A/B testing setup and experiments
We set up A/B testing on your site and run experiments: we frame hypotheses, split traffic, launch variants, compute the result with statistical significance. So decisions are made on data, not taste. Honestly upfront: we test hypotheses but do NOT guarantee a conversion increase — some tests give no lift or a negative result, and that is a normal, useful part of the process.
A/B testing setup and experiments — overview
A/B testing setup and experiments is a project (often turning into an ongoing testing program): we connect an experiment tool, set up correct traffic splitting and goals, help frame and prioritize hypotheses, launch variants and analyze the result — lift with a confidence interval and significance, impact on guardrail metrics. Honestly about the result, this is key: an A/B test is a way to TEST a hypothesis, not a guarantee of improvement; we do not promise '+X% conversion' — the outcome depends on the hypothesis, audience, offer and baseline; some tests will show no difference or a worsening, and that is a valid result (it saves you money on a bad idea), not a failure. Honestly about statistics: enough traffic and duration are needed for significance; you cannot 'peek' and stop a test on a random spike — that gives false conclusions; on low traffic a correct test may be impossible — we say so honestly. Honestly about data: correct analytics and goal tagging are needed (garbage in, garbage out). Honestly about the essence: this is testing and measurement, NOT rolling out the winner itself and NOT guaranteed growth — rolling out the winning variant and developing the product is decided by you (implementation we can do separately). Honestly about the platform: A/B tools (if paid) are billed directly, separately. An important boundary: this is running tests, not monitoring results of already-running tests (a separate analytics service), not designing/building variants from scratch (we can do it separately) and not a guarantee of rankings/sales. Picture this: instead of 'arguing which button is better' you get a data answer — or an honest 'no difference, let's not waste effort here'. The base price starts from 25,000 ₽ for setup and the first cycle; it depends on the number of tests and complexity.
Problems we solve
- You make site decisions by taste, without validation.
- You do not know which variant actually works better.
- You stop tests early and draw wrong conclusions.
- There is no hypothesis prioritization and correct significance.
What's included in the A/B testing setup and experiments service
- Connecting an A/B tool and correct traffic splitting
- Setting up goals and guardrail metrics
- Help framing and prioritizing hypotheses
- Launching variants and controlling test cleanliness
- Analyzing lift with a confidence interval and significance
- An honest conclusion: winner / no difference / worse
- A report and a decision recommendation
- Reviewing results with you
What you get
- Decisions on data, not taste
- You know what works and what does not (including 'no difference')
- Correct significance without early 'peeking'
- A base for rollout (implementation and growth — separately)
How the work goes: steps
- We clarify goals, hypotheses, traffic; collect access to the site and analytics
- We set up the tool, goals, splitting; launch the test
- We analyze significance, give an honest conclusion and recommendation
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Do you guarantee a conversion increase from tests?
No. An A/B test validates a hypothesis, it does not guarantee improvement. Some tests will show no difference or a worsening — a valid result that saves money on a bad idea. We honestly show the outcome rather than promising '+X%'.
My traffic is small — can I test?
Significance needs enough traffic and duration. On low traffic a correct A/B may be impossible or very slow — we say so honestly and suggest alternatives (qualitative research, sequential changes).
Why can't I stop the test when a leader is already visible?
Early 'peeking' and stopping on a random spike inflate false conclusions — the 'leader' may be chance. Predefined sample size and duration are needed; we watch for that.
About the provider
The «A/B testing setup and experiments» service is provided by PDV Expert — a team specialising in «Conversion & analytics». We work under contract and deliver a written report with recommendations.