Stop iGaming Churn
Before the Window Closes
By the time a weekly report shows a player has gone quiet, the decision window has already closed. Scalefocus’ player churn prevention platform scores every player continuously and identifies who genuinely needs an incentive to stay.
Acting through the CRM you already use, retention runs in real time. As a result, spending shrinks to where it matters, and your team stays in control of every decision.
What Our Solution Does
Continuous scoring of every player across churn probability, behavioral state, and live triggers.
Players are classified as active, declining, at-risk, or dormant on 24–72 hour and 7–14 day horizons.
Risk is visible while the player is still reachable.
The platform separates players who need an incentive from those who would have returned anyway and prices the intervention individually.
Players with high churn risk get a message or a targeted offer, depending on the self-return likelihood.
Players with low churn risk only get continued monitoring.
Every player is scored, and the next action is decided, along with the minimum incentive.
Your team approves each proposal or lets proven journeys run automatically where you have signed off.
The action fires through your CRM, and the models are retrained so that the loop tightens and accuracy improves with each campaign.
Measurable Impact
The shift from manual retention to a live, supervised process has measurable consequences across spend efficiency, campaign accuracy, and platform reliability. These are the outcomes clients see when retention stops being a weekly task and becomes a continuous, automated process.
Industry Pain Points
BI tells you who already
churned
Reporting looks backward, and manual A/B tests compound the problem. By the time a CRM manager identifies at-risk players and a test reaches significance, the segment has moved on, and the decision window has closed before the response is ready.
Bonus spent on players who
would have stayed
Sending the same offer to an entire risk segment means incentives reach players who need nothing to return. The cost is known, but the incremental effect is not, and margin is given away without evidence that it was necessary.
One-size-fits-all offers ignore
player value
Retention strategies built on segments apply the same logic to a high-LTV player who missed one session and a dormant player who has not logged in for three weeks. The cost of that imprecision compounds across every campaign.
Core Focus Areas
Every decision is driven by four AI engines working from a single, continuously updated player record built on real-time event data across sportsbook, casino, wallet, CRM, and KYC sources.
Probability plus behavioral state is scored continuously across short and medium-term horizons. Every score carries the factors behind it, so your team understands if and why a player is flagged. The models are pre-trained on industry data, so there is no cold start.
O Churn probability updated on every meaningful event
O Behavioral state classification across four risk levels
O 24–72 hour and 7–14 day trigger horizons
O Explainable scores with driver-level transparency
For each player, the model weighs churn risk against the likelihood they would return without any intervention. It then recommends the minimum spend required to retain rather than the maximum budget allowed. Value tier and lifetime value shape every decision.
O Per-player offer sizing instead of per-segment rules
O No-spend path for players likely to self-return
O Hard budget caps per player and per campaign
O Cost and ROI tracked per action and offer type
Continuous variant testing that shifts budget to winning offers and stops underperformers early, without manual test setup or waiting weeks for significance. Holdout groups are built in from the first campaign so that uplift is measured rather than assumed.
O Automated variant creation and budget allocation
O Early stopping for underperforming offers
O Holdout groups standard across every campaign
O Learning repository capturing what worked and for which group
Players clustered by behavior, with an affinity map built per group and cross-sell paths ranked by conversion likelihood. Cross-vertical intent identified across sportsbook and casino.
O Behavioral clustering across the full player base
O Affinity mapping per player group
O Ranked cross-sell paths with conversion likelihood
O Sportsbook and casino cross-vertical signals
What Your Team Sees
The platform surfaces within the tools your CRM team already uses, with five views that cover the full retention workflow.
Control & Safeguards
Automation You Can Hand to a Regulator
Every automated action is supervised, explainable, and auditable. This means responsible gaming and self-exclusion checks run before any commercial logic, and a named person approves every proposal before it fires. Model version, inputs, rule, approver, and outcome are recorded for each action, so the trail is complete from signal to outcome.
Explainability
in Every Score
It is your team that sets hard budget and margin caps, so the optimizer cannot exceed them. Every score also carries the behavioral factors that produced it to make decisions explainable at the individual player level. Finally, permitted data and actions are configured per jurisdiction, keeping the platform compliant across every market you operate in.
Integration Without
Disruption
Decisions flow into your CRM via its existing API, so your team can keep working in the tool they already know. This way, segments, journeys, campaign templates, and reporting stay inside your CRM. The decision layer connects via a single API and enables your investment to go further, starting with one module and adding the next only when it earns its place.