How deep data and sophisticated AI is helping operators optimise

The sports betting industry continues to grow at pace as new markets open and regulate for the pastime, leading to increased consumption of betting products and services.

The 24/7 nature of live sport sees millions of bets placed every minute of every day globally. This high volume of betting activity has created an unprecedented depth of data that operators can use to enhance the performance of their businesses and better understand how customers engage with their respective products and services.

Behavioural, trading, operational and customer data has become an integral part of an operator’s playbook as they seek to gain an advantage in a hyper-competitive marketplace. Of course, using data to better understand customer behaviour is nothing new – it’s something marketers across all industries have been doing for years.

What has changed though, is the sheer volume of data that operators now have to work with and the increasing capability to interact with that data in real time. By way of example, consider that Sportradar profiles in excess of 1.5+ million unique betting accounts per day, or more than 15 million unique active users per month. It’s fundamentally unrealistic to comb through that depth of data manually and glean any meaningful insights. And, if someone did, they’d likely only be able to process, say, 10% of those accounts at best.

In this age of ‘big data’, a term that refers to data sets that are too large or complex to be dealt with by traditional data-processing methods, operators need a technology that’s capable of processing high volumes of data quickly and efficiently. This is where AI comes in. 

AI comes to town

Increasingly, operators are using AI and machine learning technologies to analyse this deep data and extract the insights that have benefit to their businesses. The practice of data-driven decision making is becoming more common as operators turn to facts, metrics and data to guide strategic business decisions that align with company goals and objectives.

While there’s potential for use of AI across all aspects of an operator’s business, one area of great promise is risk management. The AI open to operators enables them to process an enhanced level of information, allowing them to examine the risk associated with a specific player account bet or sports event and thereby limiting their financial exposure. For example, AI-driven automated player profiling solutions analyse customer data to generate an accurate profile of a bettor’s behaviour and determine how much of a risk they are to the bookmaker.

While personal data is not known, it’s possible to identify the risk an individual poses to an operator’s business from their betting habits. It’s this insight that an operator can use to determine the type of bets they offer to groups of customers. For example, some customers will be offered restricted bets, while trusted or VIP players will be offered larger bets. Those customers identified as ‘low risk’ receive significantly reduced live time bet delays, enhancing the user experience with a super-fast betting experience. This takes place in real time, depending on the context, and positively affects the player experience without compromising on risk control and profitability.

With AI-driven risk management tools operators can go into the most granular detail to achieve the desired ‘risk/return’ profile, therefore limiting lower-quality business while enhancing high quality with as few constraints as possible.

Make it personal

In addition to using AI to protect their businesses, operators are using the technology to engage their customers more deeply with a heightened personalised betting experience. AI-driven personalisation – the customising of a content offering based on an individual player’s interests and preferences – is very much the next evolution within our industry as operators look to innovate the player experience and differentiate their customer offering to remain competitive.

AI models process data to detect patterns in player behaviour and use that information to distribute messages, bonuses and bets to customers more effectively and efficiently than could otherwise be achieved, with a much better return on the marketing spend. We’ve seen this have a big impact on customer retention, and by creating an offering tailored to the interests of individual customers, operators can use the technology to reduce the number of customers at risk of churn.

In fact, retention case studies have shown us that personalising the user experience increases dwelling time and the likelihood of a customer returning. This is reinforced by consumer psychology, which argues that customers are creatures of habit – if they are happy and getting what they want from the sportsbook, they have less reason to look elsewhere.

By providing the benefits that personalisation offers, both the likelihood of satisfaction and the rate of retention are likely to rise. This allows for more sustainable business growth and fewer costs associated with trying to win customers back, such as through return bonuses.

Furthermore, AI-driven personalisation extends the bettor’s discovery process. By using a deep learning technology, operators can identify a user’s next best bet, make market recommendations and highlight similar events to those that the user has recently bet on. Using AI in this way increases retention and brand loyalty by showing players content that they want to see before they even know it exists.

In our hyper-competitive marketplace, the combination of data and AI has become extremely powerful, empowering operators to enhance business performance and engage customers more deeply. These two aspects are now critical for any sportsbook, now and in the future.