A/B Testing What is that? And why can it assist you in implementing marketing campaigns? Many of you are probably using intuition to make predictions about what will trigger a click and to optimize conversion rates; every time you write email marketing, design the Landing Page or CTR.
However, if you do marketing based on intuition alone, it will not always bring the expected results or achieve the desired effect.
Instead of just making unfounded assumptions, there is another way that can help you know quite accurately the behavior and thoughts of your visitors. That is to use A/B Testing.
DPS will explain it to you in detail in today's article:
A/B Testing What is that?
Why should we use A/B Testing?
Implementation process A/B Testing
Things you need to keep in mind when doing this A/B Testing
Are you ready to accept new knowledge about A/B Testing Not yet? Let's conquer the new knowledge path with DPS!
What is A/B Testing?
A/B Testing also known as split testing or bucket testing. This is a method used to compare two versions in an environment or situation. And from there, you can judge which version performs better.
The version here can be a webpage, ad template, email marketing, or an application.
Basically A/B Testing is the test; in which, at least two or more variations of the page are displayed to the user in a different mannercompletely random. From there, with statistical analysis you can determine which variation might work better for your given conversion goal.
Use A/B Testing allows you to compare one or more variations and an existing experience. Thereby, you can ask questions about changes to a certain Webpage or application. Next, you will have to collect data on the effectiveness ofthat change.
By measuring changes in the variability of actual metrics, you can be absolutely sure that every change produces certain positive results. Don't just rely on your intuition to make changes.
Why should we use A/B testing?
A/B Testing is the ultimate tool that enables individuals, teams, and businesses to make thoughtful changes to user experiences. Also collect the corresponding data for the variation. And based on that data to choose the variant with the best results to make changes.
This allows you to build relevant hypotheses and better understand why the factors identified in your experiment impact user behavior.
In other words, you can prove your assumption about the best experience for a defined goal true or false through A/B Testing.
Not just one-time answers to questions or resolving disagreements; but A/B Testing It is also used consistently to continuously improve individual experiences and goals. DPS will take a good example of conversion rate over time so that it is easier for you to visualize.
For example, a B2C auto accessories company wants to improve the quality as well as the number of potential customers accessing the company's website from the campaign information channels.
In order to achieve that goal, a team will try to change the title, image, frame opt-in, CTR buttons and even the overall layout of the website with A/B Testing.
Examining each change one at a time helps them pinpoint; How will those changes affect user behavior? And are there any other changes?
Based on that, you can gradually combine the effects of many successful changes from previous experiments. This is to prove that the new experience improvement is better than the old one.
Why businesses should use A/B Testing
With the method of notifying changes in user experience (User Experience), it allows for optimized experiences. And from there, you can be more confident to take the key steps in a marketing strategy.
By testing A/B Testing For different ads, marketers can find the version that attracts the most clicks.
Or with Landing Page testing, you can find the right layout. So that there is a conversion from visitors to customers. That is, increase the conversion rate.
Investment budget for a marketing campaign (communication campaign) can be reduced if each element in each operational step is most effective to attract new customers.
Process of performing A/B Testing
There are different ways to implement A/B Testing. But the most efficient way to implement the process A/B Testing What is that? Here DPS will introduce to you the process A/B Testing template that you can use to start an experiment:
Step 1: Collect data
Collecting and analyzing the data will give you a clearer overview of the problem you need to start optimizing for. From there, it can help you get started with high-traffic sites for a website, email marketing, or an app. This will help you collect data faster.
From there, start looking for sites with low conversion rates or high drop-offs. And start improving those pages first.
Step 2: Define your goals
This is a very important next step. Because properly defining your conversion goals will be based on metrics; which you will use to determine if the variation you are doing A/B Testing more successful than the original version.
The goal here can be anything including:
- Button clicks
- Link to sales website.
- Title of email marketing.
Step 3: Create a hypothesis to perform A/B Testing
Once you have a specific goal in mind, you can generate hypotheses and ideas for A/B Testing. Let's start with the questions and answers why you think the variation will be better than the current version.
Once you have a list of ideas, sort them according to their priority of project impact and also the difficulty of implementing them.
Step 4: Create Variations for A/B Testing
To generate variations for split testing you can use many different tools. DPS can name a few of the most commonly used tools for you to consider and use:
- Google Analytics
You can use one of the apps above to help you make desired changes to a certain element of your website or mobile app experience. This could be as simple as:
- Color change of a CTA button – click-through rate
- Swap the order of elements on the page or change the layout of the website.
- Hide navigation elements or completely customize one.
Tools A/B Testing “modern” has a visual editor. They will help you make these changes easily. Always make sure your A/B testing works according to your expectations.
Step 5: Run the test
This step is pretty easy! You can start testing and wait for users to access your website or app!
In this step, visitors to your website or app will be randomly assigned to control or change your experience.
Their interactions with each experience will be measured, calculated, and ultimately compared to determine how each works.
Step 6: Analyze the results of A/B Testing
When your experiment is over, it's time to analyze the results.
A/B Testing Your will extract data from those tests. It will show you the difference between how the two versions of the website are performing. And do you consider whether or not there is a statistically significant difference?
In case the variation was successful then congratulations! From there, can you take the lessons from the experiment and apply them on other pages of the website. You should also consider continuing to repeat the tests to improve results or stopping.
In case your test fails or produces negative results, don't worry. See that experiment as an experience. And keep learning and keep creating new hypotheses so you can do another test.
Applications of A/B Testing
A/B Testing can be applied to greatly improve the operation and development of websites, online and offline advertising, as well as for mobile apps and email marketing.
1. For website
Main application of A/B Testing for website is the interface of the website and UI/UX (user experience). Because these are factors that have a direct impact on whether visitors are able to make conversions on the website.
With a website you can use A/B Testing The most likely factors that influence visitor behavior include:
- Layout of the website
- Fill-in form, etc...
You can now perform the check of each element in turn; that you feel they can improve to increase conversion rate.
2. For advertising and sales
2.1. Online advertising
For online advertising, you can use A/B Testing to measure the effectiveness of different ad formats.
The same goes for online ads, typically on Facebook. We can use different ad designs to run ads for the same campaign. Then you can measure the effect. From there, you can choose a more efficient design to run with the remaining budget.
The optimization of ads using A/B Testing, will help you continuously improve your conversion rate and at the same time run more and more effective ads.
2.2. Offline advertising
For this array A/B Testing can often be used to evaluate the effectiveness of advertising channels including: leaflets, printed newspapers and billboards etc.
DPS will take a specific example for you to visualize. For example, you use different coupon codes for the same advertisement but in newspapers, billboards or on flyers. You will collect information and analyze the results to see which advertising model on the channel is more effective.
How can you judge which channels advertise more effectively? Assume that the number of people using coupon codes in newspapers is the most among the remaining channels; then this is the most effective communication channel for your campaign.
Besides, A/B Testing can also be applied flexibly and diversely depending on the set goals. For example, how to arrange products in a store. This is to measure consumer satisfaction and make them buy more.
3. For mobile application
You can apply A/B Testing in the development of mobile applications. Similar to testing like website, A/B Testing used to improve or develop the product's UI/UX.
Technically speaking, to use A/B Testing For mobile apps, the app version needs to be updated and browsed by the AppStore or Google Play. After that, these applications are only available to users. This costs you more time to experiment.
4. For email marketing
Emails sent to clients today have more sophisticated filters. However, what is more important is how to get customers to open your sent email and interact with it.
The answer to the above problem is A/B Testing.
Confused about which title sentence should be used to attract more readers and to increase the open rate? The answer is to experiment.
Don't know how to use CTR to get users to click on the link? The answer is still to experiment.
Most popular email sending tools today are: MailChimp nice Marketo. They all have a feature that allows A/B testing of the submitted content. For the purpose of measuring the effectiveness of the campaign sent.
Things you need to keep in mind when conducting A/B testing
1. Things you should do
1.1. When to stop A/B Testing
Stopping too soon will cause you to lose valuable parameters. From there, you can make the right decision.
Conversely, if you run the test for too long, it will also cause negative effects. In case, your test version has too bad results, it is likely to adversely affect your conversion rate as well as your sales.
1.2. Homogeneity in the A/B . test
When in progress A/B Testing there needs to be a way to remember which visitor chose which beta. Make sure that the correct version is shown to the respective user at all times. This is to avoid affecting the user experience.
Or in case, you need to change the position of the CTR button to test, make sure that even though the button appears in many places on the website; Every customer should see this button the same everywhere on the website. The most commonly used method is Cookies.
1.3. Test multiple times
In fact, not every A/B test will bring the results you expect or help you find a better solution to the problem.
So you keep testing many more times but in different directions.
If each test improves your conversion rate a little, then testing multiple times and adding up will create a bigger impact.
1.4. Sdifferent between quantity traffic from mobile and desktop
In addition to the above notes, you also need to note the difference when customers access your website from mobile and from desktop. Because the user will probably have a completely different expression. Because it depends on the design, UX and whether your website is mobile-friendly or not.
Therefore, it is best to test for the mobile version and the desktop version separately.
2. Things you should not do when conducting A/B Testing
2.1. Conclusion too soon
You should remember that results are only valid if and only if they have a relative value and it takes a corresponding period of time to determine.
Let's say you run 2 ads A and B for the same customer group. And your goal is to see which post is more effective.
Let's say you rely on the number of orders from each ad to see which one is more effective.
After running the test at all 500k, exercise A is more effective than exercise B
However, after testing all 1 million, lesson B is more effective than lesson A
From here, you can see that it is not possible to decide hastily that version A is more efficient than B or vice versa; when they only differ by a few conversions, the test time is too short or the number of samples is not large enough.
2.2. Focus on new customers
When in progress A/B Testing It's best to focus on new customers only.
Because if old customers go to the website and see things differently than before, they may be surprised. It is possible that this has a negative effect on the conversion rate.
Especially when you are not sure whether the test version is selected or not.
2.3. Let your hunches and feelings govern the results
Sometimes the test results may not be exactly what you think.
Maybe a red CTA button on a green background; according to you is dazzling and annoying. However, the test results may prove that it is more effective.
You should keep in mind that your goal is done A/B Testing What is that? That is the conversion rate. Therefore, don't let your emotions or hunches go against the test results.
Hope the above article will help you answer some questions for those who learn about A/B Testing and successfully apply it to your work.