
Winters method) or ARIMA, to predict daily demand.
Use historical demand data segmented by day of the week to account for seasonal trends.
Factor in special events, holidays, and recent trends that could influence demand.
Apply a moving average method if the demand is stable, or use machine learning models like
Random Forest or Gradient Boosting if there are complex patterns.
What is your forecast for Saturday?
Without access to data, I can’t give a precise number. However, I would estimate demand
using:
Last few Saturdays' demand (weekly trend analysis)
Recent booking patterns and reservations
External factors like events, weather, and market trends
What will you do about the current request for up to 60 rooms for Saturday?
Check availability: If there are enough rooms, confirm the booking.
Overbooking policy: If demand is uncertain, consider a controlled overbooking strategy
based on past cancellation rates.
Price adjustments: If demand is higher than usual, apply dynamic pricing to maximize
revenue.
Alternative arrangements: If fully booked, offer waitlist options or partnerships with nearby
hotels.
Would you like me to refine the forecasting model further based on a specific dataset?
How is the hotel reservation system?
A hotel reservation system is software used to manage bookings, availability, and pricing. It
typically includes:
Channel management (OTA integration with platforms like Booking.com, Expedia)
Direct booking management (website or app-based reservations)
Inventory control (room availability tracking)
Pricing & forecasting (dynamic pricing based on demand)
Guest management & CRM (customer records, preferences)
Forecast? Should we use this pickup ratio?
The pickup ratio (percentage of rooms booked over time leading up to a date) is a useful
metric for demand forecasting.
Use it if past trends show consistency in last-minute bookings.
If demand is irregular, combine the pickup ratio with time series models (ARIMA,
Exponential Smoothing) for better accuracy.
Forecast for pickup ratio?
To forecast pickup ratios, analyze historical booking patterns:
Calculate pickup trends for similar days (e.g., Saturdays before holidays).
Use linear regression or machine learning models to predict how pickup ratios will change.
Consider external factors like events, holidays, and promotions.
DOW?
DOW (Day of the Week) analysis is crucial for hotel forecasting.
Different days exhibit different booking behaviors (e.g., higher demand on weekends,
business travel on weekdays).
Adjust forecasts based on historical DOW demand trends and seasonality patterns.
Improve forecasting?
Use machine learning models (Random Forest, XGBoost) to capture complex patterns.
Incorporate real-time data (weather, local events, competitor pricing).
Apply dynamic pricing based on demand forecasts.
Regularly update historical data models to reflect recent trends.
Consider cancellation probabilities to refine availability predictions.
