You Ran the Tests. You Still Can't Prove What Worked.
You ship the redesigns because they worked somewhere else, because a benchmark suggested it, or because the strongest opinion in the room won. Tests run. Dashboards fill. But six months later, the business still can't prove which changes improved customer experience or moved revenue.The gap isn't the experimentation tool. The gap is the disciplined approach to turning experiments into evidence.
ONE CX™ builds UX Experimentation as a business discipline. Every change starts with a clear hypothesis tied to a measurable outcome and is tested to reach a conclusive answer, not just an interesting observation.
Evidence identifies the winner and retires the loser. Every learning is captured and carried forward, so every sprint begins with more intelligence than the last.
Proof compounds. Opinion resets.
We Prove What Works. Then We Scale It.
From hypothesis to experiment to insight, we build and operate the complete experimentation system, delivering answers, not just reports.
SUD Life, a regulated life insurer
Turned one journey into five persona-specific journeys, tested and measured on leads, with the winner rolled out on proof, not the loud
Revolt, a hyperlocal EV brand
Turned a leaking hyperlocal funnel into a continuously tested one, rolling out proven winners at scale across journeys.
Leading Telecom Brand
Turned isolated test wins into a growth loop, lifting retention, growing referral and easing CAC, each sprint compounding on the last.
We Prove What Works. Then We Scale It.
From hypothesis to experiment to insight, we build and operate the complete experimentation system, delivering answers, not just reports.
"We don't deliver test plans.
We operate experimentation programmes.
Across rStorefront, SUD Life, Religare, and Voyze, we find the journeys and components that impact outcomes, validate changes through evidence, and scale what works.
The advantage compounds over time: every experiment creates institutional knowledge, so each sprint starts smarter than the last."
- — Annu, Senior Product Designer, ONE CX™
Experimentation Stack We Run On
Experimentation Stack We Run On
Our deepest expertise is with Statsig, but the right approach is driven by your traffic, data maturity, and business questions, not the tool of our choice.
Experimentation Platforms
Statsig (An Open AI Company)
Our Expertise built on a partnership: warehouse-native experimentation and feature gating with a serious stats engine, the platform we stand up fastest and run hardest.
Optimizely
VWO
Eppo (A Data Dog Company)
Statistical Methodology
Bayesian. Frequentist. Hybrid
Chosen per experiment: Bayesian for a fast directional read, frequentist when the board wants a hard significance threshold.
Sequential testing
Multi-armed bandit
Why India's Leaders Choose ONE CX™ for Experimentation
01
Conclusive, Not Ambiguous
Inconclusive experiments are usually a design problem, not a testing problem. We apply power analysis before launch, sequential testing, guardrail metrics, and sample-ratio checks to ensure every experiment is built to deliver a decision. Not an "interesting" observation.
Conclusive, Not Ambiguous
Inconclusive experiments are usually a design problem, not a testing problem. We apply power analysis before launch, sequential testing, guardrail metrics, and sample-ratio checks to ensure every experiment is built to deliver a decision. Not an "interesting" observation.
02
Driven by Evidence, Not Hierarchy
Every experiment starts with a registered hypothesis and success metric before a change is shipped. When opinions and data disagree, the evidence decide, removing bias from critical product decisions.
Driven by Evidence, Not Hierarchy
Every experiment starts with a registered hypothesis and success metric before a change is shipped. When opinions and data disagree, the evidence decide, removing bias from critical product decisions.
03
Proof Compounds Sprint to Sprint
Every test is captured with its hypothesis, result, and learning. This creates a growing knowledge base where the tenth experiment builds on the first nine instead of repeating them. The advantage compounds with every sprint.
Proof Compounds Sprint to Sprint
Every test is captured with its hypothesis, result, and learning. This creates a growing knowledge base where the tenth experiment builds on the first nine instead of repeating them. The advantage compounds with every sprint.
04
Built for India's Reality, Not Imported Benchmarks
A test sized for a high-traffic US app often fails to conclude on Indian journeys with lower volume and higher variance. We apply variance reduction and Bayesian reads to achieve reliable decisions across real Indian traffic, mid-range Android and vernacular variants included.
Built for India's Reality, Not Imported Benchmarks
A test sized for a high-traffic US app often fails to conclude on Indian journeys with lower volume and higher variance. We apply variance reduction and Bayesian reads to achieve reliable decisions across real Indian traffic, mid-range Android and vernacular variants included.
05
We Operate It, You Own the Proof
The same practitioners who design your experiments run and analyse them, no junior teams learning on your customers. Your experimentation backlog, frameworks, and validated learnings remain yours, continuously building institutional knowledge.
We Operate It, You Own the Proof
The same practitioners who design your experiments run and analyse them, no junior teams learning on your customers. Your experimentation backlog, frameworks, and validated learnings remain yours, continuously building institutional knowledge.
What Product and Growth Leaders Ask ONE CX™ About Experimentation
What does ONE CX do in an experimentation engagement?
We own the end-to-end experimentation lifecycle: hypothesis definition, metric selection, experiment design, statistical validation, rollout decisions, and learning capture. Every test is designed to reach a conclusive outcome, with winners scaled, losers retired, and insights compounding sprint after sprint.
We already run A/B tests. What do you add?
Most teams can run a test. The challenge is running one that reaches a reliable decision. We bring the discipline behind experimentation: correctly sized tests, validated metrics, statistical guardrails, clear ownership, and a knowledge base that compounds over time. With power analysis, sequential testing, and guardrail frameworks, every experiment moves from "interesting result" to an actionable decision, what to scale, what to stop, and what to test next.
How does an engagement work, and what's in a 90-day sprint?
We begin with a 30-minute no commitment teardown. To identify where experimentation can create measurable impact. Within the first 90 days, we establish the foundation: audit the current state, prioritise the opportunity backlog, set the statistical framework, and launch live experiments. The outcome is not a test report, it is a repeatable experimentation system with proven learnings and a clear path forward. The same team that scopes the programme operates it, ensuring every sprint starts with more evidence than the last.
Is experimentation a design discipline, a product one, or analytics?
It's a design and product discipline that runs on analytics. The experiment decides which design or product change ships; analytics only measures it. That's why it sits under Experience Design, the output is a proven experience, not a dashboard.
How is experimentation different from good UX design?
UX design creates the change; experimentation proves which change actually moved the number, and retires the ones that didn't. They're partners: design without testing is a confident guess, testing without design has nothing worth shipping.
How should a brand choose an experimentation or design partner in India?
Ask 5 Things as part of your evalution. 1. Do they design tests to conclude, or just run them? 2. Do they measure their design against revenue, or engagement? 3. Do they keep what each test proves? 4. Do they design for low Indian traffic and mid-range devices? 5. Do the seniors who pitch actually do the work?
Why do most experiments in Indian enterprises come back inconclusive?
Usually for two reasons. Experiment design: Insufficient sample sizing, premature stopping, weak hypothesis definition, or misaligned metrics prevent statistical confidence. Measurement architecture: Fragmented events, identities, and customer signals across platforms make reliable measurement difficult. We address the first through rigorous experimentation design and work around the second by building measurement approaches that reflect the reality of enterprise data ecosystems.
What is conversion rate optimisation (CRO)?
CRO is the practice of systematically raising the share of users who complete a goal, purchase, sign-up, KYC, by finding where they drop off, testing fixes, and keeping what works. Done properly it's a continuous cycle, not a one-time audit.
How much does an experimentation or CRO engagement cost?
It depends on scope: a single funnel, a full experimentation programme and a growth-loop build are different engagements, so we scope to the outcome rather than quote a flat rate. Most start with a 30-minute teardown that sizes the work before any commitment.
How do you handle consent and DPDP when experimenting on real users?
Experiments run on consented traffic only, and we treat the test infrastructure as in-scope for DPDP from the start. Variant assignment, event capture and any user identifiers follow the same consent and data-minimisation rules as the rest of your stack no separate, ungoverned experimentation data trail. The record we keep is of hypotheses and results, not unnecessary personal data.
What is a growth loop, and how is it different from CRO?
CRO optimises linear journeys: traffic → interaction → conversion. It finds leaks and improves the efficiency of the funnel. Growth loops optimise compounding systems: an action by one user creates value that drives acquisition, engagement, or retention from the next. CRO recovers existing opportunity. Growth loops create repeatable growth mechanisms. We design, instrument, and optimise these loops through continuous experimentation, turning isolated wins into systems that compound over time.
How is UX experimentation different from Product Optimisation?
Product Optimisation makes the product run better. UX Experimentation makes the product perform better for customers. Optimisation focuses on the underlying system, speed, stability, efficiency, and technical health. Experimentation focuses on experience decisions, what changes, why they matter, and whether they improve outcomes. When technical improvements are expected to influence behaviour, they enter the experimentation framework and earn impact through evidence. One tunes the engine. The other proves which direction to drive.
Proof compounds. Opinion resets. Build on the one that lasts. No commitment. 30 minutes. One clear next step.