A/B testing. Step-by-step guide
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Technologies similar to Cookies
Similar technologies are technics or tools, using certain characteristics to identify devices so that visits to a website can be analyzed. It includes, for example, HTML5 local storage, local Shared objects, fingerprinting techniques, tracking pixels and plugins.
For ease of reference, cookies and similar technologies are referred as "Cookies".
Usage of Cookies
Cookies enable us to provide users with certain features and supply us with information about website visits.
We also make use of cookies to make our site more user-friendly, to better understand our existing and prospective customers, including associating a user with different devices and browsers that they may use for online targeting and advertising purposes.
Steve Jobs and Steve Wozniak, Marie and Pierre Curie, John Lennon and Paul McCartney - each of these partnerships became a historical event, created something unique and extraordinary. In business and marketing, this principle is also relevant: the right combination of tools and strategies can lead to outstanding results.
Very often an advertising creative, an apt phrase, a marketing proposal is a kind of guesswork that can only reflect a subjective vision of the situation. According to Fincraft Capital s.r.o. specialists, only accurate data analytics can defeat intuitive marketing. A/B testing helps to find the most effective solutions in marketing and advertising. During testing, different versions and elements of ad units, landing pages, pop-ups, headlines, price offers are compared and combined.
How does A/B testing work?
In a typical A/B test, traffic is randomly assigned to each variant page based on a predetermined weighting. For example, if you're running a test with two landing page variants, you might split the traffic 50/50 or 60/40. To maintain the integrity of the test, visitors will always see the same variant, even if they come back later.
If you are starting a new marketing campaign and have several ideas on what direction to take, you can create a variant for each idea.
Step 1: Determine your key metric and goal
Before you start A/B testing your campaign, you should be extremely clear about the outcome you hope to achieve. For example, you may want to increase your ad's click-through rate or decrease your landing page bounce rate. A clearly defined goal will help you form a hypothesis for your A/B test.
Step 2: Formulate your hypothesis
The next step is to formulate a hypothesis to test: a clear statement related to only one variable. Whatever you're testing, you need to make sure it's clear and close to your audience.
Step 3: Create your variants
A standard A/B test will test two variants, A and B.
‘Variant A’ is usually the baseline version of what you're testing. Since you already know how this version works, it becomes the baseline version.
‘Option B’ is any in Option A that you hypothesise should improve the result. If the hypothesis is that a different colour CTA button will get more clicks, that's the version where we'll make that change.
Very often an advertising creative, an apt phrase, a marketing proposal is a kind of guesswork that can only reflect a subjective vision of the situation. According to Fincraft Capital s.r.o. specialists, only accurate data analytics can defeat intuitive marketing. A/B testing helps to find the most effective solutions in marketing and advertising. During testing, different versions and elements of ad units, landing pages, pop-ups, headlines, price offers are compared and combined.
How does A/B testing work?
In a typical A/B test, traffic is randomly assigned to each variant page based on a predetermined weighting. For example, if you're running a test with two landing page variants, you might split the traffic 50/50 or 60/40. To maintain the integrity of the test, visitors will always see the same variant, even if they come back later.
If you are starting a new marketing campaign and have several ideas on what direction to take, you can create a variant for each idea.
Step 1: Determine your key metric and goal
Before you start A/B testing your campaign, you should be extremely clear about the outcome you hope to achieve. For example, you may want to increase your ad's click-through rate or decrease your landing page bounce rate. A clearly defined goal will help you form a hypothesis for your A/B test.
Step 2: Formulate your hypothesis
The next step is to formulate a hypothesis to test: a clear statement related to only one variable. Whatever you're testing, you need to make sure it's clear and close to your audience.
Step 3: Create your variants
A standard A/B test will test two variants, A and B.
‘Variant A’ is usually the baseline version of what you're testing. Since you already know how this version works, it becomes the baseline version.
‘Option B’ is any in Option A that you hypothesise should improve the result. If the hypothesis is that a different colour CTA button will get more clicks, that's the version where we'll make that change.
Step 4: Conduct the test
In this step, divide the target audience into two groups (or more if you have more than two versions) and show one part version A and the other part version B. To achieve real performance and validity of the results, the duration of the test is important. A/B test of two versions should be run in parallel and at the same time.
Step 5: Analyse the results
Once you have a sufficient sample, you need to analyse the data collected. To determine which version performed better, study the metrics: click-through rate, bounce rate, conversion rate. Which version got a greater response from new site visitors? Which version worked best on mobile phones and which version worked best on desktops? Which traffic source was most effective? Which version had the best results? The winning version becomes the new version - the blueprint.
But the process doesn't stop there. Keep tracking the impact of the changes and keep testing. Even if the last 10 tests didn't meet your expectations.
What motivated your customer to make a purchase? Why did he walk away and leave the order in the basket unpaid? What button did he fail to click to confirm payment? Sales without A/B-testing is a movement with closed eyes, noted the managers of Fincraft Capital company. It is important to conduct tests throughout the entire sales and ordering process. This helps to clarify many issues.
Fincraft Capital Czech Republic helps platform partners to conduct anchor tests and simultaneous experiments. Our platform's analytics allows comparing several options, testing hypotheses and determining the best result.
In this step, divide the target audience into two groups (or more if you have more than two versions) and show one part version A and the other part version B. To achieve real performance and validity of the results, the duration of the test is important. A/B test of two versions should be run in parallel and at the same time.
Step 5: Analyse the results
Once you have a sufficient sample, you need to analyse the data collected. To determine which version performed better, study the metrics: click-through rate, bounce rate, conversion rate. Which version got a greater response from new site visitors? Which version worked best on mobile phones and which version worked best on desktops? Which traffic source was most effective? Which version had the best results? The winning version becomes the new version - the blueprint.
But the process doesn't stop there. Keep tracking the impact of the changes and keep testing. Even if the last 10 tests didn't meet your expectations.
What motivated your customer to make a purchase? Why did he walk away and leave the order in the basket unpaid? What button did he fail to click to confirm payment? Sales without A/B-testing is a movement with closed eyes, noted the managers of Fincraft Capital company. It is important to conduct tests throughout the entire sales and ordering process. This helps to clarify many issues.
Fincraft Capital Czech Republic helps platform partners to conduct anchor tests and simultaneous experiments. Our platform's analytics allows comparing several options, testing hypotheses and determining the best result.