A/B testing platforms

A/B testing is checking hypotheses on real users: half see variant A, half B, and we see where conversion is higher. We connect an experimentation platform (VWO, Optimizely, Yandex Experiments, etc.), set up tests of pages, buttons, headlines, forms and scenarios, goals and correct collection, anti-flicker protection and analysis of results with statistical significance. Honestly about the main point: A/B is about statistics, not magic. For a reliable result you need enough traffic and conversions: on low traffic a test either runs for months or gives no significant conclusion — then it is more honest to grow traffic first. Not every test "wins": a significant share of hypotheses show no difference or even a decline — that is a normal part of the process, not a failure, and we do not promise "every test raises conversion". The result is probabilistic: with incorrect duration or peeking at the test it is easy to mistake randomness for an effect, so we run tests by the rules (duration, sample size, significance). A/B shows the relative difference between variants, not the absolute truth about users. Accuracy depends on correct goal markup: part of the traffic is not tracked (ad-blockers, cookie rejection, technical failures), and on small numbers the noise is higher; if the site or goals are changed mid-test, the result can be broken, so we run the test carefully. Free platform tiers have limits on users, tests and features — exceeding them means upgrading or capping data. Foreign platforms (VWO/Optimizely) store data abroad — for the RF that is a residency question, we pick with that in mind. And without clear hypotheses and goals a test is meaningless — we do not run it for the sake of process, better to start with analytics. The platform is a subscription, the tariff depends on traffic and features and is your cost; our price is for setting up and running tests. Hypotheses and variant design are needed: deciding what to test and preparing mockups is joint work, we help with prioritization. If traffic is low or there are no clear hypotheses, A/B is premature — start with analytics to find the problem spots (see "Analytics integrations"); we will honestly say so. Picture this: you argue about which button is better — instead of guessing you run a test, and in two weeks the data shows one variant gives a noticeable share more leads; the decision is made by fact, not by taste. The base price starts from 35,000 ₽; it depends on the number of tests, the platform and your system.

• You make site decisions by taste, without testing on users.
• You change pages "blind" and do not know if it got better or worse.
• The team argues about buttons and headlines without data.
• There is no process for checking hypotheses and assessing the result correctly.

• Connecting an experimentation platform (VWO / Optimizely / Yandex Experiments)
• Setting up tests: pages, buttons, headlines, forms, scenarios
• Goals, correct collection and anti-flicker protection
• Sample-size and test-duration calculation
• Analysis of results with statistical significance
• Help with hypothesis prioritization
• An honest assessment of whether there is enough traffic for reliable tests
• Documentation of tests and conclusions

• Site decisions are made on data, not on taste
• You see which variant is really better (or that there is no difference)
• Tests are run correctly: duration, sample, significance
• It is clear where the tech ends and your traffic, hypotheses and design begin

• We clarify the hypotheses, goals and assess traffic sufficiency
• We set up the platform, variants, goals and anti-flicker protection
• We run the test for the needed period, analyze significance, record the conclusion

• Will any test raise conversion?

  No, and promising that would be dishonest. A significant share of hypotheses show no difference or even a decline — that is a normal part of the process. A/B helps select working changes and weed out harmful ones, but "every test is growth" does not happen. The value is that you stop changing the site blind.

• How much traffic is needed?

  Enough to reach statistical significance in a reasonable time. On low traffic a test runs very long or gives no reliable conclusion — then it is more honest to grow traffic first and start with analytics. Before launch we calculate the needed sample size and duration and say whether it is realistic in your case.

• How much does it cost?

  The experimentation platform is a subscription, its tariff depends on traffic and features — that is your cost; our price is for setting up and correctly running tests. Some platforms have free limits — we will suggest what fits your volume.