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AI automated decision making allows businesses or companies to make faster, accurate, and consistent decisions by capitalizing on datasets with AI. Artificial intelligence can analyze large datasets without error. This helps business teams to focus better on work relevant to their field.
1 Churn prediction
Churn prediction models in iGaming leverage machine learning to identify players at risk of leaving the platform.
Through analysing player behaviour, transaction history, and engagement patterns, these models can predict which players will churn. Based on this insight, operators can intervene proactively with targeted strategies aimed at keeping the players.
Player churn prevention. Early identification of potential churners enables the deployment of personalised messages offering bonuses, free spins, or other incentives. Tailoring these offers to individual player preferences maximises their effectiveness in preventing churn.
Preventing early life player churn. Focusing on players who show signs of disengagement within their first 30 days is really important in iGaming. Operators can improve early engagement and long-term retention by targeting these players with customised campaigns highlighting the platform's value and entertainment potential.
Preventing high-value players churn. High-value players contribute disproportionately to revenue, making their retention a priority for operators. Combining churn prediction with lifetime value (LTV) prediction models identifies these valuable players before they disengage. Then, offering them exclusive rewards and personalised attention ensures they feel valued.
Custom engagement strategies. Beyond standard retention tactics, understanding the specific reasons behind each player's risk of churn enables the creation of highly customised engagement strategies.
Dynamic retention offers. Machine learning models can continuously update churn predictions based on new data so that the operators can refine and adjust their retention offers in real-time.
2 LTV prediction model
LTV (Lifetime Value) prediction models in iGaming utilise advanced analytics to forecast the future value of players to the business.
The models provide important insights for strategic decision-making by estimating how long players will remain active, the number of bets they will place, and the total amount they will wager. This information can be used by operators to tailor bonuses, messages, and rewards to each player's predicted value.
Tailoring bonus amounts. Operators can assign bonuses proportional to the LTV of each player by predicting their LTV. As a result, the high-value players receive rewards that reflect their importance, which encourages them to continue to engage and stay loyal to the operator.
Forecasting player activity. LTV models can predict the likelihood of player activity over various time frames, such as the next 7, 30, 60, 90, or 365 days. Operators can use this insight to identify inactive players and target them with re-engagement strategies.
Predicting betting behaviour. Understanding the expected number of bets from players within specific time frames allows operators to plan for game availability, customer support, and liquidity management.
Revenue forecasting. LTV prediction models enable more accurate revenue forecasting by estimating the total spending of customers over different periods.
Customised player engagement. Insights from LTV predictions allow for highly personalised communication strategies. Operators can craft messages and offers that resonate with individual player's behaviours and preferences.
3 Dynamic RFM segmentation
RFM segmentation uses real-time data about recent purchase behaviour, transaction frequency, and overall spending to automatically divide the customer base. In this manner, operators can understand their players on a deeper level, categorising them based on their engagement and value. This dynamic insight facilitates stronger, more personalised relationships with customers and increases their loyalty.
Personalised marketing campaigns. Leverage dynamic RFM segmentation to deliver marketing messages that resonate with each player segment's unique characteristics. Tailoring offers based on recent behaviour and spending ensures higher engagement and conversion rates.
Optimised loyalty programs. Design loyalty programs that reward players based on their RFM segment. Players with higher frequency and monetary value can receive more substantial rewards, incentivising continued engagement and spending.
Enhanced player experience. Use RFM insights to customise the gaming experience for different segments. For example, high-value players might get access to exclusive games or early releases, while frequent players could receive bonuses for their loyalty.
Strategic resource allocation. Operators can allocate resources more efficiently by understanding which segments contribute most to revenue. Focus development efforts on features that appeal to the most lucrative segments or prioritise customer support.
Real-time segmentation for real-time engagement. The dynamic nature of RFM segmentation allows operators to adjust their strategies in real time, responding to shifts in player behaviour with agility. As a result, marketing efforts are always aligned with the current state of play.
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