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A/B testing is a marketing research method in which two versions (A and B) of an advertisement, landing page, or website element are compared to determine which is more effective.
Half of the audience sees version A (control group), the other half sees version B (experimental group). Based on the test results, it is estimated which version solves the problem better - attracts more clicks, generates more leads, sales.
A/B testing as a website optimization method appeared in the early 2000s. Among the first to use it were such giants as Google and Amazon. Then the method spread to testing advertisements, email newsletters, and other elements of digital marketing. Today, A/B testing is used by marketers all over the world, from small online stores to large international corporations.

Why A/B Testing Is Important for Advertising Campaigns


Regular A/B testing allows you to constantly improve the performance of advertising campaigns and save budgets. The advantages of the tool also include:

increasing CTR (click-through rate) of ads;
reducing the cost per click and increasing the conversion rate of landing pages through optimization of shopify website design texts, design and usability;
improving the quality of traffic and target actions on the site by testing various USPs and offers;
budget savings by redistributing funds to the most effective elements and disabling ineffective options.


Preparing for A/B testing


Before launching A/B testing, you need to do thorough preparation. Clear planning will help you get reliable results and avoid typical mistakes.
Step-by-step algorithm for preparing for testing:



Define the test objective and key metrics. Answer the question: what do you want to optimize — CTR, conversion, or average check? Record the current indicators so that you have something to compare the results with.
Select an element to test (headline, ad copy, image, landing page lead capture form). Start with the elements that are expected to give the biggest boost.
Formulate a hypothesis — a guess about how changing an element will affect the target metric. For example: “Replacing a generic headline with a personalized one will increase CTR by 5%.”
Create test variants. Leave control variant A unchanged, and make one significant change to experimental variant B that you want to test. If there are two, three, or more changes, you won’t know which one worked.


Practical task:
Make a list of 5 website elements or ads that you want to test first. For each element, formulate a hypothesis - what change, in your opinion, will improve conversion.

The A/B Testing Process


Once the preparatory stage is completed, proceed directly to conducting the A/B test:

Set up an equal split of traffic between the control and test groups in a 50/50 ratio. Be sure to use randomization to eliminate the influence of external factors on the reliability of the results.
Determine the required test duration. Advertising requires 1-2 weeks, email newsletters - a few days. Use A/B test duration calculators to estimate the time frame.
Launch the test and monitor its progress. Make sure that traffic is distributed evenly and that the test groups are comparable in volume and quality.
Stop the test after the scheduled time or when statistical significance is reached. Do not stop or change the test prematurely, even if one of the variants shows high results in the first days.
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