Dec. 19, 2018, 1:38 p.m.
What is A/B testing in marketing?
Dec. 19, 2018, 1:39 p.m.
As you know, in business there are no static states. The company must constantly evolve to meet the current market situation, the needs of customers and owners. Having stopped development, the project starts to deteriorate at the same second. For example, you cannot create an online store, add 200 products to the site and make a monthly profit of 100 thousand rubles. In order for the project’s profitability to at least not fall, the entrepreneur needs to constantly expand the product range, increase the audience’s reach by advertising and publishing useful content, and improve the website’s behavioral metrics and conversion rate. One of the tools for the development of web projects is A / B testing. This method allows you to measure audience preferences and influence key performance indicators of a site, including conversions, users' time on the page, average order amount, bounce rate, and other metrics. In this article, you will learn how to properly conduct A / B testing. A / B testing is a marketing method used to evaluate and manage the performance of a web page. This method is also called split testing (from the English. Split testing - separate testing). A / B testing allows you to evaluate the quantitative performance of the two options on a web page, as well as compare them with each other. Split testing also helps to assess the effectiveness of page changes, for example, adding new design elements or calls to action. The practical meaning of using this method is to search for and implement components of a page that increase its effectiveness. Notice again, A / B testing is an applied marketing method by which you can influence conversion, drive sales, and increase the profitability of a web project. Split testing begins with evaluating the metrics of an existing web page (A, test page) and finding ways to improve it. For example, imagine the landing page of an online store with a conversion rate of 2%. The marketer wants to increase this figure to 4%, so he plans changes that will help solve this problem. Suppose a specialist assumes that by changing the color of the conversion button from neutral blue to aggressive red, it will make it more visible. To check whether this will lead to an increase in sales and conversion, the marketer creates an improved version of the web page (B, new page). Using split-testing tools, the expert randomly splits the traffic between pages A and B into two approximately equal parts. Relatively speaking, half of the visitors get to page A, and the second half to page B. In this case, the marketer keeps in mind the sources of traffic. To ensure the validity and objectivity of testing, you need to direct to pages A and B 50% of visitors who come to the site from social networks, natural search, contextual advertising, etc. Having gathered enough information, the marketer assesses the test results. As stated above, page A conversion rate is 2%. If on page B this indicator was 2.5%, then a change in the conversion button from blue to red really increased the effectiveness of landing. However, the conversion rate did not reach the desired 4%. Therefore, the marketer is further looking for ways to improve the page using A / B testing. In this case, a page with a red conversion button will act as a control.