One of my favourite movies of all time is Rain Man, in which Dustin Hoffman plays Raymond Babbit, an autistic savant whose ability to count hundreds of cards at once leads to significant wins at the Las Vegas casino tables. Fortunately for gambling and gaming companies, Raymond’s counting abilities extend far beyond the normal range of human subitising and, aside from the occasional winning streak, the vast majority of us will be net losers. But in this industry it’s not just the customer who needs the statistical insight at their fingertips in order to succeed, it’s the vendors. And this insight comes in the form of data, AI/ML and the correct cloud infrastructure in order to help vendors win as big as their customers.

On the surface of it, the odds are stacked in favour of the vendors. With 2 billion gamers across the world, and the current size of the global gambling market – almost $46 billion – forecast to double in the upcoming years, it’s clear that the ‘have to be in it to win it’ player mentality is proving lucrative for vendors. Each of the billion global players will participate in multiple actions and interactions, leaving a trail of data as they go, allowing vendors to act on this information to really understand their customers. But scratch beneath the surface and a mine of potential complexities unfold. How do they prevent the loss of high value customers ? How do they optimise marketing spend? Or predict & prevent problem playing/gambling?  How do they ensure they are identifying and developing the right products? Forecasting sales accurately? Predicting churn? Or preventing & detecting fraud?

When Mango begins a new engagement with companies operating in this sector, before any data science can take place, it’s crucial to ensure that the right questions are being asked and challenges being addressed.

Let’s take a look at some of those common challenges facing the gaming & gambling industry:

Preventing the loss of high value customers

Solving the issue of customer churn is one of the biggest challenges for any online business, but the effective use of data science can help by better forecasting when customers – and particularly high value customers – look likely to leave. The more accurately you can forecast churn, the more effective you can be at preventing customer loss. Using data science to segment the customer base by any attribute, such as age, location or date they joined, means approaches can be developed that are personalized – and therefore relevant – to that customer base. For example, analytics could show a list of customers who are approaching the end of their contract or detect less activity on an account than is ‘normal’ according to historical patterns, or perhaps that new, heavily featured games are not being played. In all of these instances, data driven decisions can be made on the most effective intervention tactics and appropriate incentives to retain these customers, such as loyalty points, or reduced price play.

Optimising marketing spend

Contrary to popular opinion, marketing funds are not bottomless pits, and adopting a ‘spray and pray’ approach will likely result in little, if any, return on investment. With customer data being captured with every online transaction, however, vendors can gather huge volumes of structured and unstructured data about each individual in order to offer targeted, personalised marketing. The key word here is ‘personalised.’ Just because 100 customers might fall into the same broad segment, it doesn’t mean they should be targeted in the same way. Individuals within these groups have individual preferences, and algorithms can help determine which communication or marketing channel would have the most impact with a particular customer and deliver the highest response rate, thus optimising marketing spend.

Maximising cross & upsell through great customer experience

Ensuring that customers are happy enables vendors to cross and upsell and data science can unlock insights which help win and retain customers. These insights can help online businesses ‘know their customer’ and therefore make tailored improvements to the customer experience and measure immediate impact. Did increased spend on customer retention contribute to increased revenue? If so, by how much? What is the impact of multiple offers and communications to customers on sales, unsubscribes and retention? Excellence in customer service can be achieved by adopting a data driven, 360 degree approach which offers a thorough understanding of the audience and a means to deliver the service desired at the right time and via the most appropriate channels.

Predicting & preventing problem gambling

As the gambling industry grows, so does the problem of gambling addiction. According to a recent BBC article, there are about 430,000 people experiencing problems with gambling and, as we all know, this can impact anyone. There is no typical ‘problem gambler’ – it’s an issue that transcends all social and demographic groups. The Gambling Commission recently launched its new three-year National Strategy which focuses on prevention, education, treatment and support for problem gamblers, which is a significant step in the right direction. And data science can help this process by identifying potential candidates for such support. Using data collected on the betting patterns of every customer, including the time of day, frequency and size of bets placed, a picture can be built up of an individual’s typical behavioural patterns, so that any gradual change or deviation from this pattern can signal the onset of a potential problem. At this point, the company can decide to apply intervention strategies, such as the temporary stop of an account, or refer the player for online help.

Predicting & preventing fraud 

In this sector, the list of potential pitfalls is sadly, long and sobering for customers and vendors alike. Frequent, often large volumes of credit card payments being made, free credit offered by companies as incentives to play encouraging fake accounts being created, stolen credit card details, accounts being hacked….I could go on. Fortunately, advanced analytics can be used to help create a picture of ‘normal’ account activity for individual players, and so flag any abnormalities at the earliest opportunity. With this early detection system in place, an effective monitoring programme can help protect the organisation and the individual.

The possibilities for data science to help your business win are far reaching and, if you’re wondering how you can find out more, I’m delighted to say that next Wednesday evening, I’ll be presenting alongside Rackspace and Google to share with attendees some of the possibilities. We’ll be showing how to build a successful data science capability on Google Cloud, aligning key challenges with the ‘Art of the Possible.’ By answering the right questions through advanced analytics, we can help you create predictive models, including churn, assessing demand and customer life time value to enable effective decision making.

Don’t leave your fortune to Lady Luck. Organisations that win with data science will do so by answering the best business questions, not creating the best answers to data questions.  With that in mind, I hope to see some of you next week!