Wed, Nov 12 · 3:00 PM CET
Duration: 45-60 minutes
Format: Live presentation with Q&A session
Level: Intermediate to Advanced
## Why Attend?
This webinar walks through two complete implementations of predictive models at European casino and sportsbook operators. You'll see the actual development process, the challenges they hit, and the measurable results they achieved - no theory, just what happened when they went live.
## What You'll Learn
Case 1: Anti-Churn Model
An operator losing half their players after two years built a model that predicted churn 3.5x better than random selection. Result: 23% more lifetime GGR from targeted retention.
Case 2: Promotion Effectiveness
A sportsbook running multiple campaigns couldn't tell which ones worked. They isolated the true impact of each promotion and redistributed budget. Result: 26% more GGR next quarter from the same spend.
Plus: The 8-step development cycle, common data challenges (negative lifespans, anyone?), and why "before vs after" comparisons mislead you every time.
## Key Takeaways
By the end of this webinar, you'll understand:
The Four Types of Analytics and where predictive fits in your current setup
Why data foundation matters more than algorithms (garbage in, garbage out isn't just a saying)
How to define "churn" in iGaming where there's no subscription cancellation
The lift curve technique for measuring model effectiveness before full rollout
Segmentation vs. player-level modeling and why degrees of freedom matter
Stepwise approach to promotions that isolates true impact from noise
Common pitfalls : overfitting, historical data assumptions, ignoring competition
## Who Should Attend
This webinar is for you if you're:
Marketing Directors/Managers running retention and promotional campaigns
CRM & Retention Managers looking for better player targeting
Head of Analytics/BI building or planning predictive capabilities
Data Scientists & Analysts interested in real-world implementation approaches
C-Level Executives evaluating investment in analytics infrastructure