Gamblitude predictive models
Built around your players. Focused on your decisions.
Use predictions and recommendations to inform player value, retention, content and protection decisions. Explore six applications, each shaped around your data and the question your team needs to answer.
Six applications
Choose the signal or recommendation that fits your team’s next decision.
Player value
Identify new players who may meet your high-value criteria. Use an early probability score alongside player context to focus onboarding and VIP review while the relationship is developing.
Player value
Estimate the sportsbook GGR an eligible player is expected to generate over the next 30 days. Compare future revenue potential with recent performance to support review and segmentation.
Assess the risk of disengagement against an agreed outcome. Bring the current score, changes over time and player activity together so your retention team can focus its next review.
Shape reactivation bonus amounts around an audience your team has already qualified. Use available player and offer history to develop recommendations, then test meaningful reactivation and the result after costs.
Rank available sportsbook events or casino games around player interests and a defined product placement. Scope event discovery, game selection or lobby ordering with your product and data teams.
Bring selected behavioural indicators and player history together to support professional RG review. Define the assessment and priorities around player-protection needs, independently of commercial value or promotional targeting.
RG Risk Detection, Bonus Value Optimisation and Content Recommendations are individually scoped implementations; we agree the approach, deliverables and integration with your team.
Built for your operation
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. The target, feature selection and evaluation follow the decision you need to support. Gamblitude’s feature store brings together behavioural, transactional and contextual attributes to support model development.
A broad feature store
These are broad data families available for assessment. The inputs used in each model depend on their relevance, quality and availability.
Explore the data foundationLearn from the behaviour and outcomes in your own player population.
Account for the context in which your players arrive, engage and use the offering.
Agree what the model should estimate or recommend and how its usefulness will be assessed.
From output to team use
A prediction, recommendation or assessment supports a specific decision. The next step depends on the model, the team and the process around it.
Within the Gamblitude platform, predictions sit alongside the historical data and metrics used by your teams.
Use Early VIP, GGR and churn outputs in player Attributes, Segments and dashboards. Combine the signal with the context your team uses to review players.
Explore the Business Intelligence Platform to see how teams work with data, metrics and player segments.
Define how bonus or content recommendations reach the relevant team or interface. Agree delivery, ownership and any required integration as part of the implementation.
Keep RG priorities focused on protection. Agree the context and responsibilities needed for professional review and follow-up, separately from commercial targeting.
A practical starting point
Agree the outcome or assessment, relevant players and the point at which your team needs the result.
Assess historical coverage, quality and relevant attributes. Establish which inputs and later outcomes can support development and evaluation.
Develop the approach around the agreed target. Compare its predictions, selections or assessments with suitable evidence and the existing approach.
Agree the workflow and integration. Measure both the quality of the result and, separately, the effect of any action taken.
The practical details
Start with a decision your team already makes and the information it is missing. The six applications above cover different questions. We can assess the available history and help define a focused first use case.
Each Gamblitude machine learning model is built individually on the operator’s historical data. Player populations, markets and products differ, so the target, inputs and evaluation are tailored to the operation.
Yes. Gamblitude’s predictive modelling offering covers both sportsbook and casino. Models are tailored to the operator’s data, player population and product. Content Recommendations, for example, covers sportsbook events, casino games and lobby ordering.
We assess relevant behavioural, transactional and contextual history, together with the outcomes needed for evaluation. Recommendations also require the applicable offer or content data. There is no single minimum history or fixed feature list for every model.
Yes, where their purposes fit. For example, churn and expected GGR can add different context to player review. A churn score does not establish bonus responsiveness, and predicted GGR is not a bonus budget. RG assessment retains its separate player-protection purpose.
The model output and the action are separate parts of the workflow. Your team defines review, eligibility and decision ownership; delivery and any automation are agreed for the implementation. Model quality and the effect of an action are evaluated separately.
Predictive Models by Gamblitude
Tell us what your team needs to decide, the data you have and how you work today. Let’s identify a useful starting point.
