Hotel Revenue Forecasting
Data Science of Hotel
Revenue Forecasting Mastercourse
with Excel and SQL
Who has registered for this Certification course:
Revenue Managers | Finance & Accounting Managers | Asset Managers
General Managers | F&B Managers | Consultants | Operations Managers
Short, One-topic, On-demand Anytime lessons.
Bite-size, easy, pre-recorded lessons. 5-10 minute videos,
allowing you to fit this course into your lifestyle.
Start any time. Finish any time. Come Back forever.
20 Hours of Self-Paced Videos | Real Hotel Data | Hospitality Case Studies
Downloadable Excel Templates | Certificate of Completion
You get Lifetime access to the course.
Allowing you to come back to review the material at any time.
Plus UNLIMITED One-on-One Sessions with the Instructor.
What You Will Learn
Take your Forecasts to the Ultimate level
with Multi-dimensional measures and Statistical and Probabilistic KPI’s.
Fully-automate your Forecasting.
Use advanced mathematical techniques to see
trends that others cannot.
Learn to identify signals and discard the noise.
Effectively communicate patterns and results
to managers and top executives in your organization.
Forecasting Techniques that are transferrable to many industries.
You Will Also Learn
Data Extraction | Data Cleansing | Data Dimensioning
Averaging Models | Statistical Models | Probabilistic Models
The Best Predictive Techniques
- Data Extraction and Transformation: Data extraction and transformation are vital steps in the forecasting process. Accurate and relevant data is essential for building reliable forecasting models.
- You will learn to use SQL and Excel to extract data from various sources, clean and preprocess it, and transform it into a suitable format for forecasting analysis.
- Statistical Concepts for Forecasting: Statistical concepts serve as the foundation for forecasting. This module covers key concepts such as probability, probability distributions, correlation, regression analysis, and time series analysis.
- You will learn to analyze data patterns, identify relationships, and make informed forecasts.
- Excel Forecasting Functions: Excel is a widely used tool for data analysis and forecasting. This module focuses on the specific forecasting functions available in Excel, such as FORECAST, TREND, GROWTH, and LINEST.
- You will learn how to leverage these functions to generate forecasts, analyze trends, and evaluate the performance of forecasting models. Proficiency in Excel forecasting functions enhances efficiency and productivity in forecasting tasks.
- Excel Array Functions: Array functions in Excel provide advanced capabilities for forecasting calculations. This module introduces students to the concept of array formulas and their application in forecasting.
- You will learn how to leverage Excel array functions to perform complex calculations on multiple data points simultaneously, leading to efficient and effective forecasting analysis.
- Simple Moving Average: The simple moving average is a fundamental forecasting technique that smoothes out variations in data. This module explores the concept of moving averages and its applications in forecasting.
- You will learn how to calculate and interpret moving averages, select appropriate window sizes, and utilize them for short-term forecasting. Mastering simple moving average techniques enables you to identify trends and make quick forecasts based on historical data.
- Weighted Moving Average: Weighted moving averages offer a more nuanced approach to forecasting by assigning different weights to past observations. This module delves into the calculation and interpretation of weighted moving averages.
- You will understand how to assign weights based on data significance and use this technique to capture changes in data patterns. Proficiency in weighted moving averages enhances forecasting accuracy, particularly when recent data points carry more importance.
- Exponential and Double Smoothing: Exponential smoothing techniques provide a flexible and adaptable approach to forecasting. This module explores single-exponential smoothing and double-exponential smoothing.
- You will learn how to apply exponential smoothing methods to capture trend and seasonality in data. Understanding these techniques equips you with the ability to handle time series data and generate forecasts that account for different components of variation.
- Exponential Smoothing with Trend: Exponential smoothing with trend extends the capabilities of exponential smoothing by incorporating trend analysis. This module focuses on techniques like Holt’s linear exponential smoothing to forecast data with both level and trend components.
- You will learn how to adjust smoothing parameters to capture trend changes effectively. Proficiency in exponential smoothing with trend allows for more accurate forecasting in situations where data exhibits a consistent trend over time.
- Seasonality Smoothing: Seasonality smoothing is crucial for forecasting data that exhibits recurring patterns at regular intervals. This module covers methods for seasonality decomposition and adjustment, such as seasonal indices and deseasonalization techniques.
- You will gain the ability to identify and handle seasonality in data, resulting in more accurate and reliable seasonal forecasts.
- Bayesian Forecasting: Bayesian forecasting offers a probabilistic approach that incorporates prior knowledge and observed data. This module explores Bayesian inference and its application in forecasting. Proficiency in Bayesian forecasting equips you with a valuable tool for handling uncertainty and incorporating expert opinions in forecasting analysis.
- You will understand how to update prior beliefs with data to generate posterior distributions and probabilistic forecasts.
- Regression Techniques: Regression analysis plays a crucial role in forecasting when relationships exist between the dependent variable and one or more independent variables. This module introduces students to simple linear regression, multiple linear regression, and non-linear regression models. Proficiency in regression techniques equips you with a powerful tool to forecast based on the influence of various factors on the dependent variable.
- You will learn how to identify relevant variables, estimate regression coefficients, interpret results, and use regression analysis to forecast outcomes.
- Measuring Forecast Accuracy: Measuring forecast accuracy is essential for evaluating the performance of forecasting models. This module focuses on commonly used metrics such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE).
- You will learn how to calculate and interpret these metrics to assess the accuracy of their forecasts. Understanding forecast accuracy metrics enables students to objectively evaluate the performance of different forecasting techniques and make informed decisions.
- Improving Forecast Accuracy: Forecast accuracy improvement is a continuous process in forecasting. This module covers techniques and strategies for enhancing forecast accuracy. Mastery of techniques for improving forecast accuracy empowers you to generate more reliable and actionable forecasts.
- You will learn how to analyze forecast errors, identify patterns or biases, and diagnose the root causes of inaccuracies. You will explore methods for model selection, parameter tuning, incorporating external factors, and leveraging expert judgment to refine their forecasting models.
Applied to Real Hotel Data Case Studies
- Forecasting Year-over-Year Growth: Forecasting year-over-year growth is crucial for hotels to anticipate and plan for future revenue performance. This case study focuses on understanding historical trends, identifying factors influencing growth, and developing forecasting models that project revenue growth for the upcoming periods.
- You will learn how to predict revenue growth accurately, enabling effective resource allocation and strategic decision-making.
- Dynamic Forecasting for Transient Sales: Transient sales, including individual bookings and walk-in guests, are a significant revenue source for hotels. This case study emphasizes dynamic forecasting techniques tailored to transient sales, considering factors such as market demand, pricing strategies, and booking patterns.
- You will learn how to develop flexible and responsive forecasting models, enabling hotels to maximize revenue potential, optimize pricing decisions, and efficiently manage transient inventory.
- Forecast Negotiated Rate Sales: Negotiated rate sales involve agreements with corporate clients or groups for specific rates and room allocations. Forecasting negotiated rate sales is essential for hotels to effectively manage inventory and optimize revenue. This case study focuses on techniques to forecast negotiated rate sales based on contract terms, historical performance, and market dynamics.
- You will gain insights into predicting negotiated rate sales accurately, contributing to revenue optimization and client relationship management.
- Forecast No-Shows and Cancellations: No-shows and cancellations have a significant impact on hotel revenue and operations. This case study explores methods to forecast and manage the likelihood of no-shows and cancellations based on historical data, booking patterns, and other relevant factors.
- You will learn techniques to predict these events accurately, enabling hotels to minimize revenue loss, optimize room utilization, and allocate resources efficiently.
- Forecast Group Pickup and Attrition: Forecasting group pickup and attrition is critical for hotels to manage group reservations effectively. This case study focuses on techniques to predict the actual number of guests attending a group event and the potential cancellations or no-shows.
- You will learn how to develop accurate forecasts, allowing hotels to optimize inventory utilization, revenue, and operational efficiency.
- Forecasting Techniques for Group Sales: Group sales play a significant role in hotel revenue, and accurately forecasting group sales is essential for proper capacity planning and revenue management. This case study explores forecasting techniques specific to group sales, considering factors such as group booking patterns, lead times, and historical data.
- You will learn how to utilize forecasting methods tailored to group sales, enabling hotels to optimize revenue potential and effectively manage group inventory.
- Forecast Food and Beverage: Food and beverage operations are essential revenue streams for many hotels. This case study focuses specifically on forecasting techniques for food and beverage sales, covering factors such as historical trends, seasonal variations, menu engineering, and event planning. Accurate forecasting of food and beverage sales helps hotels optimize inventory, staff appropriately, control costs, and tailor marketing strategies, leading to increased profitability and customer satisfaction.
- You will learn how to analyze historical F&B sales data, consider factors such as seasonality, day of the week, special events, and customer behavior, and develop accurate forecasts for F&B revenue.
- Forecast Ancillary Sales: Ancillary sales, such as food and beverage, spa, and other services, contribute to a hotel’s overall revenue. This case study focuses on forecasting techniques specific to ancillary sales and covers.
- You will learn how to analyze historical data, market trends, and guest behavior to predict ancillary sales accurately. This case study enables hotels to plan and optimize ancillary revenue streams, enhance guest experiences, and drive overall profitability.
- Building a Forecasting Model: This case study focuses on the practical aspects of building a forecasting model specifically tailored for hotel revenue forecasting. Students will learn the step-by-step process of constructing a forecasting model, including data collection, preprocessing, feature selection, model selection, and model evaluation. Emphasis will be placed on incorporating relevant variables and industry-specific factors into the model.
- You will learn the skills to develop customized forecasting models that accurately predict hotel revenue, enabling informed decision-making and effective revenue management.
- Build Rolling Forecasts: Rolling forecasts allow hotels to continuously update and refine their revenue forecasts based on the most recent data and market conditions. This case study focuses on the concept of rolling forecasts and the techniques used to build and maintain them.
- You will learn how to incorporate new data, adjust forecast models, and analyze forecast variances to ensure the accuracy and relevance of rolling forecasts. By mastering this case study, students will be equipped to develop agile forecasting processes that reflect the dynamic nature of the hotel industry.
- Forecast Automation: Automating the forecasting process can significantly enhance efficiency and accuracy in hotel revenue forecasting. This exercise explores techniques and tools for automating the forecasting workflow, including data extraction, data transformation, model selection, and report generation.
- You will gain hands-on experience with forecasting software and learn how to leverage automation to streamline the forecasting process, reduce manual effort, and improve overall forecasting quality. Mastery of this exercise empowers students to build efficient and reliable automated forecasting tools for hotel revenue management.
Master the Science of Hotel Forecasting.
100% Money-Back Guaranteed.
All self-paced course material
Course material released November 29, 2024.
No programming or mathematics background required.
No-nonsense, Immediate-Impact Training
Most hotel analysis courses focus an a specific software or platform, thereby limiting your ability to transfer your knowledge to other systems. Not this one.
THIS COURSE IS VENDOR AGNOSTIC. WE DO NOT PROMOTE OR LIMIT THE COURSE TO ANY SPECIFIC RMS, PMS, STATISTICS PACKAGE OR ANY OTHER SOFTWARE. WE ALSO DO NOT PROMOTE ANY THIRD PARTY COMPANIES OR CONSULTING SERVICES INCLUDING OWL. THIS COURSE IS NOT AFFILIATED WITH ANY HOTEL ASSOCIATION OR HOSPITALITY COMPANY.
The Instructor
Hi, I am Robert Hernandez and I am an expert in the field of Mathematical Profit Optimization and Analytics. I have a degree in mathematical optimization and I have spent my entire career building data-driven forecasting and revenue optimization models for companies in over 20 different industries, from tech to tourism. I want to save you a lot of time and frustration by teaching exactly what you need to know to be a top Data Analyst. Read More+
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Become a Forecasting Expert, the easy way .
100% Money-Back Guaranteed.
All self-paced course material
All Course Materials Available September 17th, 2024