Audience
Start with
the eligible players.
Define the reactivation audience and apply your contact, offer and player-protection requirements. The recommendation starts from that audience.
Gamblitude predictive models
Bonus Value Optimisation for iGaming.
Make every bonus amount
a considered decision.
Design player-level reactivation bonus recommendations for an audience your team has already qualified. Bring player behaviour, previous offers and the outcome you want to achieve into the decision about how much to offer.
An operator-specific implementation, scoped with your CRM and data teams.
Eligible audiencePlayer-level amountMeasured reactivation
01 / A focused reactivation decision
Decide the amount
around the player.
Bonus Value Optimisation is designed to recommend a reactivation bonus amount for each player in an operator-defined, eligible audience.
Your team establishes who can receive the offer and which offer mechanics apply. We shape the amount recommendation around the available player history, agreed constraints and a clear definition of successful reactivation.
- Decision supported
- How much to offer
an eligible player - Intended output
- A player-level
reactivation bonus value - Starting audience
- Defined and qualified
by the operator - Delivery
- Approach, evaluation and
integration agreed together
02 / What the recommendation means
An audience. An amount.
A decision to test.
Start with
Amount
Work within the offer
you can deliver.
Agree the offer mechanics and the bonus values that can be used. The intended recommendation concerns the amount for each player within those boundaries.
Decision
Put the recommendation
into a measured process.
Decide how the result will reach your CRM team, how the offer will be delivered and how outcomes will be compared. Those steps form part of the implementation scope.
03 / Ask the right predictive question
Leaving, returning
and responding are
different questions.
A player may be likely to disengage without being responsive to a particular incentive. A player who returns after an offer may also have returned without it.
Use churn and expected player value as context where relevant. Evaluate the bonus recommendation against the reactivation outcome and cost your team has agreed to measure.
04 / A broad feature store
Learn from the history
behind the offer.
Gamblitude’s feature store brings together behavioural, transactional and contextual attributes. For bonus-value work, we also assess what historical offer and response information is available.
These families describe the data that may inform development. The final selection follows your data quality, reactivation target and evaluation results.
Sessions & activity
Recent activity, frequency and patterns of returning to the product.
Product usage
The products players use and how their engagement changes over time.
Channel & acquisition
Available channel, acquisition-source and cohort context.
Deposits & wagering
Deposit history, stakes and other transactional patterns relevant to the decision.
Bonus history
Available records of previous offers, claims, costs and subsequent activity.
Lifecycle & value
Player tenure, revenue history and changes across the relationship.
05 / Individually built for every operator
Your players. Your offers.
Your history.
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.
For Bonus Value Optimisation, the starting point is your reactivation process: the players you contact, the offers you can deliver and the outcomes you can observe. Feature selection, modelling and evaluation are shaped around that context.
Your player base.
Learn from the behaviour and offer history available for your customers.
Your market and products.
Account for the setting in which an offer is received and used.
Your definition of success.
Agree what meaningful reactivation looks like and how costs enter the assessment.
06 / Meaningful reactivation
Measure what happens
after the bonus is claimed.
Claiming an offer is one event in the journey. Define the activity you want to see afterwards, how long you will observe it and which financial measure will capture the result after incentive costs.
Offer response
Did the player
use the incentive?
Track receipt and use where the data allows. These events show engagement with the offer, but do not establish the outcome on their own.
Meaningful activity
Did the player return
beyond the claim?
Agree the activity and follow-up period that count as reactivation. Look beyond a single bonus-use event to the subsequent behaviour your team wants to evaluate.
Incremental result
What changed
because of the offer?
Use a controlled test with a comparable control group following the current offer policy or another agreed treatment. Include incentive costs and measure the additional result, rather than attributing every return to the bonus.
Historical patterns help develop a recommendation. A controlled test measures whether the recommendation improves reactivation outcomes after costs.
07 / From definition to evaluation
Build the recommendation
around the test.
Define the audience and outcome.
Agree eligible players, offer mechanics, meaningful reactivation and the observation period.
Review the available history.
Assess player attributes, previous offers, outcomes and the consistency of cost data.
Develop the amount recommendation.
Establish the approach and the feasible bonus values around your data and operating constraints.
Connect the CRM process.
Agree delivery, permissions, controls and the way outcomes return for analysis.
Test and refine.
Compare results with the control group in the agreed test, including costs and subsequent activity.
The practical details
Bonus Value
Optimisation.
Your questions.
The audience, data and evaluation behind an operator-specific implementation.
Is this a standard model ready to switch on?
This is an individually scoped implementation. We agree the approach, deliverables, evaluation and delivery plan with your CRM and data teams after reviewing the available history and reactivation process.
Does it decide who receives a bonus?
The operator defines and qualifies the eligible audience. The intended recommendation is the bonus amount for each player within that audience. Contact eligibility, exclusions and player-protection rules remain part of your operating process.
Does it choose the bonus type as well as the amount?
This scope focuses on value within agreed offer mechanics. Automatically choosing between different bonus types is a separate question and is not assumed here.
Is this for reactivation or every promotional campaign?
The starting use case is reactivation. Other campaign purposes have different audiences, objectives and measurement needs, so they require their own scope and evaluation.
Can we use churn or predicted GGR to set the amount?
They can provide context, but they answer different questions. Churn concerns disengagement; predicted GGR concerns future revenue. Neither alone establishes how a player will respond to an offer or which amount produces an additional benefit.
What historical data is needed?
We assess available player activity, transaction and bonus histories, including which offers were made and what happened afterwards. Data coverage and the evaluation design determine the final inputs and history requirements.
Would it issue the offer automatically?
The recommendation and its execution are separate parts of the process. Delivery through your CRM, approval rules and any automation are defined during implementation.
How would we know whether it works?
Agree meaningful reactivation, the observation period and cost treatment before a controlled test. Use a comparable control group following the current offer policy or another agreed treatment to measure the additional outcome after incentive costs.
Bonus Value Optimisation by Gamblitude
Start with the offer
your team wants to improve.
Bring your reactivation audience, current bonus rules and definition of success.
Let’s scope a recommendation and a test that can tell you whether it helps.