PRODUCTPREDICTIVE MODELS

Turning timing into return.

iGaming is a margin business decided by timing. Predictive models translate live data into forward-looking signals about where value, risk or change is about to emerge — days before it shows up in your KPIs, while there is still room to shape the outcome. Built for this industry, trained on the behavioural, transactional and performance data that define how your business actually works.

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live re-scoring · one player in focus

1 of 4.2M tracked

RE-SCORES / SEC842

MODEL FAMILIES05

SIGNALS TODAY2,418,335

ACTIVITY STREAM — player_3432705 MODELS WATCHING

MODEL RESPONSE — RECOMPUTED ON EVERY EVENTLIVE

P(CHURN)0.42

P(VIP)0.18

P(RG RISK)0.08

WHY PREDICTIVE MODELS

Insights describe reality. Predictions create leverage.

Operators focused only on historical insight make decisions after the fact. By the time a trend is visible in a report, most of its value — or its cost — has already been decided. Those using predictive models decide while there is still room to act.

FIG. 00 — INSIGHT VS PREDICTION

// TIMING

iGaming is a margin business decided by timing.

SIGNALS LEAD KPIS BY DAYS, NOT QUARTERS

PREDICTIVE MODELS ALLOW OPERATORS TO

Anticipate player value before revenue fully materialises

Detect churn and risk before it becomes visible in KPIs

Intervene selectively, not broadly

Expand margin through earlier, more selective decisions

Scale what works before competitors do

// COMPOUNDING

Earlier decisions compound into long-term advantage.

EVERY CYCLE WIDENS THE GAP

When teams know what is likely to happen next, they can:

T1Prioritise the right players, campaigns and actions

T2Allocate budget more efficiently

T3Free up teams to focus on value-generating decisions

T4Move from reporting to decision-making

PREDICTION TURNS ANALYTICS INTO AN OPERATIONAL TOOLINSIGHT → LEVERAGE

WHY PURPOSE-BUILT

Generic engines assume stability. iGaming punishes that.

Predictive modelling in iGaming cannot be approached the way it is in ecommerce, fintech or SaaS — the data itself behaves differently. Generic predictive engines assume stability and punish you with false positives the moment reality stops cooperating — and every false positive costs twice: the wasted intervention, and the real signal it buried.

HIGH-FREQUENCY BETTING EVENTS CASINO VOLATILITY & RTP VARIATION IRREGULAR DEPOSIT CYCLES AFFILIATE TRAFFIC SHIFTING HOURLY RISK & AML CONSTRAINTS LIVE MARKET CALENDARS

FIG. 01 — GENERIC VS PURPOSE-BUILT

GENERIC ENGINE

ASSUMED BASELINE

◆ FALSE POSITIVES

Assumes stable, slow-moving behaviour

Trained on ecommerce-style textbook patterns

Breaks the moment volatility hits

GAMBLITUDE MODELS

VOLATILITY IS THE BASELINE

Built assuming none of this is stable

Trained on real betting and gaming behaviour

Signals that hold up under real operational conditions

FIVE FAMILIES OF MODELS

Every signal a team can act on, before it shows up in KPIs

Value, churn, incentives, content and player protection — five families of models producing signals that analysts, CRM, trading, risk and product teams can act on with real confidence.

FIG. 02 — MODEL FAMILIES

MODELS — 01 / 05 P(LTV | day 1)

Player value and lifetime

OUTCOME

NATIVE TO THE PLATFORM

Not a separate module. Part of the tools your teams already use.

Every prediction is built on the same semantic layer as Metrics and Attributes — consistent with reporting, explainable in business terms, flowing through the entire Gamblitude ecosystem without manual handoff.

No additional integrations. No parallel pipelines. No engineering effort to wire predictions back into where they need to land.

Predictive layer inside the Gamblitude platform

NATIVE — NO PIPELINES

PREDICTIVE MODELS

GOVERNED SEMANTIC LAYER

METRICS · ATTRIBUTES · PERMISSIONSSHARED

SIGNALS FLOWINGONE SOURCE OF TRUTH

FIG. 03 — SIGNAL FLOW

OPERATES DIRECTLY ON TOP OF

DATA WAREHOUSE GOVERNED METRICS & ATTRIBUTES EXISTING PERMISSIONS

▼▼▼

PREDICTIVE MODELS FORWARD-LOOKING SIGNALS

▼▼▼

Dashboardsforecasts next to historical KPIs

Dynamic Listsforward-looking segments

Insight Radaralerts as a shift starts forming

AI Agentdecision support in plain language

Reports & Targetsno manual handoff

Team workflowsCRM, trading, risk, finance — real time

TURNING PREDICTION INTO ADVANTAGE

In iGaming, the cost of acting late is measurable

Acting late rarely looks like one big mistake. It looks like a bonus paid to a player who was staying anyway, a VIP who slipped away unnoticed, a risk flagged after the damage was done. None of it appears as a single line item — and all of it compounds.

FIG. 04 — LATE VS EARLY

DECIDING AFTER THE FACTHINDSIGHT

MISSED UPSIDE INEFFICIENT SPEND ERODED MARGIN LOST PLAYERS

▼

DECIDING WHILE THERE IS ROOMFORESIGHT

The signal arrives while the outcome can still be shaped — the campaign lands before the player leaves, the bonus goes only where it changes behaviour, the risk is handled before it escalates.

WITH MODELS EMBEDDED IN DAILY WORKFLOWS, TEAMS CAN

A1Capture value earlier — and prevent avoidable risk

A2Focus budget and attention where it actually changes outcomes

A3Reduce invisible waste that never shows up as a single line item

A4Balance growth, margin and player protection with intent

OVER TIME, THIS SHIFT COMPOUNDSA REAL COMPETITIVE ADVANTAGE

FOUNDATION BEFORE PREDICTION

Without a solid foundation, predictions are opinions

Predictive models are only as reliable as the data and definitions beneath them. The predictive layer was designed for the volume, complexity and pace of real iGaming operations — so predictions stay reliable, explainable and fully auditable.

With the right foundation, they become decision-grade signals.

FIG. 05 — RELIABILITY STACK

Billions of events

Across sportsbook and casino, at production pace

Clean event-level data

Drawn from your own Gamblitude Data Warehouse

Stable, reproducible features

The same governed Attributes and Metrics as the rest of the platform

Adapts as you evolve

Product catalogue, markets and player behaviour included

Explainable & fully auditable

For data science, BI, compliance and internal audit alike

purpose-built · operator-proven

iGaming is full of characteristic events and constraints that generic models were never trained on — bonus cycles and wagering requirements, live sport calendars, volatile casino sessions, payment, affiliate and regulatory behaviour. We have designed and deployed these models many times inside real operators, so we know which signals matter, which patterns mislead, and how to keep predictions reliable once they run in daily operations.

WHY OPERATORS CHOOSE IT

Reliable enough to run operational decisions on

Purpose-built for iGaming — not retrofitted from a general-purpose template

Trained by practitioners with direct industry experience

Grounded in your governed data warehouse — consistent with reporting

Integrated across every module without extra engineering effort

Decision-grade signals — not interesting side information

PART OF HOW THE BUSINESS RUNS —
NOT A PROJECT WITH ITS OWN ROADMAP

FAQ

Common questions

Something else on your mind? Talk to us.

01What data do the models need?

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They run directly on your Gamblitude Data Warehouse — event-level bets, sessions, deposits, bonuses and risk events. No separate feature store, no export pipeline, no parallel data preparation.

02How quickly do we see first predictions?

Value and lifetime models produce long-term NGR estimates within hours or days of a player's acquisition. Because the models use the same governed Attributes and Metrics as the rest of the platform, there is no lengthy feature-engineering phase before signals start flowing.

03Are the predictions explainable?

Yes. Every prediction is built on the same semantic layer as your Metrics and Attributes, so signals are consistent with reporting, explainable in business terms, and fully auditable — for data science, BI, compliance and internal audit alike.

04Do we need our own data science team?

No. The predictive layer is designed and maintained by Gamblitude practitioners with direct operator experience. Your teams consume the signals where they already work — dashboards, Dynamic Lists, Insight Radar alerts and the AI Agent.

05How do the models handle volatility and seasonality?

They are built around the assumption that nothing is stable: casino volatility and RTP variation, sportsbook seasonality tied to live market calendars, hourly affiliate swings and irregular deposit cycles. That assumption is what keeps false positives down under real operational conditions.

EXPLORE THE PLATFORM