In Gambling, Games Analytics

I’ve always thought that the industry was missing a trick, and that social responsibility should be about in-game player behaviour, particularly in e-gaming, where the data collected from games provides a real opportunity for measurement and live intervention.

If you can identify the types of behaviour that identify problem gamblers from high rollers, like repeated high-risk betting after a loss, burning through deposits really quickly, repeatedly leaving only when there is nothing left in the bank, coupled with high return frequencies, then you can identify when an individual is at high risk, and manage that risk, maybe before the player is even aware of it.

There’s plenty of incentive for operators to look at this. William Hill announced last week that they are losing 3,000 customers a day to self-exclusions and that each of them are, on average, four times the value of the average punter, with an expected annual impact of between £20m and 25m. If many of those players could have their behaviour moderated at an earlier stage, then they could still have a positive gambling experience in a more protected environment, without pressing the nuclear button of self-exclusion, which, as many will appreciate, is often implemented too late in the process.

It’s self-evident that some players need to be protected from themselves, and currently either the player has to recognise they have a problem and choose to stop against all instinct, or the operator has to stop providing them with a service, knowing that the player is likely to eventually go elsewhere. Neither of these approaches is ever going to be effective as the core behaviour remains unaddressed. The status quo is that players typically don’t self-identify and operators see little incentive in curtailing their most profitable customers ultimately in favour of their competitors, despite the protestations of the regulators. We need to move on, and ultimately, these are high value customers, so it’s got to be worth the time and effort to evaluate their game play and invest in strategies to better manage it for the long-term, and to build trust into your brand.

At-risk behavioural segmentation

While we know the players who self-exclude spend more on average, it’s really not the point when looking at individuals as incomes vary, so acting on deposit amounts on their own is a pretty blunt tool. Instead, there is an opportunity to look at how players interact with the game. We should use data to analyse the previous gameplay of players who self-exclude and ask questions, based on what we know of problem behaviour identifiers, such as: How do their stakes compare to their deposits? How frequently do they make deposits? Do they end each session with no credit? How often do they return? What times of day do they play? How long do they play for? And, do they consistently increase their stakes when they lose? By doing this, you can build segmentation profiles that can be used for targeting players live, as they play, and separating your “at-risk” players from your “high-rollers”.

Real-time intervention

By understanding the triggers that could indicate a player who either has a problem or who is likely to develop one, you have a real chance to engage with them to modify their behaviour, providing a graduated response. You can limit the size of stake that can be placed based on deposit amounts, offer players the opportunity to opt-in to time blockers, incentivise players to stop playing before they deplete their funds by offering free spins linked to remaining funds at the end of each day, or provide free goes after a player loses heavily, in order to prevent them immediately compounding the loss. From a purely financial perspective, you can afford to be patient and generous with these players while encouraging a more sensible approach to the way they play.

Like most things in life, it is about balance, both in defining the triggers and the interventions. Only really by testing the outcomes of your interventions on these defined player segments can you be sure that the approach you are using will be successful. If it’s done right, you should see reductions in the numbers of self-exclusions and long-term positive relationships with this group of players.

I’m convinced that a data-led behavioural segmentation and real-time intervention approach is in the best interests of the player, it enhances the brand of the operator, and just as importantly, it demonstrates a proactive, caring approach which will be appreciated by the regulators.


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