A/B testing is a powerful tool that allows you to test two or more variations of your site or web page. The goal is to determine which variation performs better.
A/B testing has become a standard practice in marketing, especially for ecommerce sites. In fact, Google uses A/B testing to decide which ads appear on its search results pages.
But before you start running tests, you should consider some important questions. For example, what type of traffic do you want to attract? How much time do you have to run the test? What kind of data do you need to collect? And finally, what metrics do you want to track?
Let’s answer these questions one by one.
What Type Of Traffic Should You Be Targeting With An A/B Test?
You can use an A/B test to target any type of traffic. So if you sell products online, you can use it to find out whether a specific product description works better than another. If you offer services, you can use it for different pricing plans.
If you are selling physical goods, you can also use this technique to see how well a certain shipping method performs. It could be as simple as putting up signs at your store saying “Free Shipping On Orders Over $100!” and then tracking how many people click through to your website from those signs.
The point here is that there are no limits when it comes to using A/B testing to improve your business.
How Much Time Will Your Tests Take To Run?
The amount of time required to set up and run an A/B test depends on several factors:
• The number of variations you want to test (more variations require more time)
• The size of each variation (the bigger the difference between them, the longer it will take to compare them)
• The complexity of the changes (if you make multiple changes simultaneously, it will take longer to compare all of them)
• The speed with which you can implement the changes (if you change something every day, it will take less time to run the test than if you only change it once per week)
So let’s say you want to test three variations of a landing page. Each variation would include a headline, body copy, and call-to-action button. Let’s assume that each variation takes 10 seconds to load. This means that the total time needed to run the test would be 30 minutes.
Of course, you don’t always need to wait until the end of the month to run an A/B experiment. You can choose to run the test whenever you feel like it. But if you plan to run a lot of experiments during the same period of time, it might be best to schedule them ahead of time so they won’t interfere with each other.
How Many Variations Can I Have In One Experiment?
This question is closely related to the previous one. The number of variations you can have in one experiment depends on the number of variables you want to test.
For example, let’s say that you want to test four variations of a landing page:
1. Headline 1
2. Body Copy 1
3. Call-To-Action Button 1
4. Headline 2
5. Body Copy 2
6. Call-To- Action Button 2
7. …and so on
In this case, you would have 7 variations in total.
Now, imagine that you wanted to add a fifth variation to the mix:
8. Headline 3
9. Body Copy 3
10. Call-To-Actions Button 3
11. …and so on.
When you have too many variations, you risk confusing yourself or even losing focus. So try to keep things reasonable. Keep in mind that you don’t necessarily have to pick five variations. Sometimes, two or three variations are enough.
But if you really want to go crazy, you can create as many variations as you want. Just remember that it will take you a long time to run all of them.
What Kind Of Data Do You Need To Collect?
There are two types of data you will need to collect to run an effective split test:
• Metrics – These are the numbers that show whether or not your changes were successful. For example, did visitors who saw the new version convert better than those who saw the old version? Did sales increase after adding a new feature? How much money did customers spend after changing their payment options?
• Statistics – These are the numbers used for statistical analysis. They help you determine whether or not your results are accurate. For example, what percentage of visitors converted on the first visit vs. the second? What was the average order value before and after implementing the new design?
You should collect both metrics and statistics. However, collecting metrics alone isn’t enough. You also need to analyze the data to find out why your changes worked or didn’t work.
Collecting Metrics
You can use any type of metric you want. There are no rules about how you should measure conversion rates. Some people prefer using bounce rate, but others prefer using unique visits or sessions. It’s up to you.
The most important thing is that you make sure that you know exactly how you measured conversions in the past. If you changed something without knowing how it affected your conversion rates, then there’s no point in running another experiment.
It’s also possible to track conversions over time by setting up multiple versions of the same landing pages. Then, when you see that some changes are working well, you can simply remove the old version from the site and replace it with the new one.
If you want to do this, you may want to consider creating a separate URL for each version of the page. That way, you won’t confuse visitors who come across the different URLs.
Here are some other ways to measure conversions:
• Conversions per visitor (CPA) – This shows how many visitors actually converted into paying customers.
• Conversion rate – This shows how often visitors converted into customers.
• Average order value – This shows how much money customers spent after they purchased.
• Average revenue per visitor – This shows how much profit you made per visitor.
It’s best to start with a simple metric like CPA or conversion rate. Once you get comfortable with these numbers, you can move on to more complex ones such as average order value or average revenue per visitor.
Collecting Statistics
Statistics are very useful because they allow you to compare the performance of different versions of the page. Here are some examples of statistics:
• Bounce rate – This tells you how likely visitors are to leave the page after visiting it.
• Page views – This shows how many times visitors viewed the page.
• Unique visitors – This shows how many unique visitors came to the page.
• Pages / session – This shows how many pages visitors viewed during their stay at the website.
• Time on page – This shows how long visitors stayed on the page.
• Exit pages – This shows which pages visitors left immediately after viewing them.
• Referring sites – This shows where visitors came from.
• Search terms – This shows what keywords visitors entered into Google or Bing to reach the page.
• Traffic source – This shows where traffic originated from.
• Geolocation – This shows where visitors were located geographically.
• Device – This shows what kind of device visitors used to view the page.
• Browser – This shows what browser visitors used to access the page.
• Operating system – This shows what operating system visitors used to access the site.
• Screen resolution – This shows what screen size visitors had while accessing the page.
• Language – This shows what language visitors used to access the webpage.
Conclusion
A/B testing is an effective way to improve your sales funnel. It allows you to test various elements of your landing pages to find out which ones work better than others.
When you run experiments, you should always keep track of all the data collected so that you can analyze it later. You need to be careful not to change too many things at once, however, since that will make it difficult to draw any conclusions about the results.
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