A/B Testing — Introduction and Implementation
It helps in splitting traffic 50/50 between a control and a variation. A/B split testing is a new term for an old technique that is basically controlled experimentation. When researchers have their testing on in terms of efficacy of new drugs, they make use of a ‘split test’. Most research experiments could be considered a ‘split test’, complete with a hypothesis, a control, a variation, and a statistically calculated result. For a Web Development Company in India, it is one of the tasks to get things tested and have its own positive results.
The main difference is the variability of internet traffic. In a particular lab, it is much easier to control for external variables. You can mitigate it when it is available online, but it is difficult to create a purely controlled test. In addition to all, testing new drugs requires an almost certain degree of accuracy. In technical terms, your period of ‘exploration’ can be greater, as you expect it to be without any sort of Type I error, that means false positive. The process for A/B split-testing considers business goals. It weighs risk vs, reward, exploration vs. exploitation, science vs. business.
You can easily create more than two variations. Tests with more than two variations that are known as A/B/n tests. If you have more than enough traffic, you can test as many variations you would like. A/B/n tests are great for implementing more variations of the same sort of hypothesis, but they still need more traffic so they can split it amongst more pages. A/B tests, while the other most popular, are just one type of online experiment.
How to prioritize A/B test hypothesis
There are many different frameworks to give your attention to when it comes to A/B tests. You can innovate with your own formula. Given below is a way to prioritize work shared. Once you go through all the steps, you will find issues, a few being worst, a few being minor.
1. Instrument
- It involves fixing, adding or improving certain tags/ events that handle in analytics.
2. Test
- This is the bucket where you place stuff for testing.
3. Hypothesize
- This is exactly where you would find a page. Widget or your process that is not working well but does not reveal a clear solution.
4. Investigate
- If any item is in the bucket, you will need to question or dip deeper.
A/B testing statistics
While analyzing A/B test results, statistical knowledge comes in hand. But the question arises, why do you need to know about statistics? Given below are three terms you should be aware of before diving into the notes of A/B testing statistics:
1. Variance
- What is the natural variability of a population? That affects the results and how it has to be used.
2. Sampling
- It can’t be measured when it comes to the true conversion rate, so you will need to select a sample that is representative.
3. Mean
- There is definitely no measuring of conversion rates, but just the samples. The average is representative of the whole.
A/B testing tools and resources
There are many tools for online experimentation. Some of the most popular ones are as follows.
- VWO
- Optimizely
- Maximiser
- Conductrics
- Adobe Target.
It is an invaluable resource to anyone making certain decisions in an online environment. With a little knowledge and a lot of diligence, you can mitigate many of the risks that most beginning optimizers face. If you are a Mobile App Development Company, you will surely need to get yourself up to date with A/B Testing.
