Data Science in Gaming and Hospitality

Opportunities in Hospitality Data Science

The Promise of Big Data
Today, the hospitality and gaming industry has lots of data to work with. This includes both:

  • Traditional structured data (e.g., transaction records, website visits)
  • Unstructured data (e.g., social media streams, guest comment cards, video feeds, etc.).

Thanks to inexpensive storage, mobile technologies, powerful processing and other innovations, hotels and casinos now have access to more information. Insights from big data are typically used to anticipate consumer demand, labor and HR management, and operations.

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Real-Time Revenue Management

Data scientists have the ability to tap into a constant flow of real-time pricing data and adjust their offers accordingly.

Take Lisa Terry’s story of Marketspan. To help Kees Hospitality compete with global hotel brands, the revenue management provider LeisureLink created a cloud-based application to handle revenue management, merchandising and electronic distribution.

The partnership between Denihan Hospitality Group and IBM is a similar success story. In addition to filling rooms during non-peak seasons, Denihan was looking to boost revenue at popular times. IBM’s daily reports enabled the hotel’s team to:

  • Predict the most beneficial type of business to book at a given time
  • Understand how far in advance it would do so
  • Estimate what the room-rate trend would be, which booking channel would be used, and the length of the stay

IBM claims their tools created a boost in productivity for revenue management teams and allowed one of Denihan’s New York City hotels to perform at double the room rate during United Nations Assembly Week.

Satisfaction Guaranteed

Customer satisfaction is important in the hospitality and gaming industry. Hospitality data scientists look at a wide variety of customer touch points – website tracking, line item purchase details, dietary preferences, room temperature settings, guest surveys, mobile apps, social media comments – to create a 360-degree view of patrons.

Fraud Detection

Casinos also use big data to keep undesirables out. You remember the story of Bill Kaplan and the MIT card-counting team in the late 20th century?

They were partially brought down by Non-Obvious Relationship Awareness (NORA), data matching software that determines connections (e.g., a prior relationship between croupier and player) that aren’t immediately apparent.

Combine this kind of technology with facial recognition data, video analysis, on-the-spot background checks, RFID transmitters in casino chips and you have a recipe for constant surveillance.

SoLoMo Marketing

Let’s say a hotel has done its homework and is making guests happy. How can it best attract new ones?

Marketers use multi-touch attribution and the convergence of social, local and mobile (SoLoMo) data.

Twitter comments, geo-location data, mobile apps that monitor your daily behavior – all of these can be combined with offline data to create a detailed profile of your behavior and preferences.

Hospitality and gaming marketers then have the opportunity to supply personalized offers, promotions and services through the channels you prefer. For example, the mobile app Hotel Tonight checks centralized reservation records against a customer’s geo-location data and supplies last-minute booking offers.

Marketers are also using customer data and feedback to predict the value of returning guests. Denihan, for instance, provides customized offers. Patrons who spend more (e.g., tens of thousands) receive a different deal than those who spend less (e.g., thousands).

Beating the Odds

Data scientists have realized that big data can help online gamblers beat some rapidly changing odds.

For example:

  • Claiming to be the largest online poker tournament database in the world, the poker stats firm SharkScope monitors millions of games and players per day. Those who use SharkScope can track their own statistics while avoiding the poker “sharks” who might gobble up their cash.
  • The online startup Betegy is employing big data algorithms to calculate the outcome of soccer matches.

In this realm, speed is paramount. SharkScope uses a MySQL database from TokuDB that is capable of producing insights for a variety of requests in just under two seconds.

Data Risks and Regulations

The Challenges Ahead

Hospitality and gaming companies face challenges in their push to become data-driven organizations.

For one, some may grapple with outdated relational databases and with software that can’t handle large volumes of both structured and unstructured data.

For another, there’s a worldwide shortage in data analysts. Business intelligence and CRM systems are well and good, but you also need experts who know how to extract valuable information from them. The hospitality industry could have a tendency to be on the bottom of the job wish list for data scientists. Finance, biotechnology, research and manufacturing tend to garner their attention first.

Excuse Me, That’s Private

The hospitality and gaming industry must confront another issue: individual privacy

The massive online gaming industry, for example, typically collects sensitive personal information, including data on behavioral patterns and personal finances. How is this data being stored? How is it being protected?

Hotels and casinos are equally at risk. The more they collect, store and share data (e.g., with third-party business intelligence companies), the more they expose themselves to security breaches and abuse of information.

Global hospitality companies are required to comply with a dizzying array of data privacy regulations. The E.U., in particular, has recently enacted strict privacy rules.

Last but far from least is the issue of customer blowback. While some guests may appreciate the “personal” touches made possible by big-data algorithms, others are less impressed.

History of Data Analysis, Gaming and Hospitality

The hospitality industry – that hodgepodge of lodging, restaurants, event planning, theme parks, casinos and cruises – collects lots of data! Check out a few examples below

The First Automated Booking System

Travel is to hospitality like Sinatra is to swing. You can’t have one without the other.

In the mid-1940s, American Airlines tackled the problem of booking flights with an experimental automated booking system called Reservisor. It was successful enough to interest Sheraton Hotels, who simply substituted hotel rooms for airline seats.

But it wasn’t perfect. For one thing, it was impossible for individual ticketing agents to directly query the system. Starting in 1953, Trans-Canada Airlines (TCA) began developing a reservation system with remote terminals. In 1962, renamed ReserVec, the new system was ready, complete with a transistorized computer. In 1963, it was in every TCA ticketing office. Queries and bookings could be completed in one second – no remote operators required.


The U.S. took its own path. A chance 1953 meeting on a flight from Los Angeles to New York brought two Smiths together. C.R. Smith, the CEO of American Airlines, asked R. Blair Smith, a senior IBM sales representative, if something couldn’t be done about American’s clunky Reservisor system. A new project was born.

The result of the IBM/American partnership was the Semi-Automatic Business Research Environment (SABRE). Sabre had the power to create and manage airline seat reservations and make this data available to any agent at any location instantly. Completed in 1964, it was the largest private, real-time data processing system in the world.

Automated Hotel Bookings

Success in the airline industry prompted hotels to take a closer look at computers. As Jennifer Riesselman notes in her history of electronic booking:

“In the late 1960s, hotels began to see the opportunities that electronic distribution provided and started to develop their own systems based on the airlines’ frameworks. Many hotel brands adopted hotel identifiers based on airline city codes. Some of those identifiers still are in use today.”

Holiday Inn, Marriott and Westin were just a few of the hoteliers that dipped their toes in the data of automated hotel bookings.

Marriott’s Big Data Explosion

In the 1980s, desktop computers and hotel reservation systems appeared at the front desk. Travel agents learned to put down the phone and peck at the keyboard to make hotel reservations. In 1983, Westin became the first major hotel company to offer reservations and checkout with major credit cards.

But it was another chance meeting – this time between Robert Crandall, current Chairman and CEO of American Airlines, and J.W. “Bill” Marriott – that changed data history forever.

Crandall had been spearheading an effort in Yield Management, maximizing airline revenue through analytics-based inventory control. Marriott saw that Crandall’s big-data system was also a good fit for hospitality, where advance bookings, heated competition and wide swings in supply and demand were always an issue.

So Marriott came up with a Revenue Management System. This data-driven system helped Marriott create a daily forecast of demand, offer targeted discounts based on that demand and, eventually, forecast guest booking patterns and optimize room availability.

“Revenue management is a methodology to maximize an enterprise’s total revenue by selling the right product to the right customer at the right price at the right time through the right channel.”

This data-driven system helped Marriott create a daily forecast of demand, offer targeted discounts based on that demand and, eventually, forecast guest booking patterns and optimize room availability.

Travelocity Hits Warp Speed

With the arrival of the Internet, attention shifted from company to customer. There had already been experiments in online access – as early as 1985, Sabre had introduced easySabre®, which allowed customers with personal computers to tap into Sabre’s system of airline, hotel and car reservations – but it was on March 12, 1996 that Travelocity was born.

Travelocity was one of the first websites that put bookings directly into the hands of the end-user. Now there was no need for a travel agent – customers could access Sabre’s fare and schedule information and make reservations for hotels, rental cars, cruises and packaged vacations in a New York minute.

In the process, of course, web administrators could collect large volumes of valuable data on customer behavior. In that same year, Travelocity was joined by Expedia and branded hotel websites.

As the industry crept into the new millennium…

  • Valuable search tools appeared (e.g., searching for flights based on fare.
  • Reservation systems became increasingly efficient.
  • Customer loyalty programs fed directly into giant databases.

In 1998, Priceline launched it’s “Name your own price” model. Six years later, TripAdvisor provided an online forum for hotel reviews and recommendations. The tide of data was rising. Information was everywhere.

Casinos Invest in Data

Everywhere, yes, but not necessarily understood. Enter Gary Loveman.

In 1998, Loveman became COO of Harrah’s License Company. Harrah’s was a player – it was the first casino company to be listed on the New York Stock Exchange – but not a high roller.

  • It lacked the ready cash to invest in hotels and shows.
  • Customers were generally older folks with small incomes.
  • Loyalty cardholders often gambled elsewhere.

So, as Effy Oz points out in his book, Management Information Systems, Loveman invested in data instead. He threw his resources into data warehousing, business intelligence systems and data analysts.

Through a series of experiments, analysts discovered:

  • Gift shop discounts weren’t appealing, but deals on hotel rooms were – so deals were created.
  • Customers who lived nearby valued chips more than any other incentive – so casino chips were offered to local patrons.
  • Customers who enjoyed their visit in one year spent more in the next – so employee incentives were set based on customer satisfaction.

Last updated: July 2020