Successful CRM strategies are no longer only driven by revenue and cross-sell capabilities but also by the need to develop healthy and enjoyable gaming experiences that foster long-lasting player relationships.
As operators shift their focus away from promotion-based marketing, you must have the right tools to identify players who are predicted to become at-risk.
Here’s how your team can build a predictive model for at-risk players.
Define At-Risk Players
To define an at-risk player, you can explore historical data and develop a definition based on trends. For instance, an Optimove gaming operator-defined their at-risk players by assigning a weighted score, from 1 to 10, to several player activities, such as time spent on site and bonus usage. The higher the weighted average, the more at-risk the player is.
Understand the Data
The more data available for a machine learning algorithm, the more accurate the results. However, when it comes to at-risk players, the number of players who become at-risk is typically a low percentage of the database. To learn more about balancing the data set for an accurate machine learning algorithm, click here.
Choose the Variables
Once the dataset is balanced, the attribute selection process can begin. Choosing the right variables is crucial for the accuracy and success of the prediction model.
By clustering players into groups based on the gross amount or trendline slope of daily bets made, you can separate players into two distinct groups – those predicted to become at risk and those who have a low likelihood.
Create the Model and Analyze its Results
Once the attributes and variables have been selected, the machine learning algorithm will run and identify players who are predicted to become at-risk. This model is self-optimizing, allowing for changes in player preferences and industry trends to influence the model creation.
Utilize the At-Risk Predictive Model
To adopt more socially responsible gaming marketing strategies, you can segment players into three tiers – low, medium, and high, based on their likelihood to become at-risk.
Players with a low-risk level can be given the occasional promotional campaign. In contrast, players in the medium risk level can receive 30% of the promotional campaigns as the low-risk group received, and players from the high-risk level can receive only informative and educational campaigns.
Using a bespoke machine learning algorithm can empower gaming operators to better understand their player base, their trends, and the behaviors players exhibit before becoming at-risk. These insights can optimize marketing strategies and improve long-term player retention.
Gabriella Laster is Product Marketing Manager at Optimove, responsible for prospect marketing and product messaging. Gabriella holds a B.A. in International Relations and English Literature and an Executive MBA from Hebrew University.
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