A/B Testing Explained: Going Beyond the Basics with Examples

Before getting all technical and diving deep into the world of A/B testing, let us first get a little bit of Matrix feeling into us. Sounds interesting? Cool.

In "The Matrix," the main character, Neo, chooses between taking a red or a blue pill. If one wants to see which pill results in more people waking up to the truth of the Matrix, one could give the red pill to one group and the blue pill to another. Then, you could compare the results to see which pill was more successful in helping people realize the truth.

What is A/B Testing?

A/B testing is like giving two groups the red and blue pills to see which works best. A/B testing is a statistical method used to compare two versions of a webpage or app to determine which one performs better based on user behavior. Marketing and product development use A/B testing to determine which version of a website, ad, or product performs better. Online businesses need A/B testing to optimize websites, apps, and marketing campaigns. Companies can improve user experience, conversion rates, and revenue by comparing variations. In this blog post, we'll discuss A/B testing, why it's essential, and best practices. This post will help beginners and advanced A/B testers.

What is Brand Lift?

Importance of A/B Testing

A/B testing is an essential technique in the world of marketing to optimize various aspects of websites, apps, and marketing campaigns. Here are some of the critical reasons why A/B testing is essential:

    1. Data-Driven Decision-Making: A/B testing allows marketers and analysts to make data-driven decisions based on real user data rather than assumptions or hunches. This helps to optimize marketing strategies and improve the user experience.
    2. Improved User Experience: A/B testing can help improve the user experience by identifying which design or content changes are most effective at increasing engagement or conversion rates. This can lead to increased customer satisfaction and loyalty.

    3. Increased Conversion Rates: A/B testing can help increase conversion rates by identifying which elements on a website or app are most effective at driving conversions. This can lead to increased revenue and profitability.

    4. Cost Savings: A/B testing can help reduce the risk of making costly mistakes by allowing marketers and analysts to test changes before implementing them on a large scale.

    5. Continuous Improvement: A/B testing promotes a culture of continuous improvement by encouraging experimentation and testing new ideas. This can lead to ongoing optimization and innovation in marketing and design strategies.

  • Types of A/B Testing

    One can carry out A/B tests either in an online or offline manner.

    • Online Tests: Online A/B testing involves comparing two or more versions of a webpage or app in a live online environment with real users interacting with the site. This type of A/B testing allows you to track user behavior, such as clicks, conversions, and time spent on the site. As a result, it can optimize website design, improve user experience, and increase conversion rates.
    • Offline Tests: Offline A/B testing, on the other hand, involves comparing two or more versions of a product or marketing material outside of a digital environment. This type of testing is typically used in more traditional marketing contexts, such as print ads, direct mail campaigns, or in-store promotions. Offline A/B testing allows you to test different variations of marketing materials with real-world customers to determine which version is more effective in driving sales or other key performance indicators.

    Let us look at examples of online A/B tests:

    1. A/B testing for emails: HubSpot, a marketing software company, used A/B testing to improve its email open rates. The company tested different subject lines and preview text to see which resulted in the highest open rates.
    2. A/B testing for ads: Slack, a team communication platform, used
      A/B testing to optimize its Facebook ads. The company tested different ad images and copied them to see which resulted in the highest click-through rates and conversions.

    3. A/B testing for landing pages: Airbnb, a vacation rental
      platform, used A/B testing to improve its booking process. The company tested different versions of its checkout page, such as changing the layout and wording of the call-to-action button, to see which one resulted in the highest conversion rates.

    These are just a few examples of companies using A/B testing to optimize their marketing strategies and improve the user experience. By testing different variations of key elements, businesses can gain insights into what works best and make data-driven decisions that increase engagement, conversions, and revenue.

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    How is A/B Testing Carried Out?

    The A/B test process consists of two major steps: Identifying the elements to test and then running the test.

    Identifying the Elements

    1. Identify Issues: Before the test commences, one must have the base to start the test. One can gather ideas for the test from the following tunnels: Customer support and sales team, surveys, on-site customer behavior, competitors' strategies, etc.
    2. Identify Which Metric to be Tracked: In this step, one needs to identify the goals they want to achieve by deciding the improvement metric. Then, they will use the metric to draw the outcome of the A/B tests.
    3. Develop a Hypothesis: Once they identify the goal, they need to develop hypotheses (backed up by data) about changes that will improve the metric.

    How to Run the Test

    a. Create Variations: In this step, one will implement the hypothesis. In addition, they'll also decide which A/B testing approach they will use - split, multi-page, or multivariate testing. Then, create the test version of the page.

    For instance, one could change the CTA copy or button size to increase the click-through rate. In addition, reducing the signup field or adding testimonials besides the form can be a variation if they wish to make more form submissions.

    b. Determine Audience Size and Divide Equally: To accurately determine the success of the A/B test, select the appropriate user sample size that divides them evenly. The number of groups will be determined by the testing method used.

    For example, in split testing, two distinct groups are required, whereas, in multi-page testing, the number of groups is determined by the number of test versions.

    Furthermore, having the appropriate sample size is critical for validating test results.

    Rule of thumb for A/B sample size: According to a general rule of thumb, one requires a least of 30,000 visitors and 3,000 conversions for a very reliable test for each variant.

    c. Run the Test: Once all is set up - the variations and sample size, one can run the test using Google Optimize, Crazyegg, and Ominiconvert. Then let the test run for an adequate time until they begin to interpret the results.

    One might wonder - How long should they run the A/B test?

    Typically, one should run a test until they have statistically significant data before they make any changes. Besides, the timings depend on the variations, sample size, and test goals.

    Keep an experiment running until at least one of these conditions has been met:

    • Two weeks have passed to account for cyclical variations in web traffic during the week.
    • At least one variant has a 95 percent probability of beating the baseline.

    d. Decipher Results: Most commonly, one will use A/B test tools to monitor the performance and analyze the collected data. That is the primary approach every marketer must be familiar with.

    However, seeing improved metrics on one of the versions doesn't mean one should immediately implement the changes. Instead, as many factors influence the A/B test, one needs to activate their judgment phase and implement the changes depending on whether they will bring better returns on investment. The following steps are considered while deciphering results:

    • Look at the Conversion Rates for Each Group: Calculate the conversion rate as the number of conversions divided by the total number of visitors in each group.
    • Determine if the Difference is Statistically Significant: Use a t-test or chi-square test to determine whether the difference in conversion rates between the two groups is statistically significant. The p-value from the test will tell one the probability of obtaining the observed difference by chance alone. If the p-value is less than the chosen significance level (typically 0.05 or 0.01), one can reject the null hypothesis and conclude that the
      difference is statistically significant. ​
    t-test and p-value
    chi squared and p-value


                 





    • Look at the Effect Size: The effect size tells you how effective the difference is in practical terms. A small effect size may not be meaningful, even if statistically significant. Common effect size measures for A/B tests include Cohen's d or the relative improvement or lift.
    • Consider Other Factors: It's essential to consider other factors that may have influenced the results. For example, if the test was run during a holiday, there may have been differences in visitor behavior that could have affected the results. Similarly, if there were technical issues during the test, this could have affected the results.
    • Draw Conclusions and Take Action: Based on the results of the A/B test, conclude the effectiveness of the treatment and decide whether to implement it permanently. If the treatment is effective, make sure to implement it in a way that ensures the results are sustained. If the treatment is impractical, use the results to inform future experiments and continue testing new ideas.

    e. Devise a better test for next time by figuring out mistakes done with this test:

    5 mistakes to avoid while ab testing

    Best Practices for A/B Testing

    Right Item

    Testing the right item is a key A/B testing best practice. Testing conversion and click-through rates are crucial. By prioritizing the critical elements to test, you can ensure that the A/B test results are meaningful and actionable, improving website or marketing campaign performance.

    Sample Size

    A/B tests must consider sample size. A large sample size ensures statistical significance and population representation. Small samples can yield unreliable results. To avoid outliers and biases, consider sample variation. A/B testing can inform data-driven decisions by carefully selecting a sample size.

    Trustworthy Data

    A/B testing requires trustworthy data. Decisions require accurate, reliable data. Data collection should be bias-free, error-free, and consistent. Using randomized samples and verifying data accuracy can help ensure data integrity. Avoid making decisions based on incomplete or inconclusive data and interpret results within business goals. A/B testing can improve the website or marketing performance and growth by gathering reliable data.

    Correct Hypothesis

    A/B testing requires a correct hypothesis. Hypotheses predict A/B test results. A good hypothesis is specific, testable, and aligned with business goals. By creating a testable hypothesis, you can focus on the A/B test and gain actionable insights. A well-formed hypothesis can also guide data-driven decision-making and prevent incorrect A/B test conclusions.

    User Behavior

    User behavior is important in A/B tests. Navigation includes website and marketing campaign use. User behavior analysis aids A/B test optimization. Understanding user behavior can help you select elements to test and ensure the A/B test meets user needs. User behavior-focused A/B testing improves marketing and website performance.

    Test Duration

    A/B tests must be properly timed. Long enough to collect enough data and ensure statistical significance. However, too much time can slow decision-making and progress. In addition, seasonality and external events can affect the test duration. Therefore, A/B testing can inform data-driven decisions by carefully selecting the test duration.

    No Mid-Test Change

    A/B tests should never change midway. Changing test variations during testing can introduce biases and invalidate results. Mid-test changes can also slow the test. Before starting the test, carefully plan and execute the A/B test variations. A/B testing can improve website and marketing performance by avoiding mid-test changes.

    One Element at a Time

    A/B tests should test one element at a time. Testing multiple elements at once can make it hard to identify the culprit. Testing one element at a time helps optimize and identify the most effective changes. A/B testing can improve marketing and website performance by testing one element at a time.

    Document Findings

    A/B tests must be documented. Record the hypothesis, test duration, sample size, variations, and results. Document the A/B test's findings and how they will inform future optimization efforts. A/B testing can help teams collaborate and make data-driven decisions by documenting results.

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    Metrics to Follow for Online A/B Tests

    Conversion Rate

    Conversion rate is a metric that measures the percentage of users who take a desired action on a website.

    What it Tracks: Conversion rate tracks the rate at which users complete a desired action on a website, such as making a purchase or filling out a form.

    Formula Used: (number of conversions / total number of visitors) x 100

    Click-Through Rate

    Click-through rate is a metric that measures the percentage of users who click on a link or advertisement.

    What it Tracks: Click-through rate tracks the rate at which users click on a specific link or advertisement and indicates how effective the link or advertisement is at generating user interest.

    Formula Used: (number of clicks / total number of impressions) x 100.

    Bounce Rate

    Bounce rate is a metric that measures the percentage of users who leave a website after viewing only one page.

    What it Tracks: Bounce rate tracks the rate at which users navigate away from a website after viewing only one page and indicates how engaging and relevant the website content is to the user.

    Formula Used: (number of single-page visits / total number of visits) x 100.

    Net Promoter Score

    Net Promoter Score is a metric that measures customer loyalty and satisfaction based on their likelihood to recommend a product or service.

    What it Tracks: Net Promoter Score tracks the likelihood that a customer will recommend a product or service and is a valuable indicator of overall customer satisfaction and loyalty.

    Formula Used: % of promoters(customers who would not recommend the product or service) - % of detractors(customers who would recommend the product or service) = Net Promoter Score. 

    The score can range from -100 to 100, with higher scores indicating greater customer loyalty and satisfaction.

    Basket

    Basket is an A/B test metric that measures the average number of items added to a user's cart on a website or app.

    What it Tracks: Basket tracks the average number of items users add to their cart and can help businesses optimize product offerings, pricing, and user experience.

    Formula Used: total number of items added to carts / total number of sessions.

    Checkout Rate

    Checkout rate is a metric that measures the percentage of users who complete a purchase after adding items to their cart.

    What it Tracks: Checkout rate tracks the rate at which users complete a purchase after adding items to their cart and indicates the effectiveness of the checkout process and overall user experience.

    Formula Used: (number of completed purchases / total number of users who added items to their cart) x 100.

    Order

    Order is an A/B test metric that measures the average value of an order placed on a website or app.

    What it Tracks: Order tracks the average amount of money customers spend per transaction and can help businesses optimize pricing, product offerings, and user experience.

    Formula Used: total revenue / total number of orders.

    Cost Per Acquisition

    Cost per acquisition (CPA) is an A/B test metric that measures the cost of acquiring a customer through a specific marketing campaign or channel.

    What it Tracks: CPA tracks the efficiency of a marketing campaign or channel by measuring how much it costs to acquire a customer and can help businesses optimize their marketing spend.

    Formula Used: total cost / number of conversions(customers acquired)

    Conclusion

    In conclusion, A/B testing is a powerful tool that can help businesses optimize their websites, apps, and marketing campaigns. By comparing the performance of different variations, companies can make data-driven decisions to improve user experience, increase conversion rates, and drive revenue. To ensure the success of an A/B test, it's essential to follow best practices such as setting a correct hypothesis, using reliable data, testing one element at a time, and documenting findings. With the right approach, A/B testing can help businesses stay ahead of the competition and achieve their goals.

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