Total number of day to run the test:
|80% confidence level|
|95% confidence level|
|Number of Days||Number of Visitors (for completing test in 1 day)|
A/B testing (or split testing) compares two versions of a web page or any other marketing asset and gives you a performance analysis report. This helps in identifying the most effective strategy which will get you more quality results. Basically, through this process, you will be able to find out the best possible versions of your asset, which will give you the data-driven results to see which comes on top. This will help in making result-oriented future decisions.
After researching and formulation of the hypothesis, the next step is to create variations. A variation is nothing but, just another version of your current version with changes that you want to test. You can try with many variations to find out the most efficient one. You can create a variation based on the assumption of what could give you a positive outcome. Consider an example of running any information collection by getting forms filled from users. If you are not getting any results, then you try using other variations of decreasing the number of fields or changing the layout of the form. You can try multiple other variations also to test your hypothesis.
This is a very common question. But there are many different opinions on this one. Basically, if you are using only two tests, then you will observe that your hypothesis is either right or wrong and will again test for another hypothesis to cover all the loose ends. But with multiple variant testing, you can test many hypotheses at the same time. This will help you understand how you can increase the conversion rate optimization with the help of multiple variations testing methods of A/B testing. It is simply like a game wherein a team, many players work together to win the game.
Conversion rate is the percentage of visitors to your marketing asset, which could be a website, landing page or any other event created. The event or goal is set as per the business requirements, which could be anything like form filling, calling, signing up, downloading something, messaging, etc. The formula for calculating the conversion rate is:
Conversion rate = (conversions/total visitors) * 100
You can calculate the conversion rate of any specific event or it could be a broad one. With this conversion rate, you get to evaluate the performance. You can also use certain methods of conversion rate optimization to enhance the conversion rate and the quality of the conversions.
In a paid search strategy, the conversion is a key element. It is the sole focus of the whole campaign to convert visitors into buyers at a good rate. Conversion rate optimization helps you to increase conversions by increasing the prospects of your campaign.
One of the most important question- what is the ideal conversion rate to achieve? But the truth is, it varies from industry to industry, location to location, and from platform to platform. In some categories, 3%-4% is considered a good conversion rate while in some categories, it may go higher with the help of landing pages. You should focus on increasing the conversion rate in comparison to what you were getting earlier and on getting more quality MQLs.
Statistical significance is a mathematical way to check reliability in the result of an analysis that allows you to be confident in your decision. Statistical significance says that the difference in conversion rates between the variations and the current conversation is not due to some random chance.
A result is said to be statistically significant only if it is likely not caused by the chances for a given statistical significance level. The statistical significance level is also about understanding risk tolerance and confidence level.
For example, if you run an A/B testing experiment with a significance level of 95%, this means that the finding has a 95% chance of being true. That also means that you can be 95% confident about your results and it is not an error caused by some randomness. It also means that there is a 5% chance that you might go wrong.
The baseline conversion rate is also known as your current conversion rate. You can get this information on analytics tools like Google Analytics and others.
It is calculated as the number of successful actions taken on a particular page, divided by the total number of page visitors.
Our A/B Testing Duration calculator gives you an idea of the duration of your A/B test.
To calculate the A/B test duration, please fill in the information in the above calculator, you need to give inputs on your average daily traffic on the tested page, your number of variations including the control version, your current conversation rate, and your expected increase in conversion rate.
There is no sure certain answer for this, perspectives may vary. You need to always have a strong case for testing and iterating on your site. Just be sure that each test has a clear objective and will result in a more functional site for your visitors. If you’re running a lot of tests but the results are not that significant then reconsider your testing strategy. Get in touch with ab testing service providers like Nabler.