What do visitors say about your website when you are not around? Although it's hard to say, we all know that it could be the difference between selling your tickets, or not.
You probably have some clues, right? But how can you assess it?
If you answered "by testing different options" you're partially right.
Why partially? Let's see this example: you've changed some copy on your home page and saw your online ticket sales increasing by 10% within the next 30 days.
Was this experiment successful?
It's quite hard to say.
The reason are all the different external variables that influence on your sales.
From simple variables like seasonality or the weather forecast to more complex ones like big events on your city, they can give you the false feeling of success even though your experiment was not successful.
But how to solve that problem?
That's when you start AB Testing your website.
Simply put, in the web environment AB Testing is a method used to compare performances by exposing two versions of the same web page to the same audience at the same time.
How to do it?
The secret lies behind the scenes, where 50% of the traffic is randomly assigned to the original page ('control page') and the other 50% to the new version ('variation page').
It reduce external influences and allows direct comparisons, as both pages are exposed to the same environment.
AB Testing allows companies and individuals to prove their hypothesis. You eliminate the guesswork as much as possible by supporting your decisions with data and implementing changes based on a clear methodology.
As soon as you decide to run tests frequently you are also deciding to learn from your audience. It means that your strategy will now be based on 'real-world' findings, and that your website will address your customers' needs.
And the benefits? Well, from this point on, the benefits can be multiple.
Just within the ticket sales example, you could understand how to sell more tickets, how to increase the cart size, how to increase the up-sells, what attractions to highlight, or what call-to-action to use.
It's also allowed to assume that, as those experiments will help you to understand your customers better, they will also help you to enhance your entire customer service, even the offline experiences.
Why? Because by the end of a couple AB tests you'll have a better understanding of your customers' needs, and as more you know about them, better you will be able to serve them.
AB Testing is a statistic-valid experiment. It means that you should have a representative sample, a clear hypothesis, and a way to analyze it between some confidence intervals.
That said, many tools can help you with setting your experiments up and analyzing the results. One important thing, though is the traffic. You'll usually need to have enough traffic to create valid results, by exposing your audience during a specific timeline (usually 30 days are enough).
The process is simple and should start with a good understanding of the problem, otherwise, you'll test just for the sake of testing, but your results are not going to help you to sell more.
1. Identify the problem or the opportunities of improvement: by looking into your analytics you could find high cart abandonment rates in specific pages, or you could see that some of the forms are converting better than others. This is up to you, but make sure to understand what is your expected outcome.
2. Define the goals: Now that you found opportunities, it's time to define what you want to improve and how you'll recognize success. It could be to increase a button click, the time on page, the ticket sales, or whatever makes more sense for you. Keep in mind that this is what will help you to determine the success of the experiment.
3. Write the hypothesis: After defining your goals it's time to write the hypothesis down. This is where you'll come up with ideas and will explain what you expect from them. Make sure to create a hypothesis that can give you an insightful answer. It means that if you change everything on your page you won't know what is really moving the needle.
4. Create the variation page: From a simple headline to a complete redesign, this is the piece that will translate your hypothesis and be shown to your variation audience.
5. Set the experiment: This is a very important part of the job as well, and tools like Convious can help you out with properly setting this step up.
6. Wait the necessary time (usually 30 days): During this time half of your traffic will be exposed to the original page and the other half to the variation and you'll be able to see which page is generating more positive outcomes regarding your goals.
7. Analyze the results: Although it's tempting to analyze the results before the end of the experiment, you should never do it. Whenever the test is finished you'll analyze the results and understand if your hypothesis was confirmed, or not.
8. Implement or keep testing: In the case that your hypothesis was confirmed, congratulations! It's time to implement it to 100% of your traffic! Otherwise, it's time for your next round of testings!
At Convious, we run AB tests regularly for our clients and we frequently see small changes creating massive uplifts. It's not about the change itself, it's about your audience. AB testing demands knowledge about the problem, and this is where you should spend most of your time.
Every website has opportunities to improve, and this method has been proved so effective that has been adopted by companies of all industries, all sizes, and even by companies that explore offline business models.
You don't need to start with big changes. Try to solve small problems, learn from it, and after some time you'll be asking deeper questions and creating a more relevant hypothesis.
Have you been AB testing your website regularly? Let us know what you think about it by commenting below, and good sales!