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Szczegóły

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:

  1. The Four Types of Analytics and where predictive fits in your current setup
  2. Why data foundation matters more than algorithms (garbage in, garbage out isn't just a saying)
  3. How to define "churn" in iGaming where there's no subscription cancellation
  4. The lift curve technique for measuring model effectiveness before full rollout
  5. Segmentation vs. player-level modeling and why degrees of freedom matter
  6. Stepwise approach to promotions that isolates true impact from noise
  7. 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

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