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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
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.

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