Analytics and reporting in casino platforms
Introduction
Analytics and reporting are the heart of online casino management: based on them, decisions are made on marketing, risk management, bonus programs and operational efficiency. Properly configured data collection and end-to-end ETL pipeline ensure timely and accurate information for all concerned departments.
1. Key business metrics
1. GGR (Gross Gaming Revenue): total amount of bets minus winnings; the main indicator of profitability.
2. NGR (Net Gaming Revenue): GGR less bonuses and returns; reflects net income.
3. ARPU (Average Revenue Per User): Average revenue per active player for the period.
4. DAU/MAU: daily and monthly activity; retention ratio (DAU/MAU).
5. Conversion Rate: Proportion of first-time or deposit customers
6. Turnover per Session: average bet amount per session; engagement rate.
7. Churn Rate and LTV: Churn and lifetime customer value.
2. Data Architecture and ETL Pipeline
```mermaid
flowchart LR
subgraph Data Sources
Data Lake for storing "raw" events: spin-sessions, transactions, clicks.
Data Warehouse (Snowflake, Redshift, BigQuery) with star/snowflake model for quick analytical queries.
TSDB (Prometheus, InfluxDB) for time series of system and game metrics.
ETL processes: Apache NiFi, Airflow or dbt for cleaning, aggregating and loading data.
3. Visualization and dashboards
1. Operational panels
KPI boards with real-time X-ray reports: GGR, number of active sessions, average bet amount.
Heatmap by time of day and geography to optimize campaign schedules.
2. Deep analytics
Cohort analysis: behavior of groups of players over time, analysis of the effectiveness of bonuses and attraction channels.
Funnel analysis: user's path from registration to the first bet and retention.
3. Self-service BI
Ad-hoc queries and exploration panels where marketers and analysts can build their own reports.
Automatic documented data catalog.
4. Automatic report generation
Scheduled Reports: daily, weekly, monthly reports in PDF/CSV format with comments about anomalies (low GGR, increase in transaction failures).
Alerts and Notifications: automatic notifications in Slack/Email when metrics deviate from thresholds (DAU drop> 10%, payment failure growth> 5%).
Embedded Analytics: embedding dashboards directly into the admin panel via an iframe or BI API.
5. ML modules and forecasting
Revenue forecasting: ARIMA or Prophet models to predict GGR for next month.
User segmentation: clustering into k-means or behavior-based DBSCAN, defining VIP profiles.
Anomaly Detection: algorithms (Isolation Forest, LSTM-autoencoders) for detecting anomalies in transactions or activity.
6. Integration with external systems
CRM/Marketing Automation - export segments and triggers based on analytics (email mailings, push notifications).
Ad Platforms: downloading ROI reports through advertising channels (Google Ads, Facebook) to optimize the budget.
Fraud Detection: sharing data with anti-fraud services (ThreatMetrix, Emailage) for risk scoring.
7. Data Quality Management
Data Observability: Monitor data completeness, uniqueness, consistency, and relevance through Great Expectations or Monte Carlo.
Data Lineage: traceability of the path of each indicator from the source to the report.
Data Governance: model role (data steward, data owner), access policy and GDPR compatibility.
8. Analyst Infrastructure Security
RBAC and ABAC in BI systems: differentiation of rights to view and edit reports.
Encryption at rest & in transit - Encrypts all data in Lake/DWH and TLS connections.
Audit logs - Capture all analyst activity and model changes.
Conclusion
Advanced analytics and full-fledged reporting in casino platforms provide comprehensive control over the business, allow you to make informed decisions, quickly respond to deviations and predict development. A complex of data lake, storage, BI-dashboards and ML-modules creates a single ecosystem for monetization, marketing and risk management.
Analytics and reporting are the heart of online casino management: based on them, decisions are made on marketing, risk management, bonus programs and operational efficiency. Properly configured data collection and end-to-end ETL pipeline ensure timely and accurate information for all concerned departments.
1. Key business metrics
1. GGR (Gross Gaming Revenue): total amount of bets minus winnings; the main indicator of profitability.
2. NGR (Net Gaming Revenue): GGR less bonuses and returns; reflects net income.
3. ARPU (Average Revenue Per User): Average revenue per active player for the period.
4. DAU/MAU: daily and monthly activity; retention ratio (DAU/MAU).
5. Conversion Rate: Proportion of first-time or deposit customers
6. Turnover per Session: average bet amount per session; engagement rate.
7. Churn Rate and LTV: Churn and lifetime customer value.
2. Data Architecture and ETL Pipeline
```mermaid
flowchart LR
subgraph Data Sources
| A [Game Engines] --> | stream | Kafka |
|---|---|---|
| In [Payment Gateways] --> | REST/Webhooks | API-Gateway |
| C [CRM & Marketing] --> | batch | S3 |
| D [Monitoring Systems] --> | metrics | Prometheus |
| end | ||
| subgraph Storage | ||
| Kafka --> | ingest | RawLake[(Data Lake)] |
| API-Gateway --> | ingest | RawLake |
| S3 --> | ingest | RawLake |
| Prometheus --> | scrape | MetricsDB[(TSDB)] |
| end | ||
| subgraph Processing | ||
| RawLake --> | ETL | DataWarehouse[(DWH)] |
| MetricsDB --> | ETL | DataWarehouse |
| end | ||
| subgraph Reports and BI | ||
| DataWarehouse --> | SQL | Looker/Tableau/PowerBI |
| DataWarehouse --> | API | CustomDashboards |
| DataWarehouse --> | batch | ScheduledReports[PDF/CSV] |
| end | ||
| ``` |
Data Lake for storing "raw" events: spin-sessions, transactions, clicks.
Data Warehouse (Snowflake, Redshift, BigQuery) with star/snowflake model for quick analytical queries.
TSDB (Prometheus, InfluxDB) for time series of system and game metrics.
ETL processes: Apache NiFi, Airflow or dbt for cleaning, aggregating and loading data.
3. Visualization and dashboards
1. Operational panels
KPI boards with real-time X-ray reports: GGR, number of active sessions, average bet amount.
Heatmap by time of day and geography to optimize campaign schedules.
2. Deep analytics
Cohort analysis: behavior of groups of players over time, analysis of the effectiveness of bonuses and attraction channels.
Funnel analysis: user's path from registration to the first bet and retention.
3. Self-service BI
Ad-hoc queries and exploration panels where marketers and analysts can build their own reports.
Automatic documented data catalog.
4. Automatic report generation
Scheduled Reports: daily, weekly, monthly reports in PDF/CSV format with comments about anomalies (low GGR, increase in transaction failures).
Alerts and Notifications: automatic notifications in Slack/Email when metrics deviate from thresholds (DAU drop> 10%, payment failure growth> 5%).
Embedded Analytics: embedding dashboards directly into the admin panel via an iframe or BI API.
5. ML modules and forecasting
Revenue forecasting: ARIMA or Prophet models to predict GGR for next month.
User segmentation: clustering into k-means or behavior-based DBSCAN, defining VIP profiles.
Anomaly Detection: algorithms (Isolation Forest, LSTM-autoencoders) for detecting anomalies in transactions or activity.
6. Integration with external systems
CRM/Marketing Automation - export segments and triggers based on analytics (email mailings, push notifications).
Ad Platforms: downloading ROI reports through advertising channels (Google Ads, Facebook) to optimize the budget.
Fraud Detection: sharing data with anti-fraud services (ThreatMetrix, Emailage) for risk scoring.
7. Data Quality Management
Data Observability: Monitor data completeness, uniqueness, consistency, and relevance through Great Expectations or Monte Carlo.
Data Lineage: traceability of the path of each indicator from the source to the report.
Data Governance: model role (data steward, data owner), access policy and GDPR compatibility.
8. Analyst Infrastructure Security
RBAC and ABAC in BI systems: differentiation of rights to view and edit reports.
Encryption at rest & in transit - Encrypts all data in Lake/DWH and TLS connections.
Audit logs - Capture all analyst activity and model changes.
Conclusion
Advanced analytics and full-fledged reporting in casino platforms provide comprehensive control over the business, allow you to make informed decisions, quickly respond to deviations and predict development. A complex of data lake, storage, BI-dashboards and ML-modules creates a single ecosystem for monetization, marketing and risk management.
