Casino operators can gain valuable player insight, and a potential new revenue source, by recognizing, utilizing and partnering with third-party customer data sources

Authors’ Note:  In the eighth of a 12 article series themed on where the money is now for “smart” casinos, VizExplorer executives discuss the merit of third-party data collection and how this information could be potentially monetized for a property’s benefit. Please note these articles are meant to stimulate thought and that we are using some deliberately provocative metaphors which should be taken with a grain of salt.

Before social media, it was possible for an operator to own nearly all the data associated with the casino-player relationship.

That was because most of this information originated from casino-centric systems such as player tracking, hotel management, point-of-sales (POS) and even valet parking services. In most cases, the software from which customer data was extracted ran on property and remained in the sole ownership of the property.

For better or worse, this is not the case today—casino customers now often interact with systems that are owned by third parties, which completely changes the picture. For example, let’s consider a sample customer called Kathy and her journey to and from a casino property. On the way to the casino, Kathy interacts with online mapping systems, social media (including yelp reviews), and even purchases show tickets through a third party booking agency.  At the property, Kathy withdraws money from an ATM before embarking on her gaming experience. In the course of this journey, Kathy has managed to leave her digital footprint in no less than five systems where the data does not belong to the casino. This creates a business data flow best represented by the following diagram:

The advent of third-party data collection can also impact other aspects of the casino enterprise, including player development and the retention of high-value customers. In the past, if a host stopped working at a casino, his or her players could be reassigned to alternate hosts and the relationship between the player and the property could continue. This is no longer a guarantee in today’s social media-dominated world.

Let’s consider the example of a host called Chris who moves from Casino Alpha to a competing property. In the past, the operator would strive to keep Chris from bringing her “black book” of players to her new job; forcing the host to essentially start from scratch. For Chris and other modern hosts, this situation has changed. For example, as soon as Chris accepts the new job, she makes a Facebook posting announcing her new role and starts directly communicating with her Facebook friends, which Alpha Casino can do little to stop. Meanwhile, players feel like it is business as usual as they have been communicating with Chris on social media for years. Chris might even have a private party and invite her old players to the new property to experience a special event accompanied with a special signup offer.

This again illustrates the impact non casino-owned data streams can have on a property. Quite simply, a casino that thinks its proprietary data systems are all it needs to be concerned with will not only be behind today, they will fall further and further behind their competition in the years to come. So it would behoove operators to better understand the sources of third-party big data collection within its own facilities and how operators may be able to take advantage of it.

STRANGE NEW DATA WORLD

It is predicted that there will be a mind-blowing 44 trillion gigabytes of “big data” by 2020, and that more and more of this information will be collected in innocuous places and “owned” by dispersed parties. With these two aspects in mind, let’s hone in on the casino enterprise and discuss offbeat places where data may accumulate, its relative value and how it can help operators grow business.

ATMs—Data from ATMs is a core component of most cash-side analytics at properties. This data can give deep insight into on-property wallet information (how much money did the customer start with, leave with and have at various points during their trip). This info can also potentially provide wider insight into gaming behavior.

Security cameras—Cameras have become inexpensive, more effective and, thanks to image recognition technology, a growing and reliable source of customer information. Properties can now monitor customer movement, improve security and understand how groups of people move around the property.

CO² sensors—It may seem strange, but some hotels can count the number of people in a room based on the carbon dioxide output being read by sensors inside each room.

Uber costs—Prices for Uber trips vary by time of day. Such information would be valuable to a data feed modeling the “true cost” of a customer visit to a property.

Twitter—Twitter feeds can be quite enormous, and the impact from this data flow can be significant since a wide range of media organizations use it to measure public response.

Traffic monitors—Traffic monitoring information shows the wait time customers experienced visiting a property. Such data can be used to create real-time offers. For example, if there are substantial traffic delays in the region, offer a late dinner option to customers as they walk in the door.

Messaging—Engaging customers via e-mail, text and other messaging platforms creates a set of data that can be leveraged to craft future interactions between the casino and the customer. Properties can then improve communications with customers through various means ranging from real-time alerts for front-line employees to Chatbots.

Mobile usage—Mobile operators have detailed data on the mobile usage of customers. Of course, personal information will not be shared, but grouping customers based on call details may gain greater understanding into patron connectivity.

Retail stores—Retail data is collected at the point of sale inside our casino. This data is often anonymous, as credit card transactional information is protected by law. But sophisticated data matching algorithms can help us tie purchases to tracked customers and provide a much more detailed view of the customer.  Some operators find this data so valuable they are willing to offer retail discounts in exchange for a tracked player presenting their Rewards Card at the time of purchase.

Casino data—In an industry known for its data secrecy this may seem out of place, but consider monetizing gaming databases to provide information flows to complementary businesses or even monetizing the data flows between jurisdictions. This can be done in a way that maintains customer anonymity while leveraging the value of this data to other businesses, simultaneously protecting the important detailed data that needs to remain secret and proprietary to a property.

Electrical usage—Power usage can provide a surprisingly large amount of information on a customer, since every activity has its own unique electrical signature. It can be used to identify trends from unrated customers or untracked activity emanating from all customers.

SHOW ME THE MONEY

While casino operators are accustomed to owning all the data in the business, many companies these days are looking to databases as a separate revenue stream.

These revenue streams place operators in a fascinating place where the data ecosystem is now, by its nature, a multi-organizational linkage and the entities that do not stream these external datasets will be figuratively driving blind in a snowstorm while the competition has radar and a heads-up display. Indeed, of the 11 data streams we just described as being present in casinos, most gaming operators are only paying attention to one or two at the most. Ideally, casinos should be trying to create a canonical data ecosystem (see Diagram 2) that captures all these streams and provides us the much needed navigate for the future.

In this world of data sharing, there is value in gaming info and operators are now presented with new opportunities to monetize existing data streams, which can occur in partnership with the entities such as manufacturers, airlines and retailers, just to name a few.

• Monetizing data streams with collaboration with a manufacturer is a powerful tool. Consider the example of a new manufacturer who is looking to expand its product offering, say a video reel manufacturer adding mechanical reels. In the world of gaming, there
• is a huge difference between video and reel products; an opportunistic operator could partner with the manufacturer throughout the lifecycle of its new game to understand how it fits into the competitive landscape at a detailed level.
• Collaboration with airlines has been done before in gaming with some success. In the world of data sharing, a deeper kind of collaboration is possible, let’s call it integrated database marketing approach. The airline needs flexibility in driving yield for specific dates and times. The casino needs to be able to offer special marketing products. Now imagine a last minute offer to a customer promising a package deal where the airline makes the pitch to its customers based on a sophisticated data sharing agreement.
• Retailers would love to know which customers are avid gamers, and would then be able to communicate differently with them based on their gaming outcomes. Imagine a retailer learning that one of its customers just hit a huge jackpot, and that the data science indicates this person is likely to go out and make a big retail purchase. These retailers can then leverage that information to drive specially-timed promotions to this shared customer. Conversely, a casino can benefit greatly from knowing a player’s retail spending habits. Perhaps analytics reveal that a casino customer who makes a clothing retail purchase is more likely to visit a casino that weekend. The casino can then create marketing programs to target these likely gamers.

But how would such a data share arrangement between different businesses work? A level of data integration will have to be created between interested organizations, involving business, marketing, timeliness and partnership components.

Business—Within this level, key elements of the data are provided to the operators in an anonymized form such as a market view of gaming machine penetration or a summary of market performance based on market surveys.

Marketing—This is the level where the actual marketing to customers now incorporates external data feeds, and social media strategy can be tied into database marketing initiatives. The data from the strategies rolls into analytics that can be examined in a daily fashion.

Timeliness—In the real-time integration model, the full external data feeds are flowing into the gaming operation and assisting with all real-time decision making. For example, real-time ATM transaction volumes are used to predict future table game play levels or in the min/max math models to determine the minimum bet structure across table games.

Partnership—In this model, there is a real partnership with the data provider and the data owners work together to build a combined competitive advantage.

IN SUMMATION…

The gaming industry is known for pushing the envelope when it comes to analytics and has always been data rich. The next step in the industry’s evolution is to realize the need and value of non-owned external sources of consumer information and stream them into analytics systems. Such an integration has the added benefit of being a substantial opportunity for revenue growth.