Replace debates with evidence
No more decisions based on taste. Every change is validated by data.
A/B testing that
drives revenue
We run experiments across your website, product, and funnel to grow conversion and revenue with data, not opinions.
from 190,000 RUB / month
We reply within 1 business day. NDA available on request.
Metric-focused execution
We test hypotheses that move revenue, not cosmetic UI tweaks.
Protection from false winners
We rely on correct statistics and clean data before making decisions.
Every test ties to money
We estimate impact and roll out only changes that increase profit.
Why businesses need A/B testing
Without experiments, changes often look convincing but do not improve sales. We turn optimization into a controllable growth loop.
Reduce wasted ad spend
Traffic goes to variants that convert instead of burning budget.
Find real sales drivers
See exactly what creates uplift: value prop, structure, form, CTA, or pricing.
Lower rollback risk
We do not ship noisy results. Winners are selected by predefined criteria.
Build repeatable growth
We maintain a learning library so each new hypothesis launches faster.
Align product and marketing
One metrics framework for all teams: fewer conflicts, faster execution.
How A/B testing works: 6 steps
A full cycle from diagnostics to rollout, with no gaps between analytics, design, and engineering.
Cases: how tests turn into profit
Short examples where data-backed hypotheses delivered measurable business impact.
E-commerce: checkout optimization
We rebuilt checkout structure and trust elements. Payment conversion increased by 19%.
Learn moreSaaS: trial activation
We tested new onboarding and value messaging. Activation improved by 24%.
Learn moreLead generation: request form
We reduced fields and reordered messaging. Qualified lead cost dropped by 27%.
Learn moreSubscription: pricing paywall
We retested plan order and CTA framing. Revenue per session grew by 14% in 6 weeks.
Learn moreWhat we test
We adapt the experimentation program to your product type and growth objective.
Landing pages and promo funnels
Increase lead and purchase conversion through offer, structure, and CTA testing.
- Offer, messaging, and value proposition
- Block order and trust proof sequence
- Forms, CTAs, and checkout steps
Online stores
Reduce drop-offs in product page, cart, and checkout to increase revenue from existing traffic.
- Product card and trust blocks
- Cart, delivery, and payment flow
- Pricing structure and bundle offers
SaaS and digital products
Improve activation, retention, and monetization with a structured product experimentation process.
- Onboarding, paywall, and trial journeys
- Feature flow experiments
- Billing screens, offers, and bundles
Packages, plans, and pricing
Validate pricing hypotheses safely on controlled segments to maximize conversion and margin.
- Plan grid and package order
- Value communication and constraints
- Urgency triggers and price anchoring
What you receive
Deliverables are prepared so your team can immediately execute and ship improvements.
Funnel map with loss points
Prioritized hypothesis backlog
4-8 week experiment roadmap
Launch protocols and QA checklist
Reports for each experiment
Winning changes rollout list
Insights and learnings repository
Recommendations for the next cycle
A/B testing packages
Choose the format based on traffic volume, release cadence, and team maturity.
Start
For rapid setup and first validated experiments
from 120,000 RUB
2-3 weeks
Included:
- Data and goal diagnostics
- 5-10 prioritized hypotheses
- 1 end-to-end A/B test
- 30-day improvement plan
Growth
Balanced monthly pace for conversion growth
from 190,000 RUB / month
2 tests per month
Included:
- Full cycle research -> hypothesis -> launch
- 2 A/B tests per month
- Winner analysis and rollout support
- Unified backlog and reporting
Scale
For teams with stable traffic and fast release cycles
from 320,000 RUB / month
4-6 tests per month
Included:
- Parallel tests across multiple funnels
- Advanced segmentation and prioritization
- Quarterly experimentation strategy
- Team enablement and ongoing support
Precision testing technology
We use a modern experimentation stack and predefined decision rules to reduce statistical and operational risk.
95%+
statistical confidence for final decisions
30%
average uplift on winning hypotheses
6-8 weeks
first full cycle from diagnostics to stable rollout
Platforms we use: