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Setting goals and planning an experiment
We identify key metrics and formulate hypotheses that need to be tested. We select the elements of the application for testing (interface, functionality, texts, scripts). We determine the target audience, the duration of the test and the tools for conducting it (Google Optimize, Optimizely, Firebase A/B Testing, etc.).
Preparing options and running the test
We create alternative versions of application elements (design, texts, scripts) based on a hypothesis. We are setting up analytics systems and distributing traffic between options to ensure correct data collection during the test.
Conducting an experiment and collecting data
We run the test, monitor the correctness of the scripts and the stability of the application. We capture user actions, behaviors, and metrics in real time.
Analysis of results and implementation of solutions
We compare the indicators of the variants and determine the statistical significance of the differences. We provide a report with conclusions and recommendations. If the result is positive, we implement the successful version into the main product, and if necessary, we launch new tests.
A/B testing is a method of comparing two or more variants of a product, interface, or individual element to determine which one gives the best results. The essence of the approach is that some users see option A, and the other part sees option B, after which the indicators are analyzed and the most effective solution is selected.
A/B testing is used when you need to increase conversion, improve user experience, test new functionality, or test a hypothesis before large-scale implementation. This approach allows you to make decisions based on data rather than subjective opinions.
Imagine that in a food delivery app, marketers want to increase the number of orders. They suggest replacing the standard "Checkout" button with a more visible one and changing its color. Users are randomly shown two versions of the button: the old one and the new one. According to the results of the experiment, it turns out that the new version increases the number of orders by 12%.
A/B testing is a tool that reduces the risk of introducing ineffective changes, saves the budget on improvements and helps the product develop in the right direction.
A/B testing checks which of two (or more) interface, functionality, or content options works more effectively. For example, the old version of a page and the new one are compared to determine which option increases conversion, retention, or other key metrics.
No, but automation helps you collect and analyze data faster, especially with high traffic volumes. For small experiments or unique scenarios, it is acceptable to perform the test manually, but analytical processing of the results is always mandatory.
Based on the analysis of current indicators, user behavior, results of previous tests or studies. The hypotheses describe the expected improvement: for example, "the new design of the product card will increase the number of clicks by 8%."
Regularly — when planning significant product changes, launching new features, redesigning, or changing marketing materials. Companies with an active audience can run tests on a monthly basis to continuously optimize their performance.
А/Б-тестирование — это эксперимент для выбора наиболее эффективного варианта на основе поведения пользователей и данных аналитики. Оно отвечает на вопрос: «Какой вариант работает лучше?». Смоук тестирование — это базовая проверка ключевых функций приложения после сборки или обновления, чтобы убедиться, что оно запускается и работает в принципе. Оно отвечает на вопрос: «Запускается ли продукт без критических ошибок?».