Innovation Lab and experiments

1) Why Innovation Lab

• speed up time-to-learning and reduce the cost of error;
• Validate ideas before scaling investments
• develop the product through evidence (metrics, effect, retrospectives);
• support a culture of controlled risk and scientific approach.

Key principles: evidence-based, ethics-first, risk-bounded, reproducible by design.

2) Governance model

Portfolio of experiments: a unified register of hypotheses with priorities (RICE/WSJF), owners and deadlines.
Ethics & Compliance Gate: checking GDPR/PCI/local rules before start.
Security Gate: secrets/data/networks - only in sandboxes and previews, available by role.
Change Control: all changes - through branches/pipelines, artifacts in Git.
Sunset rules: stop conditions (p-value, SLO, negative impact), deadlines and disposal/scaling plans.

3) Experiment Life Cycle (HADI)

1. Hypothesis - the formulation of the hypothesis and the target metric.
2. Action - design: ficheflag, traffic, sampling, duration, risks.
3. Data - collection: telemetry, events, logs, data protection.
4. Insight - analysis: statistics, confidence intervals, conclusions, solution (ship/iterate/stop).

• measurable goal (e.g. + 2 p.p. p95 deposit conversion without latency degradation);
• sampling plan and duration;
• agreed risks/ethics/compliance;
• rollback plan and kill-switch.

• report with results and artifacts (dashboards, SQL/laptops);
• Solution and Plan: Scale/Iteration/Close
• an updated registry of hypotheses and lessons.

4) Experimental platform

Ficheflags: targeting by traffic share/tenant/geo/role, instant convolution.
Ephemeral environments (per-PR): quick demos/UX samples without affecting the production.
Sandboxes providers: PSP/KYC/games with error simulators, signature webhooks.
Telemetry: OTel + business SLI events (conversion, Time-to-Wallet, KYC failure).
Guardrails SLO: auto-off with 5xx/latency/DLQ growth.

yaml flag: deposit_offers_v2 targets:
traffic: 25%     # canary audience tenants: [eu-casino-12, eu-casino-21]
geo: [EU]
kill_switch:
slo_error_rate: ">0. 7%"
p95_latency_ms: ">1500"
metrics:
primary: deposit_conversion guardrails: [p95_latency, error_rate, chargeback_rate]

5) A/B metrics and statistics

Primary metric: Key effect (e.g. deposit conversion)

Guardrails: stability and security (latency p95, error-rate, returns/chargebacks).
Power analysis: sample size estimate (α = 0. 05, power≥0. 8).
Statistical approach: fixed horizon (classic) or sequential/Bayesian - but without "peeking" without adjustments.
Heterogeneity of effect: analysis by segment (geo, payment method, device).
SRM check (Sample Ratio Mismatch): early signal of randomization failures.

• Valid randomization and sticky-assignment.
• No SRM.
• Target sample size/duration reached.
• guardrails analysis passed.
• Reporting with confidence intervals and practical significance (uplift, NNT).

6) Categories of experiments in iGaming

UX/Flow: onboarding, KYC forms, deposit/withdrawal paths, VIP touch.
Recommendations/Personalization: game carousels, promotional segments, antichurn triggers.
Payment routes: smart-routing PSP, new methods, payment window.
Risk/Anti-fraud: scoring rules, limits, velocity-check.
Game mechanics/Content: missions/achievements, tournaments, leaders, bonus rules.
Economic optimizations: caching, retray strategies, provider control.

7) Sandboxes and safety

Synthetic/anonymized data only.
Separate secrets, short-lived tokens, IP-allowist, WAF.
Traffic limits and quotas, individual domains.
Logs - without PII/PAN; anomalies (signatures, time drift) → alerts and DLQ.

8) ML/data: prototyping and production

Feature Store (offline/online) for repeatability.
Models: from laptop → packaged artifact → "shadow" -inference → flag in prod.
Rating: offline metrics (AUC/PR), online metrics (uplift, business SLI).
Drift monitoring and retrain policies.
Security: PII minimization, feature access control, call auditing.

9) Artifact patterns (experiment)

• Issue/Opportunity
• Hypothesis and target metric
• Design (target/duration/sample)
• Risks and guardrails
• Rollback plan
• Success/failure criteria
• Owners and deadlines

• Summary Metrics and Intervals
• Impact on guardrails
• Segment analysis
• Solution (ship/iterate/stop) and "what we learned"

10) Finance and prioritization

RICE for product hypotheses; WSJF - for infrastructure/speed.
Entry threshold: the cost of the experiment ≤ X% of the quarterly budget; time-box ≤ N weeks.
KPI Lab: the share of "failed quickly," weeks before insight,% of hypotheses that hit the scale.

11) Risks and "guardrails"

Tech: latency degradation, 5xx growth, routing failures - auto-convolution of the flag.
Regulatory/ethics: banning experiments affecting vulnerable groups; transparency of promo terms and conditions.
Data: prohibition of real PII/PAN outside the food, DPIA for controversial cases.
Market/partners: Tests must not violate providers' SLAs.

12) Lab Toolkit

DevPortal: catalog of experiments, "Now/Next/Later," owners, live dashboards.
Ficheflags: SDK + management console (targeting, progression, kill-switch).
Telemetry & Notebooks: query/laptop templates, versioning in Git.
A/B service: randomization, assignment, SRM check, statistical engine.
Data Catalog: events and schemes (Registry), lineage, access policies.

13) Roles and responsibilities

Experiment Owner - hypothesis, design, artifacts, result.
Data/ML - metrics, sampling, analysis, laptops/reports.
Platform/SRE - flags, previews, guardrails SLO, alerts.
Security/Compliance - ethics/privacy gate, DPIA.
Product/Design - UX and business effect interpretation.

14) Innovation Lab launch roadmap

M0-M1 (MVP): catalog of hypotheses, phicheflags, per-PR preview, basic telemetry and dashboards, HADI templates.
M2-M3: A/B service (assignment + SRM), guardrails SLO, sandboxes providers, 1-click reports.

M4-M6: ML gateway (shadow→flag), drift monitoring, portfolio/budgets, retrospectives and an "insight textbook."

M6 +: ring experiments by region/tenant, auto-sampling planning, integration with release calendar.

15) Test run checklist

• Hypothesis Brief is full, owner appointed.
• Ethics/compliance agreed, synthetic/anonymous data.
• Flag/target/kill-switch configured, SLO-guardrails active.
• Preview environment is available, telemetry is connected.
• Sampling plan and duration approved, SRM check included.
• Published dashboards and SQL/laptops.
• Rollback plan and success/failure criteria are fixed.

Summary

Innovation Lab turns intuition into testable solutions. Strong ficheflags, preview environments and telemetry provide speed, while ethics, guardrails and compliance provide secure boundaries. Manage a portfolio of hypotheses, automate statistics and reporting, make conclusions public - and experiments will become a systemic engine of platform growth.