Latest Viewpoint

The recent 12th Annual Tribal Casino Executive Exchange in Carlsbad, California, US, was an enormous success, bringing together tribal gaming leaders to explore the impressive collection of emerging technologies that are already transforming casino operations.

While the conference considered the benefits of facial recognition, innovations in responsible gaming and cutting-edge digital engagement strategies, one technology emerged as a game-changer for casino marketing: Causal AI, a form of artificial intelligence designed to identify and understand the cause and effect of relationships across data.

Traditional casino analytics have long relied on predictive models that identify correlations or patterns and relationships in player behaviour. These systems excel at answering standard “who-what-when-where” questions, such as who is visiting the casino, what games are they playing (and when), which promotions are they redeeming and what other services are they patronising? Traditional analytics do a wonderful job of identifying correlations that are often impossible to spot otherwise.

However, as I explained during my conference presentation, correlation doesn't equal causation – a distinction that is costing casinos millions in wasted marketing spend. Traditional casino analytics can tell you a lot about relationships in player data, but what they can’t tell you is the nature of those relationships and why they exist.

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