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Where To Start With A/B Testing

A/B testing, also known as split testing, is a useful tool for finding out what design, copy and timing can be used to optimise your online marketing efforts. Many business owners assume that only landing pages can benefit from A/B testing, but in reality there are a number of different elements which can be tested.

Learn more about A/B testing here and how you can implement it in this insightful post.

 

What is A/B Testing?

A/B testing is the process where you provide two options of a marketing piece in order to test which performs better. During the process, you would send half your audience to one version and the other half to another version.

The process is not supposed to happen just once; instead A/B testing should be a continuous evolution of your marketing. So when you have identified which one of the first set is most effective, you then create a new variance and test the current optimum option against the new variance. The idea is that as you continue to conduct A/B testing you will be increasing your marketing effectiveness.

 

What Marketing Materials Can Be A/B Tested?

A/B testing can occur at almost any level. Landing pages are the most commonly tested feature and are fairly easy to monitor the results. Testing landing pages does require a certain level of technical ability. Yet they can offer the most reward.

Emails are another popular marketing material to test. It is easier to split the audience equally for emails and depending on the service you use, statistic gathering is incredibly easy.

Social media can also be A/B tested, although this is harder to achieve.

 

What Elements Can Be Tested In A/B Testing?

There are many different elements which can be altered during the A/B testing process, from the simplest colour change to an in-depth change of the copy.

Each of the elements you are testing could also have many different options available to them. For instance you might want to test the call to action. Your call to action could have different text, colour, design, placement and role. Testing all these features could take time, especially as you should test one aspect at a time.

With emails you can also test the time and day you send them. Though it is important to note that with A/B testing there are many different elements to look at, not just the first interaction.

 

What Statistics To Concentrate On

A/B testing is a very powerful tool for finding out which marketing copy is best for converting visitors, however it relies on the correct collection, calculation and analysis of the data.

There are a number of different aspects to look at when it comes to the results of your landing pages. Some believe the conversion rate is the only useful statistic that can be collected. This is mainly for landing pages which are building an email list.

However, you could take this further and look at the lifetime of that contact on the email database. You may sign up more people using one copy, but if a higher percentage unsubscribe quickly, then the landing page is obviously converting the wrong audience.

A more extreme statistic would the lifetime customer value. However, this would be very hard to test as the time period could span a number of years and the testing period should not be of this length.

 

How Long Should You Test An Option?

One of the important decisions you need to consider when you are conducting A/B testing is how long to run each test for. The length of time of the testing is not as important as the size of the population (number of visitors) which are collected.

The larger the population the more accurate the results are for that landing page so you should never stop testing until you can be sure you’ve collected a significant set of results. At a very minimum this should be 1000, though 10,000 would certainly be a better option.

Another consideration is the time of the week and month which users land on the page. Therefore, while the length of time should not be considered too important in the testing phase, it should generally last at least one month.

 

What Statistics Should You Be Looking At?

The most obvious to review are the click through rate and conversion rate. However these are not the only interesting statistics which you could collect. Here are some of the others:

Bounce rate: sometimes one offer is not what the customer is really looking for, though they may be interested in another offer. The bounce rate looks at the number of people who view only the landing page and then exit the site. This is a good statistic to collect because a user who looks at the landing page and then clicks off hasn’t clicked on the call to action or visited other pages on the website.

A high bounce rate can mean you are advertising to the wrong audience or your keywords are wrong. Therefore changing these elements might be important.

Email Retention: Sometimes people will sign up for an email to receive the offer and then unsubscribe after a week. These are contacts that you don’t really want. Therefore determining if your content is appealing to this group, by finding a high retention rate, is an important factor.

Customer Acquisition Cost: The cost it takes to deliver the customer to the landing page to when they become a paying customer can be very useful. Some content will require a longer period of time for the visitor to convert into a paying customer, which will cost more and therefore give lower profit margins.

 

Conclusion

A/B testing is an important factor of your online marketing. It requires careful planning, monitoring and interpretation to yield the most from the process but above all, it is necessary to be patient. Results will not be instant, but the benefits to the business over the long period far outweigh the costs.

 

Action Steps:

  • Identify one landing page, copy it and change one element on the landing page.
  • Test the landing page for at least one month ensuring a significant sample size to see which version yields better results.
  • Split your mailing list into two even groups and send your next email at different times to both groups.

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