A/B-testing emails

A/B-testing emails is the methodical testing of hypotheses in mailings: formulating a hypothesis, choosing an element (subject/preheader/offer/CTA/content/time/sender), correct sample splitting, calculating the required sample size, running, assessing statistical significance, conclusions. The work: hypothesis, test design, sample, running, analysis. Honestly about 'a test ≠ a guarantee of growth', this is key: A/B tests give data for informed decisions and reduce guesswork, but do not guarantee metric growth; not every test finds a winner — often the difference is within the margin of error ('no significant difference' is also an honest result). 'We test — opens/sales will grow' = a lie. Honestly about 'a sample/significance is needed', this is critical: on a small base/with few sends the results are unrepresentative — it is easy to take random noise for a 'win'. We calculate the required sample and do not declare a winner before significance is reached; if the base is small — we will honestly say the test is premature. Honestly about 'open rate is distorted': testing by opens is unreliable (Apple Mail Privacy, etc.); we rely on clicks/actions/conversions. Honestly about 'test cleanliness': changing several elements at once distorts the result — we test one significant factor at a time. Honestly about 'it does not replace the offer/product': A/B improves email elements but will not pull up a weak offer. Honestly about the effect: it designs and runs correct A/B tests of emails 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; open rate is distorted; it does not replace the offer. Honestly about access: a sufficient base, an ESP with A/B, options for the test are needed. An important boundary: this is email A/B; ad A/B — 465; templates/coding — 553; campaigns — 543. The base price starts from 15,000 ₽ for test design/running/analysis.

• Mailing decisions on guesswork, without correct tests.
• A misconception that any A/B test guarantees growth of opens/sales.
• Conclusions on a small sample (random noise taken for a 'win').
• Testing by opens (open rate is distorted); changing several elements at once.

• Formulating a hypothesis and choosing the tested element (subject/CTA/time, etc.)
• Correct sample splitting and required size calculation
• Running, statistical significance assessment, honest conclusions (including 'no difference')
• Analysis by clicks/actions (accounting for open rate distortion)
• An honest assessment: is there enough base for a representative test
• Honest boundaries (a test ≠ a guarantee of growth; not every test gives a winner; a sample/significance is needed; open rate is distorted; it does not replace the offer)
• Documentation and handover
• Review with you

• Correctly run A/B tests of emails with significance assessment
• Reliable data for decisions (less guesswork)
• An honest assessment: what the test showed and what depends on the offer/product
• Honest boundaries (not a guarantee of growth; a sample is needed; open rate is distorted)

• We formulate a hypothesis, design the test, calculate the sample
• We run with correct splitting, assess significance by clicks/actions
• We honestly set boundaries (a test ≠ growth; '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 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 honestly run the test and show what it actually showed, without guarantees.

• Can it be tested on a small base?

  Unreliably, honestly: on a small base/with few sends the results are unrepresentative — it is easy to take random noise for a 'win'. We calculate the required sample and do not declare a winner before significance; with a small base we will honestly say the test is premature.

• Do we test by open rate?

  With caution, honestly: open rate is distorted (Apple Mail Privacy opens emails automatically), so a test by opens alone is unreliable. We rely on clicks/actions/conversions — that more honestly reflects the result.