Platform Test A/B Tools
Introduction
A/B testing allows you to test hypotheses - from new UI components and promotional offers to game mechanics and bonuses - in a real audience without risking the main platform. Ideally, the online casino platform includes at least three components: a system for allocating users to experimental groups, collecting and storing metrics, and results analysis tools.
1. Feature-flag framework
1. Configuration of flags
Centralized storage: YAML/JSON files in Git or a special service console.
Rollout support: percentage of inclusion (5%, 20%, 100%) and targeting by segment (new players, VIP, geo).
2. Client and Server SDK
JavaScript/TypeScript for frontend; Kotlin/Swift for mobile; Java/Go/.NET for backend.
The 'isFeatureEnabled (flagKey, userContext)' methods allow you to select an option in runtime.
3. Runtime-recalculation
The flags receive a TTL (for example, 60 s) in the local cache, and a fresh config is requested upon expiration.
4. Rollback-mechanism
Automatic rollback to 'default: off' on failure and alert when errors grow.
2. Randomization and targeting
1. Consistent hashing
For each 'userId' or 'sessionId', the hash and Cartesian division by range is calculated\[ 0,1) → group A/B/control.
Ensures that the user always falls into the same group throughout the experiment.
2. Multi-armed trials
More than three options (A, B, C, D) with a uniform or configurable distribution.
3. Segmentation
Trigger on events: first deposit, high roller, churn-risk.
Key-value support of context attributes (level, balance) for detailed analyses.
3. Metrics collection and storage
1. Client- and server-side tracking
Frontend: events' experiment _ view ',' experiment _ action'via analytics SDK (Segment, Amplitude).
Backend: metrics' bet _ success', 'bonus _ activation' with labels' experiment _ id ',' variant '.
2. Storage tools
Event stream: Kafka topic `experiment. events`.
OLAP storage: Redshift, BigQuery or ClickHouse for subsequent analysis.
3. Data pipeline
ETL (Airflow/dbt) aggregates events into tables of the form:
4. Analysis of results
1. Statistical methods
t-test and chi-square for conversions; Bayesian approach for conversion metrics (Beta-distribution).
Automatic calculation of p-value, confidence interval, statistical power.
2. Dashboards and Reports
Built-in UI module in the platform admin panel: experiment selection, metrics, conversion graphs and lift.
Comparison patterns by segment: new vs returned players, by geo, VIP status.
3. Stopping rules
Grow data to sufficient statistical power (e.g. 80% power) before completion.
Automatic notification of the person responsible for the experiment.
5. Integration with CI/CD
1. Experiment as code
The description of the experiments (flagKey, variants, rollout, metrics) is stored in the repository as YAML.
Bullet requests cause automatic validation of the scheme and, after merge, dumping of new flags.
2. GitOps-approach
Argo CD/Flux synchronizes feature-flags configuration between Git and live environments.
3. Automated testing
Unit tests of SDK clients for correct allocation to groups.
E2E tests simulate userContext with different flags.
6. Safety and compliance
1. RBAC control
Differentiation of rights to create and modify experiments: marketers vs devops vs product managers.
2. Audit trail
Log of all feature-flags changes and experiments with operator userId and timestamp.
3. GDPR compatibility
Anonymization of userId; possibility to delete data of experiments on request.
Conclusion
Effective A/B testing on the online casino platform requires tight integration of the feature-flags framework, randomization, event collection and storage, statistical analysis, and CI/CD processes. Only the combination of these components provides a safe, reproducible and scalable hypothesis testing process, minimizing risks to the core gaming experience.
A/B testing allows you to test hypotheses - from new UI components and promotional offers to game mechanics and bonuses - in a real audience without risking the main platform. Ideally, the online casino platform includes at least three components: a system for allocating users to experimental groups, collecting and storing metrics, and results analysis tools.
1. Feature-flag framework
1. Configuration of flags
Centralized storage: YAML/JSON files in Git or a special service console.
Rollout support: percentage of inclusion (5%, 20%, 100%) and targeting by segment (new players, VIP, geo).
2. Client and Server SDK
JavaScript/TypeScript for frontend; Kotlin/Swift for mobile; Java/Go/.NET for backend.
The 'isFeatureEnabled (flagKey, userContext)' methods allow you to select an option in runtime.
3. Runtime-recalculation
The flags receive a TTL (for example, 60 s) in the local cache, and a fresh config is requested upon expiration.
4. Rollback-mechanism
Automatic rollback to 'default: off' on failure and alert when errors grow.
2. Randomization and targeting
1. Consistent hashing
For each 'userId' or 'sessionId', the hash and Cartesian division by range is calculated\[ 0,1) → group A/B/control.
Ensures that the user always falls into the same group throughout the experiment.
2. Multi-armed trials
More than three options (A, B, C, D) with a uniform or configurable distribution.
3. Segmentation
Trigger on events: first deposit, high roller, churn-risk.
Key-value support of context attributes (level, balance) for detailed analyses.
3. Metrics collection and storage
1. Client- and server-side tracking
Frontend: events' experiment _ view ',' experiment _ action'via analytics SDK (Segment, Amplitude).
Backend: metrics' bet _ success', 'bonus _ activation' with labels' experiment _ id ',' variant '.
2. Storage tools
Event stream: Kafka topic `experiment. events`.
OLAP storage: Redshift, BigQuery or ClickHouse for subsequent analysis.
3. Data pipeline
ETL (Airflow/dbt) aggregates events into tables of the form:
| experiment\_id | variant | metric | count | users | timestamp | |
|---|---|---|---|---|---|---|
| Available in SQL for BI boards. |
4. Analysis of results
1. Statistical methods
t-test and chi-square for conversions; Bayesian approach for conversion metrics (Beta-distribution).
Automatic calculation of p-value, confidence interval, statistical power.
2. Dashboards and Reports
Built-in UI module in the platform admin panel: experiment selection, metrics, conversion graphs and lift.
Comparison patterns by segment: new vs returned players, by geo, VIP status.
3. Stopping rules
Grow data to sufficient statistical power (e.g. 80% power) before completion.
Automatic notification of the person responsible for the experiment.
5. Integration with CI/CD
1. Experiment as code
The description of the experiments (flagKey, variants, rollout, metrics) is stored in the repository as YAML.
Bullet requests cause automatic validation of the scheme and, after merge, dumping of new flags.
2. GitOps-approach
Argo CD/Flux synchronizes feature-flags configuration between Git and live environments.
3. Automated testing
Unit tests of SDK clients for correct allocation to groups.
E2E tests simulate userContext with different flags.
6. Safety and compliance
1. RBAC control
Differentiation of rights to create and modify experiments: marketers vs devops vs product managers.
2. Audit trail
Log of all feature-flags changes and experiments with operator userId and timestamp.
3. GDPR compatibility
Anonymization of userId; possibility to delete data of experiments on request.
Conclusion
Effective A/B testing on the online casino platform requires tight integration of the feature-flags framework, randomization, event collection and storage, statistical analysis, and CI/CD processes. Only the combination of these components provides a safe, reproducible and scalable hypothesis testing process, minimizing risks to the core gaming experience.
