The models the CRM team runs
One of the oldest and most commercially important uses of machine learning in online gambling is deciding what to do with each customer. An operator with a million accounts cannot treat them individually by hand; it can score them, group them, and let the scores decide which message, offer, game or intervention each receives. This lesson covers the customer-value models (churn, lifetime value, propensity), the personalisation models (recommendation, next-best-action) and the experimentation discipline that keeps them honest, and it closes with the constraints that regulation puts on all of them.
Churn
A churn model predicts, for each active customer, the probability that they will stop playing within a horizon (thirty, sixty or ninety days). It is trained on the histories of customers who did and did not lapse, with features drawn from recency (days since last bet, last deposit, last login), frequency and monetary value, trend (is activity falling), product mix, promotional response, support contacts and session patterns. Its output ranks customers by risk of leaving, and the CRM team acts on the ranking: a retention offer to the high-risk high-value segment, a content message to the high-risk low-value one, nothing to the customers who will stay anyway.
The value of a churn model is in the targeting. Blanket retention offers cost money on customers who did not need them; a model that identifies the ones who do, and the ones who can be saved, can cut the cost per retained customer. The evaluation is an experiment: hold out a random group from the model-driven treatment and measure the difference in retention and value, which is the only way to know that the model changed behaviour rather than predicted it.
Lifetime value
A lifetime value model estimates the net revenue a customer will produce over a future period, usually from early signals: first-week deposits, product, acquisition channel, device, market, early session behaviour. It is used for acquisition (bid more for channels and campaigns that bring high-value customers; set affiliate CPA by predicted value), for early-life CRM (invest in customers likely to become valuable before they are), and for the VIP pipeline (identify future high-value customers early). The model's error is large for any individual and useful in aggregate, and its main risk is the feedback loop: a model that predicts value from early deposits, used to decide who receives offers, produces customers who deposit more because they received offers, which the next model learns as value.
Propensity and next best action
Propensity models score the probability of a specific behaviour: depositing this week, trying a new product, responding to a particular offer, reactivating after a lapse. A next-best-action system combines propensities, value and business rules to choose, for each customer at each moment, which of the available actions (an offer, a message, a recommendation, a pause, nothing) has the highest expected value net of cost and within the constraints. It is the engine behind personalised CRM at scale, and its design is where commercial optimisation meets player protection: the same system that chooses the best offer must know which customers may not receive one.
Recommendation
Casino lobbies with thousands of games and sportsbooks with thousands of markets need ordering, and recommendation models supply it: collaborative filtering (customers who played this also played that), content-based ranking (games with similar features to what this customer plays), and bandit algorithms that balance showing what is known to work with testing what might. Recommendation is used to lift engagement, particularly among new customers who do not know the catalogue, and its constraints are regulatory as much as technical: several markets restrict marketing and offers to customers showing signs of harm (in Britain, licensees must prevent marketing and the take-up of new bonus offers where strong indicators of harm have been identified), and a recommender that pushes higher-volatility games to a customer who is chasing losses is a compliance failure in the making.
Personalised promotions
The bonus engine can award anything; the models decide what. Personalised promotions set the offer type, amount, wagering and timing per customer from the propensity and value models, subject to the market's rules and the operator's margin targets, and they are among the most scrutinised applications of machine learning in the sector. Regulators have gone further than questioning them. In the Netherlands the Kansspelautoriteit requires online operators not to tailor the timing or the nature of a bonus to individual playing behaviour, and not to aim bonuses at people showing risky play or at young adults under 24. In Britain the Gambling Commission said in 2020 that its enforcement work had found "too many cases of misconduct" in VIP schemes, which give high-value customers tailored bonuses, gifts and hospitality, and the current licence conditions (the version effective from 29 July 2026) cap bonus wagering requirements at ten times and bar incentives that mix gambling products. In Ontario, inducements and bonuses may be advertised only on the operator's own site and by direct marketing to players who have actively consented, and Australia's national framework for online wagering bars any credit, voucher or reward offered as an incentive to open or refer an account. The operating principle that survives regulatory review is that protection rules are applied before commercial models, not after: a customer flagged for harm, on a time-out, self-excluded or under an affordability restriction is removed from the promotional population before the next-best-action engine sees them.
Experimentation
None of the above can be trusted without experiments. A model that predicts churn does not prove that acting on the prediction retains customers; a recommender that raises clicks may not raise value; a personalised offer may cannibalise deposits that would have come anyway. Operators run controlled experiments (randomised holdouts, A/B tests, uplift modelling, which estimates the incremental impact of a treatment on a customer's outcome rather than the outcome itself) and measure against the metrics that matter (net revenue, retention, cost per retained customer) over long enough windows to see the tail. The experimentation platform, the discipline of running enough tests with enough power, and the culture of accepting null results are what separate operators whose models work from those whose models merely exist.
Data and features
All of these models run on the same foundation: an event stream with consistent identifiers, a feature store of customer attributes computed on a schedule and in real time, labels (who churned, who responded, who was harmed) recorded accurately, and a training pipeline that retrains as behaviour drifts. The platform course covers the architecture; the point here is that the models are only as good as the labels, and that a churn definition, a value definition or a harm label that changes silently invalidates every model built on it.
The constraints
Customer models in gambling operate under three constraints, two of which go well beyond anything retail faces. Data protection: under the GDPR and its equivalents, profiling requires a lawful basis and transparency, customers may object at any time to profiling used for direct marketing, and a decision based solely on automated processing that significantly affects someone is restricted and, where it is made on the basis of a contract or explicit consent, must carry at least the right to human intervention, to express a view and to contest it. Player protection: commercial models must be subordinate to harm models, with suppression lists applied first and the interaction between them audited; in Britain automated action on strong indicators of harm must itself be manually reviewed in each customer's case, with the customer able to contest it. Marketing rules: personalised offers are still inducements, subject to consent, opt-in by product and channel where required (Britain requires opt-in to direct marketing per product and per channel, set to opt-out by default), advertising codes and inducement bans. A CRM model that ignores any of the three risks becoming an enforcement case.
What to take from this lesson
Churn, lifetime value and propensity models rank customers for CRM action; next-best-action systems choose among actions; recommenders order catalogues; personalised promotions set offers per customer. Their value is proven only by controlled experiments against net revenue and retention. They depend on consistent event data, feature stores and accurate labels. And in gambling they are constrained by data-protection rules on profiling, by the primacy of player-protection models, and by marketing and inducement rules, with protection applied before commercial optimisation.