As a marketer, it is important to make the product acceptable to the customer. You can do A/B Testing as a basis for considering decision making with this purpose. Usually, this term is also carried out in collaboration with designers.
For example, you can test the design and certain buttons on a web page to attract people's attention to access it. Not only web pages, you can also change some elements in an email sent to customers.
A/B Testing is a method that is carried out by testing two variations (or more) on different target groups in order to determine better performance. In simple terms, you will give a trial version of A to be tested to half of the group and version B to the other half of the group.
Benefits of A/B Testing in Marketing
There are many different types of split tests that can be run for your company's experiments. Here are some common goals marketers have for their business when doing A/B Testing:
How to Do A/B Testing in Marketing Experiments
If this is the first time you want to test an experiment, prepare a few things in advance to develop a feature or product. Keep in mind that A/B testing is a long process so the steps must be carried out to completion. Here's how to experiment with A/B testing in your marketing efforts.
1. Specifies one variable to test
You may find several variables that you want to test, but to evaluate how effective the changes are, it's a good idea to define one independent variable and measure its performance.
Define variables by looking at the company's marketing resource elements and alternatives to design, wording, and layout. Keep in mind that simple changes, such as changing the image or wording of a CTA (click to action) button can drive big improvements.
You can test multiple variables for a single web page but be sure to test them one at a time, although in some cases it makes more sense to test multiple variables at once, this process is called multivariate testing.
2. Setting goals
Choose a primary metric to focus on before running a test, even before setting up a second variation. This is the dependent variable that changes based on how you manipulate the independent variable. Think about where you want this dependent variable to be at the end of the split test. You can also provide an official hypothesis and then check your results based on these predictions.
3. Defining Control and Challenger
Use the independent variable, dependent variable, and desired outcome as information in preparing an immutable version of whatever you are testing as a control. If you are testing a web page, then the version that cannot be changed is the usual/currently used web page design.
Then determine the challenger, which is an alternative website page that will be tested with control. For example, if you are wondering whether the testimonial column on your company's web page will make a difference, then you can set up a control page without a testimonial column and a challenger page with a testimonial column.
4. Divide the sample group by random and the same number
You need to test with two or more website visitors with the same number for each version so that it has a conclusive result.
5. Determine the sample size (if any)
How to determine the sample varies depending on the tool and type of A/B Testing and how long the test is carried out.
6. Determine how significant the results are required
After selecting your goal metrics, think about how significant the results would need to be to justify choosing one variation over another. The higher your percentage level of confidence in a variation, the more confident you will be about your results.
7. Make sure to run one test on a campaign
Testing more than one thing for a campaign can complicate the results.
8. Using the A/B Testing tool
One tool that can be used is Google Analytics which allows doing A/B Testing of up to 10 versions of a single web page and comparing their performance using a random sample of users.
9. Testing two versions at the same time
When running your test you have to run two versions at the same time. The only exception is if what is being tested is time, such as finding the optimal time to send an email/reply to a customer.
This is a good thing to test because depending on what products/services your company offers and who your customers are, the optimal time for customer engagement may vary by industry and target market.
10. Give A/B Testing enough time to generate useful data
How long is enough time? This depends on your company and how you run the test. A large part of the time it takes to get statistically significant results is the amount of traffic you get from your company's website. The less traffic, the longer it will take to run the test.
11. Asking for feedback from real customers/website visitors
One of the best ways is through surveys or polls. You can add an exit survey on your company website by asking visitors why they pressed or didn't press a certain CTA button.
12. Focus on goal metrics
Even though you'll be measuring multiple metrics, stay focused on the metrics that are the primary goals you've defined. Looking at goal metrics is usually through the conversion rate or website visitor experience.
13. Comparing conversion rates
By looking at the results, you can tell if one variation is working better than the other. However, real success is seen from statistically significant results.
For example version A has a conversion rate of 16.04% and version B has a conversion rate of 16.02% and the confidence interval for your statistical significance is 95%. Although Version A had a higher conversion rate, the results were not statistically significant. That is, version A does not increase the overall conversion rate.
14. Distinguishing customers for further suggestions/criticism
Regardless of the significance, it is important to segment the results by customer segmentation to understand how each key area responds to your variations. Common variables to differentiate customers include:
15. Planning your next A/B Test
You can try testing other features to improve certain metrics in the development of a product. You can test on different marketing channels to find the most significant results.
A/B Testing allows your company to understand what customers/visitors really want to see, especially in relation to the content and marketing offered by the company.
Testing a marketing strategy needs to be understood by company executives to develop profit-oriented products. Like the training activities that will be carried out in the Certificate of Business Management: Marketing Management program, the training does not only discuss concepts, but also case studies, group discussions, comprehensive summaries through seminars, and group assignments at the end of the program.
As a marketer, it is important to make the product acceptable to the customer. You can do A/B Testing as a basis for considering decision making with this purpose. Usually, this term is also carried out in collaboration with designers.
For example, you can test the design and certain buttons on a web page to attract people's attention to access it. Not only web pages, you can also change some elements in an email sent to customers.
A/B Testing is a method that is carried out by testing two variations (or more) on different target groups in order to determine better performance. In simple terms, you will give a trial version of A to be tested to half of the group and version B to the other half of the group.
Benefits of A/B Testing in Marketing
There are many different types of split tests that can be run for your company's experiments. Here are some common goals marketers have for their business when doing A/B Testing:
How to Do A/B Testing in Marketing Experiments
If this is the first time you want to test an experiment, prepare a few things in advance to develop a feature or product. Keep in mind that A/B testing is a long process so the steps must be carried out to completion. Here's how to experiment with A/B testing in your marketing efforts.
1. Specifies one variable to test
You may find several variables that you want to test, but to evaluate how effective the changes are, it's a good idea to define one independent variable and measure its performance.
Define variables by looking at the company's marketing resource elements and alternatives to design, wording, and layout. Keep in mind that simple changes, such as changing the image or wording of a CTA (click to action) button can drive big improvements.
You can test multiple variables for a single web page but be sure to test them one at a time, although in some cases it makes more sense to test multiple variables at once, this process is called multivariate testing.
2. Setting goals
Choose a primary metric to focus on before running a test, even before setting up a second variation. This is the dependent variable that changes based on how you manipulate the independent variable. Think about where you want this dependent variable to be at the end of the split test. You can also provide an official hypothesis and then check your results based on these predictions.
3. Defining Control and Challenger
Use the independent variable, dependent variable, and desired outcome as information in preparing an immutable version of whatever you are testing as a control. If you are testing a web page, then the version that cannot be changed is the usual/currently used web page design.
Then determine the challenger, which is an alternative website page that will be tested with control. For example, if you are wondering whether the testimonial column on your company's web page will make a difference, then you can set up a control page without a testimonial column and a challenger page with a testimonial column.
4. Divide the sample group by random and the same number
You need to test with two or more website visitors with the same number for each version so that it has a conclusive result.
5. Determine the sample size (if any)
How to determine the sample varies depending on the tool and type of A/B Testing and how long the test is carried out.
6. Determine how significant the results are required
After selecting your goal metrics, think about how significant the results would need to be to justify choosing one variation over another. The higher your percentage level of confidence in a variation, the more confident you will be about your results.
7. Make sure to run one test on a campaign
Testing more than one thing for a campaign can complicate the results.
8. Using the A/B Testing tool
One tool that can be used is Google Analytics which allows doing A/B Testing of up to 10 versions of a single web page and comparing their performance using a random sample of users.
9. Testing two versions at the same time
When running your test you have to run two versions at the same time. The only exception is if what is being tested is time, such as finding the optimal time to send an email/reply to a customer.
This is a good thing to test because depending on what products/services your company offers and who your customers are, the optimal time for customer engagement may vary by industry and target market.
10. Give A/B Testing enough time to generate useful data
How long is enough time? This depends on your company and how you run the test. A large part of the time it takes to get statistically significant results is the amount of traffic you get from your company's website. The less traffic, the longer it will take to run the test.
11. Asking for feedback from real customers/website visitors
One of the best ways is through surveys or polls. You can add an exit survey on your company website by asking visitors why they pressed or didn't press a certain CTA button.
12. Focus on goal metrics
Even though you'll be measuring multiple metrics, stay focused on the metrics that are the primary goals you've defined. Looking at goal metrics is usually through the conversion rate or website visitor experience.
13. Comparing conversion rates
By looking at the results, you can tell if one variation is working better than the other. However, real success is seen from statistically significant results.
For example version A has a conversion rate of 16.04% and version B has a conversion rate of 16.02% and the confidence interval for your statistical significance is 95%. Although Version A had a higher conversion rate, the results were not statistically significant. That is, version A does not increase the overall conversion rate.
14. Distinguishing customers for further suggestions/criticism
Regardless of the significance, it is important to segment the results by customer segmentation to understand how each key area responds to your variations. Common variables to differentiate customers include:
15. Planning your next A/B Test
You can try testing other features to improve certain metrics in the development of a product. You can test on different marketing channels to find the most significant results.
A/B Testing allows your company to understand what customers/visitors really want to see, especially in relation to the content and marketing offered by the company.
Testing a marketing strategy needs to be understood by company executives to develop profit-oriented products. Like the training activities that will be carried out in the Certificate of Business Management: Marketing Management program, the training does not only discuss concepts, but also case studies, group discussions, comprehensive summaries through seminars, and group assignments at the end of the program.
