What is A/B Testing?
In our academia, all of us have been given tests. What happened after that? The test copies are compared and then the best one grabs the top position. This A/B testing also has some similarities with traditional classroom testing. Let’s get into its depth!
A/B testing, also popular as split testing or bucket testing, is a process of estimating two versions of an app or a webpage to acknowledge which one is the topmost performer. AB testing is a form of comparison of websites and apps where two or more forms of a website are manifested to users at random and statistical inspection is used to certify which form of the website is the top scorer for a given renovation goal.
Conducting an AB test that straightly compares a variation against a current scenario. It insights you about the changes you need to make to your website or app. After that, obtain data about the impressions of that change.
AB testing enables you to take informative and productive decisions so that you can walk on the path of success. By calculation, the changes make sure that every change is producing positive results.
Pitfalls in A/B testing:
Everything has a loophole within them and the same goes for the AB testing. It is a commanding tool if used accurately. But running a test correctly requires appropriate knowledge and skills. You have to adopt a correct mindset and process to access accurate AB testing.
Some of the basic pitfalls of AB testing are:
- They don’t have a real hypothesis: The most inappropriate way of doing an AB test is to execute a change, start an AB test and look at what happens, well it is not good as the potential success measures are common to show the significant improvement. The point of concern is that you won’t be able to learn anything except the terminologies of an AB test.
A good A/B test includes a clear statement of the persisting problem and the change you need, what will impact the users, and the way to measure the impact.
- Too many metrics: Yes you read it right A/B testing provides numerous metrics which makes everything chaotic. Just like you are searching for fruit in the forest and you get stuck in the arbor of grapes in search of a bunch of grapes. Multiple metrics make you look at the only positive increases.
- Not enough sample size: When your hypothesis is all set, you should start thinking about the needed sample size, that is the number of audiences you need to get a proper statistical analysis. If you want to improve in a small amount you need to get an abundance of audience. Well gaining the target audience is not a child’s play, you have to work your fingers to the bone. If you do not wait for the right sample size, you will not be able to maintain the required statistically significant results. Sometimes even waiting does not work as it does not get enough traffic to reach the required size of the sample. Well in these cases, AB testing is not so correctly measured.
- Peeking: Do not peek until and unless you have reached the optimum size of the needed sample. If you do it before time, you would be in misconception as it will show false positives owing to the fluctuations in the target measure.
- Changing assignment during the test: In an AB test where half of the people are assigned to the control group and half of the people to the test group, it is absolutely fine. And if in an AB test 90% of people is a control group and the remaining 10 % of people is a test group, it is also okay. The matter of concern is changing the group in between the test and making it 50/50, to depict a positive change. Changing allocation will surely lead to the wrong results.
For example, A company whose revenue is 30 million, conducts an AB test by increasing the size of the clickable button, in its earlier variant, the add to cart section of the company has the presence of a wish list option and in the other variant, it removed the wish list option and converted it into a direct buying option. After this minor change, it says that its revenue increased up to 159.95% just by changing the button. Do you think the changing option worked? It’s utter nonsense. Please do not believe in these false claims. It’s like an advertisement of the food product of a company, the picture of food is something else and in reality, the food comes in a different form.
How long should AB tests be done?
AB testing is one of the most unique forms of marketing strategy. Do not follow the mistakes of what other people have done in the past, you must not end your test so soon. Do not act like you know everything, act like John snow which means you know nothing. Keep patience if you think your patience is being dried out then again put a drop of patience in your mind and wait till you reach the ample amount of the sample. It could take a week or two to get genuinely done. It’s just a matter of two weeks. You need to awaken the patience within you as the customer behavior is dynamic. Only then you could get the accuracy of your app or webpage.
How to achieve conversion?
The rate of conversion is much higher on Thursdays instead of weekends. To achieve optimum conversion, you need to continue testing until the week because half the week can show false results. Refrain yourself from these things. And try to run the test for another seven days if you haven’t reached the statistical significance.
Key things to achieve growth:
There are two hotel booking companies both have reached the optimum stage and have numerous audiences with an abundance of hotels, they both go for online marketing. But why has one of them dominated the other in only a few years? Why? Well, they dominated because they have a team of optimizers but the other has not. The dominant one is testing their website regularly to make it sell better. They took note of the cognitive biases which are also of cost. Cognitive biases are available on Wikipedia. The dominant companies try different cognitive biases to make it through. And increase the curve.
To achieve optimum growth you must
- Aim for the right amount of validity: You should take tests for a longer duration. So that you can see genuine conversions. You must know alpha error inflations.
- Create more effective variations: You should use many effective forms and go for behavioral economics. Opt for qualitative methods and prioritize your hypothesis.
- Change your focus: You should put your focus on the process of efficiency, such as the success rate, average uplift.
To achieve optimum growth one should follow a three-step process which is Goals ability + culture. And can dominate like a pro in the market. If you are keenly interested in mastering the art of marketing then cxl.com and booking.com have paved a path for you. Take a walk there and then you could create a trackway to the digital marketing world.
Cognitive biases: It refers to the multiple ways in which the subject and mounting of details or information affect individuals. Cognitive biases increase our rational thinking and efficiency. Let’s have more clearance. For example, Booking.com is a hotel booking company and it is dominating the market. Why? It is the only one to suppress the others? Because they read the list of cognitive biases and applied them. For instance, they implemented the bandwagon effect (one of the cognitive biases) and now they are ruling the market. While the others are not taking it seriously they are getting lost in the crowd.