Customer feedback is not easy to predict, so you should use a tool like A/B tests. It can minimize the risk of failure in communicating your marketing message and save a lot of time. Here are a few words on how to perform A/B tests that give you actionable tips and help to improve your campaigns.
The key part of today’s Internet marketing is collecting data for effective analysis and intelligent use of the results. Online marketing companies have a huge amount of data about consumer preferences, shopping locations and even favorite stores. But how do you know if the insights you obtained are actually useful? How to assess if a marketing campaign works?
In the simplest terms, a marketing message is most effective when it meets the goals that were set up during its creation. It can be a certain CTR, the number of newsletter subscriptions or purchases. There are many simple mistakes that may occur in marketing materials. Even small details, such as the background color of the ad that is displayed to the recipient, can discourage the customer to interact. There’s only one way to see if your leads and customers are doing what you want them to do: test your assumptions.
What is an A/B test and how does it work?
An A/B test is based on the assessment of how different variants of the marketing message affect the recipient. It is useful when you see that an element doesn’t bring the desired effects (e.g. a low click-through rate). The goal is to check if another variant can change it.
You start by creating at least two versions of the message for testing. The first is the original one and the second one includes a changed element that you want to test. With two different versions of the material, you can assess which one will get a larger conversion rate.
It is important to create a hypothesis at the very beginning, which will later be verified. It should state what exactly you want to investigate and the activities you’ll expect from the recipients. After preparing the variables and the thesis, select the groups of people to be examined. To get reliable results, the test groups should be similar to each other, and the messages you test must be sent at the same time.
To analyze an A/B test’s results, you need to compare the conversion rate of the tested variants in order to verify the hypothesis. The implementation of the assumed goal, e.g. a higher conversion rate on a website, doesn’t necessarily mean an increase in sales of the products concerned. Sometimes the conversion rate, your assumed goal, will increase, but the overall profit on sales will remain the same or even decrease. However, the greater the discrepancy between the results is, the more reliable the research is.
The benefits of A / B tests
Analysis is easy to introduce
The method is based on comparing the marketing message options. Depending on the type of tests, you are able to compare over two or more variants, differing only in small details. The information is easy to examine even for those with weaker analytical skills. At the moment you know which of the advertising options brings you more conversions and you can use it for all customers. Gathered data can be used not only in the current campaign, but in future ones as well.
Customer reviews delivered in real time
As marketing is focused on the needs and preferences of the customer, you want to find out what people like and how to attract them. A/B tests offer data collection that helps us gain insights into this. An additional advantage is that you receive information in real time, which lets you observe changes relatively quickly. However, actionable insights will not appear overnight – A/B tests need to run for a particular period of time, which I’ll explain in the next section.
The ability to examine each item
Each element of the marketing message can have an effect on the recipient. Therefore, it is important that A/B tests allow you to analyze even the smallest details. There are practically no barriers: if you think an element is worth testing, nothing stands in the way. Remember, though, that the two test variants can’t differ too much from each other. Otherwise, you won’t be able to determine which part of the ad influenced the users.
Low costs, large profits
A/B tests are a relatively cheap method of campaign optimization. The main idea of the campaign stays the same, you only change small elements, which is neither time-consuming nor expensive. You can generate large profits through a higher conversion rate. In addition, you can minimize the risk of wasting your advertising budget.
Limitations of A/B analysis
Time to collect information
This method is time-consuming. To obtain a reliable result, in most cases, you’ll have to wait for quite some time depending on the size of the campaign, e.g. a month when testing a subpage on your website. If the analysis needs more time and the new version of the ad doesn’t get more conversions, the company may suffer losses. To avoid this, the project must be well thought out, also in terms of timeframes. You must specify how much time it will take to be able to accept the results.
A large audience
To know that particular option is better, we need a large number of people participating in the test. In order to get the largest number of recipients, it is worth considering your target group well. The more active people in the selected segment, the more data we can collect. Choose your target group wisely – if you don’t get enough data, the results of the study may come out incomplete and you’ll have to repeat the test.
Quantitative, not qualitative data
AB tests can’t be used as the only tool to improve your marketing activities. It provides only data about individual elements that correspond to the recipient. You can’t get information about things that are not suited to customers or why they don’t work. Also, external factors affecting the recipients can influence the way they will receive the message and even change their attitude from positive to negative.
Solution used in Synerise
Synerise also offers the possibility to carry out A/B tests. When creating an automation scenario, you may carry out A/B tests on up to 10 variants of the advertisement. You can do this from the level of the automation module, where you can prepare variants of the ad for individual groups.
Apart from testing two version of a campaign in the same channel, you can also test different media for them, e.g. a text message vs. an email. Not only will you see which elements of the message reach the recipients better, but also what form gives more conversions. All statistics regarding the test data are available in the analytics module.
The A/B method is comprehensive and its results allow you to increase conversions on the website, thus increasing the company’s profits. A/B test results obtained in the past may become useful in future campaigns, but they aren’t evergreen – your recipients’ preferences may change in a very short time. The key is to define what goals you want to achieve by analyzing and continuously controlling this process.