A/B testing is a tool that is part of the conversion optimisation strategy, which helps statistically validate hypotheses and understand visitor behaviour.
It is important to specify that an A/B testing strategy must be informed by data from other tools that will bring more visitor information to define hypotheses to be tested.
The key to an A/B testing strategy’s success lies in identifying strong hypotheses that can positively impact conversion rates. Although testing by chance, without really justifying the hypotheses being tested, can be warranted when learning how to use the software, this practice must quickly be replaced by strategy with more solid foundations. Having good A/B testing solution is necessary, but not always sufficient if conversion practices are complex.
A/B testing is method that consists in submitting several versions of web page to sample of consumers and measuring the precise performance of each page in real indicators such as visitor engagement or purchasing behaviour.
A/B testing is quick, low-cost way of collecting data, and is carried out on large sample with little bias, as the visitors aren’t aware of the test. This scientific method brings data back to the heart of decision making, putting personal opinions and presumptions on the back burner and speeding up decision-making.
Used in addition to visitor behaviour analysis software, A/B testing helps solve problems that are brought to light. We no longer content ourselves with identifying problem: through A/B testing we also verify if the hypothesis used to solve it works or not, we measure its impact on personalised indicators (KPIs) such as percentage improvement in purchase rate, the rate at which product is added to baskets, the percentage that views particular page, bounce rate, average basket amount, etc.
A/B testing isn’t just for e-commerce websites. Media sites, for example, are increasingly conscious of the A/B testing’s impact, even if it’s more difficult to quantify gains than for an e-commerce site.
AB Tasty is the essential SaaS (Software as Service) solution for A/B testing. Where certain A/B testing tools require complex implementation with intervention by technical teams to modify the source code of pages to be tested, AB Tasty’s solution helps marketing and e-business teams launch tests and modify their site’s pages themselves in WYSIWYG (What You See Is What You Get) editor.
Its graphical editor lets users modify site’s pages without technical skills and track business indicators specific to each site. It offers reporting software that indicates the number of conversions registered for each variation, conversion rates, % improvement compared to the original and statistical reliability indicator for each variation, among other things.
Users of AB Tasty’s A/B testing tool can quickly make their optimisation ideas reality and increase their execution speed in creating and launching tests that improve user click streams and their profitability.
Defining rigorous methodological framework is the best way to get concrete results from an A/B testing programme. Several steps must be respected.
Before anything else, you must define your objectives and your expectations. There is no point in defining unachievable objectives that will only lead to disappointment. Success is declared when an A/B test produces positive effect on visitor engagement, even if they don’t directly convert. If we only measure overall conversion (macro conversion), many A/B tests will fail. change can very well have no impact on the overall conversion rate but positively impact micro conversions, such as adding to basket, which are steps towards macro conversion.
It is important to prioritise and segment your A/B tests. You must prioritise the most strategic parts of your website like the homepage, category titles and the size of blocks. You will get faster results since more visitors will be included in tests. But in certain cases, performing an A/B test on all of website’s visitors makes no sense and can give false results. There is no point in performing an A/B test that tries to increase the number of site registrations on members who are already registered.
Before you are able to analyse your A/B test results, you must reach statistical confidence level of 95% so that the odds that any differences in results between variations being due to chance are very small.
Once one of the variations outperforms the original with certitude, it’s time to put the winning variation into production to confirm the gains observed. While the changes are being put into production, AB Tasty’s A/B testing solution lets you show the winning variation to 100% of visitors so that you don’t miss out on any gains.
Finally, always be testing since A/B testing is continuous optimisation process. At the end of each A/B test, conclusions are reached and then feed into new A/B testing hypotheses to fill out the roadmap. Efforts will bear fruit over the long term: the first A/B tests certainly won’t produce the desired effects, since building expertise takes time.