A/B testing ads

A/B testing ads is the methodical testing of hypotheses in advertising: formulating hypotheses, test design (what we change — creative/headline/offer/audience/landing; what we measure), correct traffic splitting, calculating the required sample/duration, running, assessing statistical significance, conclusions and iterations. The work: prioritizing hypotheses, setting up the test, controlling cleanliness, analysis, recommendations. Honestly about 'a test ≠ a guarantee of growth', this is key: A/B tests give data for informed decisions and reduce the share of guesswork, but they do not guarantee growth. Not every test finds a winner — often the difference is within the margin of error, and that is also an honest result ('no significant difference'). 'We will test — and metrics will grow' = a lie. Honestly about 'a sample and significance are needed', this is critical: on little traffic/a short period the results are unreliable — it is easy to take random noise for a 'win' and draw a false conclusion. We honestly calculate the required sample and do not declare a winner before significance is reached. If there is little traffic — we will honestly say the test is unrepresentative. Honestly about 'test cleanliness': changing several factors at once, audience overlap, different periods distort the result; we test correctly (one significant factor at a time where possible). Honestly about 'local optimization': A/B improves elements but does not replace strategy/offer — endless button tests will not save a weak product. Honestly about 'the auction/platform decides': external factors (season, competition, algorithm learning) affect the result — we account for them. Honestly about the effect: it designs and runs correct A/B tests with sample calculation and significance assessment, giving reliable data for decisions, but this is not a guarantee of growth; not every test gives a winner; a sufficient sample is needed; it does not replace strategy. Honestly about access: sufficient traffic/budget, access to accounts/analytics, options for the test are needed. An important boundary: this is A/B testing of advertising; producing options — 464; conversion tracking — 460; landing CRO — adjacent; strategy — separate. The base price starts from 18,000 ₽ for test design/running/analysis.

• Ad decisions are made on guesswork, without correct tests.
• A misconception that any A/B test guarantees metric growth.
• Conclusions on a small sample/short period — random noise taken for a 'win'.
• A dirty test (several factors at once, audience overlap) — an unreliable result.

• Prioritizing and formulating hypotheses for the task
• Test design (what we change/measure), sample and duration calculation
• Correct traffic splitting and test cleanliness control
• Running, statistical significance assessment, honest conclusions (including 'no difference')
• An honest assessment: is there enough traffic for a representative test
• Honest boundaries (a test ≠ a guarantee of growth; not every test gives a winner; a sample/significance are needed; does not replace strategy; external factors)
• Documentation and handover
• Review with you

• Correctly run A/B tests with significance assessment and honest conclusions
• Reliable data for decisions (less guesswork) and prioritized hypotheses
• An honest assessment: what testing gives and what depends on the strategy/offer/traffic
• Honest boundaries (not a guarantee of growth; a sample/significance are needed; does not replace strategy)

• We prioritize hypotheses, design the test, calculate the sample
• We launch with a correct split, control cleanliness, assess significance
• We honestly set boundaries (a test ≠ growth; a sample is needed; 'no difference' is also a result) and report

• Will we test — and metrics will definitely grow?

  Not necessarily, honestly: A/B tests give data for decisions and reduce guesswork, but they do not guarantee growth. Not every test finds a winner — often the difference is within the margin of error, and 'no significant difference' is also an honest result. 'We test — and it will grow' is a deception. We will honestly run the test and honestly show what it actually showed.

• Can it be done quickly on small traffic?

  Unreliably, honestly: on little traffic/a short period the results are unrepresentative — it is easy to take random noise for a 'win' and draw a false conclusion. We honestly calculate the required sample and do not declare a winner before significance is reached. If there is little traffic — we will honestly say the test is premature.

• Shall we test everything to the maximum?

  With prioritization, honestly: A/B improves elements but does not replace strategy and the offer — endless button tests will not save a weak product or offer. We honestly prioritize hypotheses by potential impact and will say if the problem is not in the tested elements but in the strategy/offer.