Turnkey A/B Testing
We deploy the platform of experiments, we tune the features, the metrics and the statistical engine, and we make experiments fast, honest and manageable, from hypothesis to decision, in one loop.
Request audit and implementationWhy do you need a system A/B test?
Random tests give random results. We create a sustainable environment: one. fitflag*, correct distribution*, valid metrics* and transparent statistics: the result is higher conversions, average checks and retention at controlled risk.
Business effects
- Reduce the time from idea to result by 2-3 times.
- +10-30% to CR and revenue from continuous test cycles.
- Reducing false positive conclusions through guardrail metrics* SRM detection*.
Tools and architecture
We're putting together a mature stack: featureflags and traffic routing, a stat engine, an event pipeline, storage and dashboards, and it works as a single platform.
Ficheflags and randomization
- Feature Flags*: Gradually rollout, kill-switch, segment targeting.
- Bucketing*: stable bucket on user_id (Murmur/CRC32), sticky, namespaces and mutual exclusivity of tests.
- Logistics of the exhibition*: single fact of hitting the version with timestamp and configuration version.
- Client and server SDKs: web, mobile, backend, edge.
Event collection and metrics
- Single scheme Tracking Plan*: Events, properties, identifiers.
- Gardreils.guardrails*): latency, errors, fault tolerance.
- Marketing/product: CR, CTR, ARPU/ARPPU, LTV, Retention, N day metrics.
- Importing transactions/checks/CRM for hard business metrics.
Statistical engine
- Power calculator*: MDE, power, sample size, clusters.
- Frequency analysis: Wilson transform, bootstrap-CI sequence*.
- Bayes: posteriores, ROPE, probability of superiority, expected loss.
- CUPED* and covariates to reduce dispersion; stratification.
- Multiple comparisons: FWER/FDR control, adjustments (Holm/Benjamini–Hochberg).
- Uplift models and heterogeneity* by segment.
Warehouse and dashboards
- Event storage/metrics (e.g. ClickHouse) + ETL/ELT connectors.
- Dashboards and reports: Redash/Metabase/Grafana, CSV/JSON uploads.
- Alerts: SRM, traffic/conversion drop, guardrail degradation.
Typical stack
Process: From Hypothesis to Solution
Formulation of the hypothesis
JTBD/behavioral insights → hypothesis → target metric → minimal detectable effect (MDE) → risks and guardrails.
Designing an experiment
Sample size, randomization (user/session/geo-clusters), inclusion criteria, exclusion windows, intent-to-treat vs per-protocol.
Launch and monitoring
Ramp rules (1%→5%→25%→50%), SRM-alerts, control of the flicker, QA scenarios and exposure correctness.
Analysis and interpretation
Verification of assumptions, variance and CUPED*, effect and confidence intervals, heterogeneity and secondary metrics.
Decision and calculation
Go/No-Go, rollout through featureflags, backward compatibility, documentation and dashboard updates.
Methodology and quality control
Before launch
- AA-test• environment: noise/stability, false positives
- Exposure check: uniqueness of user_id, stickiness of the bucket, share of traffic by variants.
- Validation of metrics: sources, formulas, frequencies and SLAs.
During the test.
- SRM*-detectors: instantaneous and cumulative.
- Consistent analysis*: control of the level of α in intermediate views.
- Guardrails: errors, latency, fault tolerance - do not let the "poisonous" option win.
After the test.
- Sustainability Testing: Retest/Rotation, Seasonal Influence.
- Heterogeneity of effect*: Devices, regions, channels, LTV segments.
- Knowledge repository: Experiment cards, code/dashboard references, reuse of ideas.
Alternatives and expansions
- Many options and many metrics: FDR/FWER control.
- Cluster/geo-tests, retention and lag metrics.
- Multivariate tests and bandits when it makes sense.
| Risk | How it manifests | Control tool |
|---|---|---|
| SRM | Imbalance of traffic between options | Online SRM Detector, Stop and Redistribution |
| Peak-poking | The Early Success That Disappears | Sequential diagrams, fixed horizon of analysis |
| Audience overflow | User participation in several tests | Namespaces, Mutual Exclusion, Compatibility Matrix |
| Changes in behaviour | Flicker, interface flicker | Server-side ficheflags, preload configuration |
What do you get?
Platform
- Deployed featureflags (client/server), routing and logging exposure.
- Statistical engine with power calculator, CUPED and reports.
- Dashboards: test results, guardrails, SRM, retrospective.
Processes
- Templates of the experiment and report cards.
- Procedures for design, launch, analysis and layout.
- Matrix of test compatibility and prioritization of hypotheses.
Integration and learning
- Integration with analytics, CRM and billing.
- Training in marketing, product and developers.
- Support of the first 3-5 tests "turnkey".
Examples of KPI
- E-commerce: CR to Purchase, AOV, returns, margin.
- SaaS: Activation D7, Retention D30, ARPU/ARPPU.
- Content: CTR, depth, subscriptions, conversion to lead.
Cases
E-commerce: Product card
Server-side ficheflags, CUPED on past purchases, guardrails on errors and latency. Result: +7.8% to CR, +4.1% to AOV at stable speed.
SaaS: onboarding
Cluster randomization by account, intent-to-treat, sequential analysis. +12 p.p. to activation of D7, -18% of the time to "first value".
Media: headlines
Multivariate + FDR control, uplift by traffic segments. +9.6% CTR on tape without retention degradation.
FAQ
How long does the base launch last?
Usually 2-4 weeks: fitflags → exposure → metrics → dashboards → first tests → procedures.
Can I test without speeding down?
Yes: server featureflags, configuration preload, cache, "flicker" minimization.
How to deal with false positives?
Sequence circuits, FDR/FWER control, fixed protocol analysis, AA-test environment.
Do you support mobile apps?
Yes: SDK for iOS/Android, offline buffer, sticky bucketing, version compatibility.
Decoding of terms
- Feature Flag: a functionality switch with targeting and smooth rollout.
- Bucketing: стабильное распределение users по вариантам теста.
- Exposition: the fact that the user has entered the test version.
- Tracking Plan: A consistent pattern of events and properties.
- GuardrailsSecurity stability metrics (errors, latency, availability).
- SRMThe traffic imbalance between the variants (Sample Ratio Mismatch).
- PowerThe power of the test is the chance to detect the real effect.
- Sequential: sequential analysis with intermediate "views" without growth of α.
- CUPED: decrease in variance due to pre-intervention covariates.
- AA-test: Environmental control: Comparison of the same options.
- heterogeneityDifference of effect by audience segments.
- KPIKey Product/Business Metrics.
Ready for rigorous experiments without pain?
We'll deploy the platform, set up the metrics and the statistics, train the team, and run the first tests, and the experiments will be fast, reproducible and useful for business.
Request an audit and work plan