Lose Big at the Casino? Big Data Helps Draw Customers Back to the Casino after Losses

Casinos specialise in customer service, they seek to provide the best environment and experience for you to spend money on tables, slot machines, entertainment and hotels. To provide the best customer services requires differentiation of service. This commonly relies on Parento’s rule where, “for many events, roughly 80% of the effects come from 20% of the causes” (Wikipedia 2013). Meaning it pays to look after the highest value customers.

The journey of differentiation of customer service started with the collection of data through customer loyalty programs. These programs have been around since the 1990s, and collect data on preferences, personal data and importantly payment transactions. Big data goes a step further to combine customer loyalty data with hotel booking data, restaurant bookings and even CCTV footage on the preferences and choices of bets. 

Caesars Entertainment is leading the curve in the adoption of these big data practices to provide tailored services to different customer groups. Tariq Shaubkat, Chief Marketing Officer of Caesars Entertainment explains in this short video how if you lose too quickly, Caesars will often intervene to offer to buy you dinner or tickets to a Celine Dion concert in order to improve your customer experience and stop customers defecting to a competitor’s casino.

Interestingly, the Flamingo Hotel (part of Ceasars Entertainment) employs 200 Data Scientists alone to analyse, manage and make decisions about the insights! This hot new job title coined by DJ Patil and Jeff Hammerbacher, is part of a growing profession in the field of big data. 

So next time you are at the casino, look out, as big  data ‘brother’ will be watching your every move.

Big Data vs Business Analytics?

Big Data can change your world and we aim to tell you how over the next few weeks.

But before we get into that, lets take a step back and understand what the terms surrounding Big Data really mean.

Big Data, Business Analytics, and Business Intelligence are terms that are generally used to refer to processing data.

But is ‘big data’ just another way of saying ‘business analytics’?

The answer is an emphatic NO. 

Wikipedia defines Business Analytics as the “discovery and communication of meaningful patterns in data”. Business analytics has been in use since the late 1960s, when computer based information systems were used to support decision-making activities. Firms commonly apply analytics to business data, to describe, predict, and improve business performance.


For instance, Banks have long been using business analytics to differentiate customers by characteristics such as credit risk and usage. Similarly, Financial services firms have been using analytics to identify future growth trends on the basis of historic data.

Business Analytics needs data to identify trends. But the traditional business analytics software struggles with Big Data. Its tools were not designed to sift through massive data warehouses storing enormous data in a variety of different formats. Millions of dollars and enormous amounts of time and energy have been spent to develop these software programs, but it is still a Herculean task to identify meaningful data and ignore the useless bits.

The term Big data is self explanatory – it is literally data that is too big to be handled by conventional data management softwares. According to Edd Dumbill of O’Reilly media, Big Data is defined as “data that exceeds the processing capacity of conventional data processing systems. The data is too big, moves too fast, and doesn’t fit the structure of conventional database architectures. To gain value form this data, you must choose an alternative way to process it.”

In the coming posts we will start digging deep into the big data universe and explore other aspects of the big data landscape…

Stay tuned!