![\"split](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2020\/10\/10-Split-Testing.png\")
<\/figure>\n\n\n\nEnter split testing.<\/strong><\/p>\n\n\n\n Unlike other marketing techniques, split testing, otherwise called A\/B testing<\/a>, helps experience optimizers focus their energies and dollars in the right direction. It typically enables them to experiment with other marketing campaigns or strategies in real-time. <\/p>\n\n\n\n If you are an experienced optimizer, you can seek your target audience\u2019s opinion by showing them old and new versions of a website page or element you wish to change through split testing. You can also compare the experiment results to see which version shows better improvement and use the gathered data to make informed decisions on whether or not to go ahead and make the changes. <\/p>\n\n\n\n Split testing is, therefore, the method of conducting controlled, randomized experiments with the core objective of improving a website metric. These could be overall business conversions, click-through rates, number of purchases, form completions, and more. <\/p>\n\n\n\n Let\u2019s get in-depth to fully understand the concept of split testing, its scope, how you can start implementing it, and the tools that aid you in the process. <\/p>\n\n\n The objective of split testing is to identify how a change to a web page, advertising campaign, or email will increase or decrease the chances of an outcome. For instance, let\u2019s say you\u2019re the owner of an online homemade chocolate venture. Your monthly website traffic is 50,000 visitors. As a marketing-savvy owner, the hypothesis you want to test is that customers are more likely to make a purchase when enticed with additional discounts or deals on all products, despite your brand being a renowned name in the market. To get certainty, you plan to run a split test. <\/p>\n\n\n\n You create a new version of your homepage banner, mentioning additional discounts that all visitors can avail on all products. The original homepage banner simply illustrates new products introduced in the current month with regular launch offers. <\/p>\n\n\n\n You float the test and make each version visible to equal traffic. 50% of the visitors see the old version (control), and 50% see the new version (variation). Running the test for a couple of weeks, you witness that your hypothesis was correct. People who saw the variation version of your test bought more chocolates from your website than those who saw your test\u2019s control version.<\/p>\n\n\n\n The outcome? Split testing provides definitive conclusions about specific elements of your web page. It offers the swiftest way to find whether or not your planned business strategies or campaigns will showcase a positive result and help move the needle upward. Additionally, split testing also helps save time, money, and effort and gives results in a shorter timespan. You can see this for yourself by taking a 30-day all-inclusive free trial with VWO<\/a>. <\/p>\n\n\n Split testing<\/a> software works by randomly dividing your website traffic between two or more versions of a page, tracking how each version performs against a defined conversion goal, such as purchases, form completions, or click-throughs, and surfacing results that help you make informed decisions with confidence.<\/p>\n\n\n\n When you set up a test, the tool embeds a lightweight JavaScript snippet on your website. Every time a visitor lands on the page being tested, this snippet fires and assigns the visitor to either the control (the original version) or a variation, consistently, so the same visitor always sees the same version throughout their session. <\/p>\n\n\n\n This is where a well-built platform makes a real difference. VWO’s testing dashboard<\/a> gives you real-time visibility into every experiment, showing visitor counts, variation performance, and conversion data as they come in. From one place, you can:<\/p>\n\n\n\n If you\u2019re planning to run a split test on your site, there are a couple of logical places to start. Ideally, you should begin testing pages that add the highest value to your business.<\/p>\n\n\n\n Once you\u2019ve identified these pages, deep dive into the page elements that you\u2019d like to experiment with, which can impact your key metric. Typical page elements include, but are not limited to:<\/p>\n\n\n\n The example below from Ben, a Netherlands-based personal budget challenger of the Dutch telecom industry, nicely illustrates how a small change in a page\u2019s element<\/a> can significantly increase the conversion rate. The company ran a split test on its product pages to make the phone color prominently visible to the page visitors. The execs at the organization found that most people visiting the product pages were unaware that they could choose phone colors along with selecting the best data and voice plans for themselves. The data collected through VWO Insights<\/a> also revealed that people did not notice the color palette given below the mobile image or couldn\u2019t understand the palette\u2019s basic function. <\/p>\n\n\n\n To make the section visible and selection easy, the execs at Ben shifted the color palette from below the mobile image and placed it adjacent to the product image. <\/p>\n\n\n\n Here\u2019s how the control and the variation look: <\/p>\n\n\n\n After running this test for about two weeks, the execs found that by merely making a slight change on their product page, they were able to increase their conversions by 17.63% and significantly reduce customer calls to change device colors as well.<\/p>\n\n\n\n Numerous other page elements<\/a> can be tested that may increase the conversion rate.<\/p>\n\n\n\n What\u2019s great about split testing is that it\u2019s a universally applicable experimentation method. The experience optimizers can use this capability to examine any assumption or theory they or their colleagues come across, without any hesitation. Be it an eCommerce site, a social media campaign, an ad campaign, and so on, everything can be tested using the split testing methodology. <\/p>\n\n\n Before moving ahead to learn about how to run a split test, it\u2019s essential to understand the statistical modeling on which all experiments run. <\/p>\n\n\n\n All split testing tools either work on the Frequentist or the Bayesian<\/a> statistical model. In the case of the Frequentist model, the conclusion of an event is drawn based on how many times the outcome has occurred through the course of the experiment. A test using the Frequentist approach requires a larger sample size to ensure it shows a statistically significant conclusion. In short, you have to collect data in abundance. For a page or website with low traffic, this approach would require a lengthy testing phase.<\/p>\n\n\n\n On the other hand, the Bayesian statistical model is based on the likelihood of an outcome occurring. You don\u2019t need the same number of visitors to predict likelihood. It\u2019s a fundamental difference. Tests conducted on the Bayesian model can be run for a shorter time (as low as 50%) than the Frequentist model and get actionable findings for low traffic pages faster. Hence, the Bayesian model makes more sense and proves more useful for all types and sizes of campaign and strategy experimentations. We, at VWO, also use and advocate the Bayesian model over the Frequentist<\/a>. <\/p>\n\n\n\n Download Free: A\/B Testing Guide<\/strong><\/span><\/a><\/em><\/p>\n\n\n Split testing and multivariate testing platforms often confuse which does what. Here’s a quick comparison to clear that up.<\/p>\n\n\n\nSimplifying the concept of split testing<\/strong><\/strong><\/h2>\n\n\n
How does split testing software work?<\/strong><\/h2>\n\n\n
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![\"Split](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2020\/10\/SplitURLDashboard.jpg\")
Which pages or page elements should you split test?<\/strong><\/h2>\n\n\n
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![\"control](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2020\/10\/Ben-Case-study.jpg\")
Image Source: Ben<\/a>
<\/figcaption><\/figure>\n\n\n\nStatistical modeling: Bayesian vs. Frequentist approach<\/strong><\/h2>\n\n\n
Split testing vs. multivariate testing platforms<\/h2>\n\n\n
![\"Blog](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2021\/09\/Blog-Banner_A-Practical-Guide-to-Split-Testing.png\")
<\/a><\/figure>\n\n\n![\"how](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2020\/10\/Screenshot-2020-10-01-at-2.30.57-PM.png\")