Prescriptive Analytics and Machine Learning for Revenue Management Leaders

Predictive analytics and machine learning are at the leading edge of revenue management practices in the hospitality industry.

HSMAI’s Revenue Management Advisory Board recently discussed how RM leaders should be thinking about these issues.

First, the basics:

There are industry standards for the categorization of analytics:

• Descriptive: Descriptive analytics have to do with reports and business analysis in relation to the past. These are backward looking and are a record of what has actually happened.
• Predictive: Predictive analytics are looking at what is likely to happen in the future based on what has happened in the past.
• Prescriptive: Predictive analytics try to answer the question: “So what do we do about it?”  In the case of revenue management systems, an example would be optimum price recommendations.

At the same time, machine learning is an exciting opportunity with much applicability for revenue management. At its core, machine learning is when an algorithm learns over time. The advantage is that a machine can comb through a lot of data in a reasonable amount of time (much more efficiently than a human can). The limitations are that the machine learns by making mistakes and gets better over time, so you must have a tolerance for, and take a calculated risk of, failure. 

When you put predictive analytics and machine learning together, you get something special. Predictive revenue management, powered by machine learning, seems to be the direction the industry is taking.

The resulting automation can help a hotel company move forward significantly faster. It will be like in Star Wars when R2-D2 is at the controls of the Starfighter taking care of the routine functions of flight, leaving Luke Skywalker free to focus on the strategy and more complex tasks at hand.

As you’re thinking about how predictive analytics and machine learning play into your work, consider these issues:

• Will hotels not using machine learning be at a disadvantage? Will our company fall behind in Revenue Management if we don’t adapt to future technologies, even if they are not perfect yet? There certainly needs to be compelling data to help us know the answer to this question.
• Is this rapidly evolving technology better than what we have today, and will it give us a better outcome at a reasonable return on investment?
• Testing and modeling are critical components for success when it comes to machine learning. There have to be latitudes given for iterations, learning, and adjustments. Some companies are spending a lot of resources looking at how to make the machines learn better and faster.
• Based on the learning curve, there are bound to be times when the machine gets it wrong, and even if it is only 1% of the time, the feeling is that executive management will focus on the missed opportunities and lost revenue for the exceptions rather than the 99% of the time that it got it right.
• You have to determine what these systems need the user to do. Users need to understand what the machine can do, and be clear as to what their roles and responsibilities are.

As this is an emerging area in Revenue Management and seems to be the direction many companies are taking, the question remains whether hotels can win without it.  There is great potential, but it is a lot of work.  The user needs to interact with the machine to make it better over time, but it is a process.  With the reams of data to be dealt with and time to gain confidence, the Revenue Manager can look at the bigger picture, like Luke Skywalker, rather than taking time with the mundane task of flight. 

Recommended Reading

• What is Prescriptive Analytics? A Conversation with UC Davis
• Machine Learning for Product Managers: A product-centric overview of machine learning (this article has some of the most practical and understandable definitions and examples the Advisory Board has seen, particularly for someone who wants to understand machine learning in a business context instead of a math modeling perspective)

About the Author

Maureen O’Hanlon is a 25-year veteran of the travel and hospitality industry. As a partner with the Prism Partnership, she leads marketing initiatives large and small. Her primary areas of expertise are loyalty programs, sales and reservation training, customer relationship management, digital marketing, sales and marketing audits, and destination marketing.

Prior to joining Prism, Maureen served with Prime Hospitality as SVP of Sales and Marketing overseeing all marketing, advertising and communications efforts in addition to the National Sales efforts and reservations department. She also served for 16 years at Carlson Companies in a variety of roles including EVP of Marketing and Sales for Radisson Hotels and SVP of the Loyalty Division for Carlson Marketing Group. 

Maureen is a past chair of HSMAI’s Americas Board of Directors as well as a past President of the HSMAI New York Chapter. She has received numerous industry honors including being recognized as one of the 25 most influential industry executives by Tour and Travel News.

About HSMAI’s Revenue Management Advisory Board

HSMAI’s Revenue Management Advisory Board is advancing the revenue management discipline by providing leading education, a best practices exchange, thought leadership, and networking for revenue management professionals, other sales and marketing professionals, and senior management in the hospitality industry. 2017 members include:

• CHAIR: Linda Gulrajani, CRME, Vice President, Revenue Strategy & Distribution, Marcus Hotels & Resorts
• Chris K. Anderson, Professor, Cornell University
• Veronica Andrews, CRME, Director of Active Data, STR
• Ravneet Bhandari, CEO, LodgIQ
• Christian Boerger, CRME, CHDM, Corporate Director of Revenue Strategy, Pacific Hospitality Group
• Denise Broussard, SVP, Revenue Management & eCommerce, Interstate Hotels & Resorts
• Rosemary Browning, President, Global Career Horizons
• Tom Buoy, CRME, EVP Pricing and Revenue Optimization, Extended Stay America
• Brian Burton, CHSE,CRME, Vice President Revenue Strategy & Optimization, White Lodging
• Janelle Cornett, Regional Director, Revenue Management, TPG Hotels and Resorts
• Kathleen Cullen, CRME, Senior Vice President Revenue & Distribution, Two Roads Hospitality
• Tammy Farley, President, Rainmaker
• Neal Fegan, CRME
• Monte Gardiner, Sr. Director, Revenue Management Services, Best Western Hotels & Resorts
• Renee Haddad, CRME, Director, Revenue Account Management, Preferred Hotels & Resorts
• Adam Hayashi, CRME, Vice President of Revenue Management, Accor Hotels
• Mohamed Khanat, CRME, Regional Director – Americas Account Management, IDeaS – A SAS COMPANY
• Kelly McGuire, VP Advanced Analytics, Global Analytics, Wyndham Destination Network
• Karen McWilliams, Vice President of Revenue Strategy, Concord Hospitality Enterprises
• Chris Nixon, CRME, AVP Revenue Optimization, Ashford Hospitality Trust
• Breffni Noone, Associate Professor, Pennsylvania State University
• Scott Pusillo, CRME, General Manager (NorthEast USA), Sales & Account Management , Sabre Hospitality Solutions
• Scott Roby, CRME, Vice President, Revenue Management, Evolution Hospitality
• Tim Schulte, CRME, Solution Consultant, Infor
• Jim A. Struna, CRME
• Tim Wiersma, Vice President, Revenue Management, Red Roof Inns, Inc.
• Monica Xuereb, Chief Revenue Officer, Loews Hotels & Resorts
• Nicole Young, CRME, Corporate Director of Revenue Management, Rosewood Hotel Group