Gamblitude predictive models
Recognise VIP potential in the
first days after deposit.
Give your team an earlier signal of which new players may meet your high-value criteria. Use predictive scores to focus onboarding and VIP review while the relationship is still taking shape.
01 / What the model tells you
Early VIP Detection estimates the probability that a new player will meet a defined high-value criterion within an agreed period. The target and scoring window are set around your player population and VIP programme.
It adds a forward-looking signal to the activity you already see, helping your team decide who deserves a closer review.
02 / From early behaviour to a signal
The model learns patterns across a broad set of player attributes selected from the feature store. This sportsbook example shows how early signals, the scoring point and the prediction target fit together.
The observation window
Behavioural, transactional and contextual data form a broad feature store. Each model uses a selection of attributes suited to the operator’s data and prediction target.
Example attribute families
The scoring point
Trained on the operator’s historical data, the model combines selected attributes into a probability score. In this configuration, new players are scored once on day three after their first deposit.
The prediction target
The target can use your historical VIP labels. If these are unavailable, Gamblitude can help define them: for example, the top 5% of players by an agreed value criterion 60 days after their first deposit. Compare predictions with those later outcomes.
Example configuration, not a performance result. The scoring window and value criterion can differ between operators.
03 / Built for the people who act on it
A prediction becomes useful when it informs a decision. Give VIP, CRM and acquisition teams a signal they can use alongside their existing player context.
VIP teams
Prioritise new players with predicted VIP potential. Apply your programme’s eligibility requirements and player protection checks before deciding on the next step.
CRM & onboarding
Identify candidates for more attentive onboarding. Your team decides the appropriate service and communication as the relationship develops.
Acquisition
Compare the share of players with predicted VIP potential across acquisition sources. Follow their later outcomes to assess whether that potential materialises.
04 / A broad feature store
Gamblitude’s feature store brings together attributes describing how players arrive, engage, transact and use your products. Machine learning draws on this breadth to identify combinations of signals associated with future value.
The attribute families below show the range available for model development. The selection used in each model depends on your data, target and validation results.
Session frequency and duration, return patterns and changes in engagement over time.
Product mix, content preferences and the way players engage across the available offering.
Channel, acquisition source and cohort context add another dimension to observed behaviour.
Deposit and withdrawal behaviour, transaction frequency and patterns across the player lifecycle.
GGR, turnover and wagering behaviour provide context on the scale and shape of early activity.
Player tenure, activity timing, recency and evolving patterns across observation periods.
05 / Individually built for every operator
Every operator has a different player base, market and product mix. Each Gamblitude machine learning model is built individually and trained on that operator’s historical data.
Early VIP Detection learns the early patterns associated with your historical VIPs, using your existing VIP labels. Feature selection, training and evaluation are shaped around your operation.
If you do not have VIP labels, Gamblitude can help define them, such as the top 5% of players by an agreed value criterion 60 days after their first deposit.
Learn from your customers’ behaviour and how their value develops over time.
Build around the context in which your players engage, from acquisition channels to the products they use.
Train towards your agreed target and evaluate predictions against subsequent outcomes in your own player population.
06 / A practical workflow
Agree what your team needs to know, make the signal available and measure whether it improves the selection.
Agree the high-value criterion and observation window. Select and evaluate features using your historical player data.
Use the prediction as a player Attribute and create candidate Segments around your review process.
Assess the candidates. Compare selections with later outcomes and your existing rules at a similar review capacity.
The practical details
The output, the data and how it fits your operation.
The scoring window depends on the configuration. One sportsbook setup scores players on day three after their first deposit. The choice balances how early your team needs the signal with the evidence available for a useful prediction.
Each Gamblitude model is built individually on the operator’s historical data. Player populations, markets and products differ, so feature selection, training and evaluation are tailored to that operation. For Early VIP Detection, this also includes agreeing the VIP criterion and observation window.
Yes. The model learns from your historical VIPs using your existing VIP labels. If you do not have them, Gamblitude can help define a target, such as the top 5% of players by an agreed value criterion 60 days after their first deposit. The definition and outcome period are established before training and evaluation.
Early VIP Detection estimates the probability of meeting a high-value criterion. A revenue model such as Player GGR Prediction estimates an amount over a defined forecast period. Explore the Predictive Models overview to see how the models relate.
The score identifies candidates for review. Your team applies the programme’s eligibility requirements and player protection checks before deciding what happens next.
Predictions can be used as player Attributes, combined into Segments and analysed in Gamblitude dashboards. These outputs can support CRM and retention teams as well as VIP review.
We assess the available historical data across sessions, product usage, channels, acquisition sources, transactions and other relevant attribute families. We also need enough later history to establish which players met the agreed target. The final feature set and amount of history needed depend on your data and player population.
Early VIP Detection by Gamblitude
Bring your VIP criteria and the point at which your team reviews new players.
Let’s discuss where an earlier signal could help.