A/B Testing Statistics 2024 - Everything You Need to Know

Are you looking to add A/B Testing to your arsenal of tools? Maybe for your business or personal use only, whatever it is – it’s always a good idea to know more about the most important A/B Testing statistics of 2024.

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How much of an impact will A/B Testing have on your day-to-day? or the day-to-day of your business? Should you invest in A/B Testing? We will answer all your A/B Testing related questions here.

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On this page, you’ll learn about the following:

Best A/B Testing Statistics

Best A/B Testing Statistics

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A/B Testing Market Statistics

As the chart below demonstrates, diminishing returns make 99% impractical for most marketers, and 95% or even 90% is often used instead for a cost efficient level of accuracy. ^[0]
93% of US companies do A/B testing on their email marketing campaigns. ^[1]
According to statistics from QY Research, the A/B testing software market was worth $485 million in 2018, and expectations are that it will continue to grow. ^[1]
With 60% of companies already using it and another 34% planning to use it, A/B testing is the number one method used by marketers to optimize conversion rates, according to recent reports. ^[1]
Copy optimization is employed by 59% of marketers in their daily work, while another 29% plan to adopt it. ^[1]
In addition to A/B testing, surveyed marketers said they also rely on online surveys and customer feedback (58%), as well as customer journey analysis (55%). ^[1]
Only 28% of marketers are actually satisfied with the conversion rates achieved after A/B testing. ^[1]
Email marketing A/B testing statistics further show 37% test content, 36% test. ^[1]

A/B Testing Software Statistics

According to statistics from QY Research, the A/B testing software market was worth $485 million in 2018, and expectations are that it will continue to grow. ^[1]

A/B Testing Latest Statistics

Within hours, the alternative format produced a revenue increase of 12% with no impact on user. ^[2]
The email using the code A1 has a 5% response rate , and the email using the code B1 has a 3% response rate. ^[2]
A50/ 1,000 (5%)10/ 500 (2%). ^[2]
(8%)Variant B30/ 1,000 (3%)25/ 500 (5%)5/ 500 (1%). ^[2]
In this example, a segmented strategy would yield an increase in expected response rates from to – constituting a 30% increase. ^[2]
So Fisher’s exact test gives p. ^[3]
In our example, using Pearson’s chisquare test we obtain χ2 ≈ 3.825, which gives p. ^[3]
Anyway, if in our case we knew the true value of σX=100 and σX=90, then we would obtain z ≈ 1.697, which corresponds to a p. ^[3]
In our example, using Student’s ttest we obtain t ≈ 1.789 and ν = 29, which give p. ^[3]
In our example, using Welch’s ttest we obtain t ≈ 1.848 and ν ≈ 28.51, which give p. ^[3]
In our example, using MannWhitney U test we obtain u = 76 which gives p. ^[3]
The null hypothesis here would be no reviews generates a conversion rate equal to 8% The alternative hypothesis here would be adding reviews will cause conversion rate to be more than 8%. ^[4]
When conducting hypothesis testing, you cannot “100%” prove anything, but you can get statistically significant results. ^[4]
You conducted an A/B test and got the following results Original page conversion rate – 8% Variation 1 conversion rate – 12%. ^[4]
The conversion page for that portion of the visitors is 8%. ^[4]
The conversion rate for that group is 12%. ^[4]
It usually shows you some percentage between 0 and 100% and determines how statistically significant the results are. ^[4]
In other words, the confidence level is 100% minus level of significance (1%, 5% or 10%). ^[4]
If you see a confidence level of 95%, does it mean that the test results 95% accurate?. ^[4]
Does it mean that there is 95% probability that the test is accurate?. ^[4]
It means that if you repeat this test over and over again the results will match the initial test in 95% of cases. ^[4]
It means that you are confident that 5% of your test samples will choose the original page over the challenger. ^[4]
We would say that less than 5% margin of error. ^[4]
At some point of time , you even may get a significant result (confidence level above 90%). ^[4]
Frequentist probability defines relative frequency with which some even to occur (remember, earlier on we said that 95% confidence level means that if you continue the experiment over and over again, it will have the same result in 95% of cases). ^[4]
The fact that the test reached a 95% confidence interval is not enough to stop the test. ^[4]
If the test satisfies sample size and duration conditions, and reached 95% confidence, only then you can stop it. ^[4]
A/B testing is a decision making method, but cannot give you a 100% accurate prediction of your visitors’ behavior. ^[4]
You want to learn but not be fully 100% sure because that will make you too slow—not adjusting rapidly enough to user wishes.”. ^[5]
In terms of conversion optimization, Marketing Experiments gives a great example of variance The two images above are the exact same—except that the treatment earned 15% more conversions. ^[5]
It had a 0% chance to beat the original. ^[5]
For practical purposes, all you really need to know is that 80% power is the standard for testing tools. ^[5]
So if your tool says something like, “We are 95% confident that the conversion rate is X% +/. ^[5]
For all practical purposes, here’s the difference The confidence interval is what you see on your testing tool as “20% +/. ^[5]
2%,” and the margin of error is “+/. ^[5]
You say I have 95% confidence that it will take you about 60 minutes,. ^[5]
So your margin of error is 20 minutes, or 33%. ^[5]
If she is coming at 11 a.m., you might say, “It will take you 40 minutes, plus or minus 10 minutes,” so the margin of error is 10 minutes, or 25%. ^[5]
While both are at the 95% confidence level, the margin of error is different.”. ^[5]
So say you take only the top 10% of students and give them a second test where they, again, guess randomly on all questions. ^[5]
It was very interesting to see how the results dramatically dropped during the ‘hard sale phase’ with 70% and more—but it recovered one week after the phase ended. ^[5]
For example, we have no way of knowing with 100% accuracy how the next 100,000 people who visit our website will behave. ^[0]
In a perfect world, the sample would be 100% representative of the overall population. ^[0]
Our true conversion rate would then be 10%. ^[0]
In other words, if you selected a sample of 10 visitors, 1 of them (10%). ^[0]
You are probably tempted to say 10.3%, but that’s inaccurate. ^[0]
10.3% is simply the mean of our sample. ^[0]
The original page above has a conversion rate of 10.3% plus or minus 1.0%. ^[0]
The 10.3% conversion rate value is the mean. ^[0]
The ± 1.0 % is the margin for error, and this gives us a confidence interval spanning from 9.3% to 11.3%. ^[0]
10.3% ± 1.0 % at 95% confidence is our actual conversion rate for this page. ^[0]
What we are saying here is that we are 95% confident that the true mean of this page is between 9.3% and 11.3%. ^[0]
In the above example, we are saying that 19 out of every 20 samples tested WILL, with 100% certainty, have an observed mean between 9.3% and 11.3%. ^[0]
Confidence levels are often confused with significance levels due to the fact that the significance level is set based on the confidence level, usually at 95%. ^[0]
If you want 99% certainty, you can achieve it, BUT it will require a significantly larger sample size. ^[0]
In high stakes scenarios , testers will often use 99% confidence intervals, but for the purposes of the typical CRO specialist, 95% is almost always sufficient. ^[0]
The industry standard significance level is 5%, which means we are seeking results with 95% accuracy. ^[0]
So, to answer our original question We achieve statistical significance in our test when we can say with 95% certainty that the increase in Variation B’s conversion rate falls outside the expected range of sample variability. ^[0]
The example is with a commonlyused 95% threshold for the confidence level, equivalent to a onesided p value threshold of 0.05. ^[6]
A 95% statistical confidence would only be observed “by chance” 1 out of 20 times, assuming there is no improvement. ^[6]
Knowing that a result is statistically significant with a p value of say 0.05, equivalent to 95% confidence, there still remains a probability of #2 being true. ^[6]
This means there is good ground to infer that the improvement, if any, is less than 5%.”. ^[6]
For example, a statistically significant improvement of 2% might not be worth implementing if the winner of the test will cost more to implement and maintain than what these 2% would produce in terms of revenue over the next several years. ^[6]
Here is what this would look like, with a accompanying confidence interval (read “Significance” as “Confidence”, the interval is from 0.4% to 3.6% lift). ^[6]
While the observed lift is 20% and it has a high statistical significance, the 95% confidence interval shows that the true value for the lift is likely to be as low as 2.9% – blue numbers bellow % change are the confidence interval bounds. ^[6]
Forgetting that the alternative hypothesis is “A is better than control” and substituting it with “A is 20% better than control” on the fly makes for a perfectly bad interpretation. ^[6]
With sequential evaluation one gains flexibility in when to act on the data which comes with added efficiency due to the 20. ^[6]
By convention, this false positive rate is usually set to 5% for tests where there is not a meaningful difference between treatment and control, we’ll falsely conclude that there is a “statistically significant” difference 5% of the time. ^[7]
Tests that are conducted with this 5% false positive rate are said to be run at the 5% significance level. ^[7]
Using the 5% significance level convention can feel uncomfortable. ^[7]
By following this convention, we are accepting that, in instances when the treatment and control experience are not meaningfully different for our members, we’ll make a mistake 5% of the time. ^[7]
We’ll label 5% of the non cat photos as displaying cats. ^[7]
Say we want to know if a coin is unfair, in the sense that the probability of heads is not 0.5 (or 50%). ^[7]
Say we observe that 55% of 100 flips are heads. ^[7]
In our case, the null hypothesis is that the coin is fair, the observation is 55% heads in 100 flips, and the p value is about 0.32. ^[7]
It comes back to that 5% false positive rate that we agreed to accept at the beginning we conclude that there is a statistically significant effect if the p value is less than 0.05. ^[7]
In the coin example above, we observed 55% heads in 100 flips, and concluded we had insufficient evidence to label the coin as unfair. ^[7]
To calculate the rejection region, we once more assume the null hypothesis is true , and then define the rejection region as the set of least likely outcomes with probabilities that sum to no more than 0.05. ^[7]
In the case of the simple coin experiment, the rejection region corresponds to observing fewer than 40% or more than 60% heads. ^[7]
We call the boundaries of the rejection region, here 40% and 60% heads, the critical values of the test. ^[7]
We then go through a thought exercise given the observation, what values of the null hypothesis would lead to a decision not to reject, assuming we specify a 5% false positive rate?. ^[7]
For our coin flipping example, the observation is 55% heads in 100 flips and we do not reject the null of a fair coin. ^[7]
Nor would we reject the null hypothesis that the probability of heads was 47.5%, 50%, or 60%. ^[7]
There’s a whole range of values for which we would not reject the null, from about 45% to 65% probability of heads. ^[7]
Because we’ve mapped out the interval using tests at the 5% significance level, we’ve created a 95% confidence interval. ^[7]
The interpretation is that, under repeated experiments, the confidence intervals will cover the true value 95% of the time. ^[7]
According to recent research, average open rates across more than a dozen industries ranging from 25 to 47 percent. ^[8]
Unlike the frequentist approach, the Bayesian approach provides actionable results almost 50% faster while focusing on statistical significance. ^[8]
Can you be 100% objective at all times?. ^[8]
Sure, someone else changed their sign up flow and saw a 30% uplift in conversions. ^[8]
The test ran for two weeks and produced a 25% uplift in owner registration. ^[8]
The new variation increased page visits by about 5%. ^[8]
When we run our experiment for one month, we noticed that the mean conversion rate for the Control group is 16% whereas that for the test Group is 19%. ^[9]
For the significance level of 0.05, if the p value is lesser than it hence we can reject the null hypothesis Confidence intervalThe confidence interval is an observed range in which a given percentage of test outcomes fall. ^[9]
Generally, we take a 95% confidence interval. ^[9]
Your test results Test “B” converted 34% better than Test “A”. ^[10]
I am 99% certain that the changes in Test “B” will improve your conversion rate. ^[10]
Variant B’s conversion rate (1.14%) was 14% higher than variant A’s conversion rate (1.00%). ^[11]
You can be 95% confident that variant B will perform better than variant A. ^[11]
For example, if you run a test with a 95% significance level, you can be 95% confident that the differences are real. ^[11]
Most of the time, this is set at 95%. ^[12]
your AB test results are statistically significant at a level of 95%. ^[12]
However, 95% of the time, your results will not be due to chance. ^[12]
AB test results are considered “significant” if the probability of a Type 1 error is lower than our pre determined “alpha” value (which is usually 5%). ^[12]
It is 100% minus our Confidence Level (which is usually 95%). ^[12]
For example, with an Alpha value of 0.03 (or 3%). ^[12]
Thomas Bayes Bayesian probability—coined in the 18 th century by Presbyterian minister Thomas Bayes—is believed to have been developed to counter David Hume’s argument that a seemingly “miraculous” event was unlikely to be a true miracle. ^[12]
100% bConfidence LevelIf the Alpha value is the maximum limit you allow for the probability that your results are due to chance, the Confidence Level you choose is the minimum probability that your results are not due to chance. ^[12]
If you set your Confidence Level at 95% then 19 times out of 20 your results will reflect a real change. ^[12]
The level of Statistical Significance you choose (90%, 95% or 99%). ^[12]
Confidence If your AB test results are statistically significant at a level of 95% they could still be due to random variation once in every 20 times. ^[12]
It is 100% minus our Confidence Level (which is usually 95%). ^[12]
Testing with a statistical significance level of 95% will produce more false positives and fewer false negatives than testing with a significance level of 99%. ^[12]
Bayesian probability—coined in the 18 th century by Presbyterian minister Thomas Bayes—is believed to have been developed to counter David Hume’s argument that a seemingly “miraculous” event was unlikely to be a true miracle. ^[12]
The empirical p value is around 1% and therefore the result is statistically significant. ^[13]
A 6% increase in video engagement. ^[14]
ComScore A/B tested logos and testimonials to increase social proof on a product landing page and increased leads generated by 69%. ^[14]
Amazon is very familiar with A/B testing – they’re constantly testing to improve UX and conversion rates Surprisingly, average conversion rates for e commerce sites continue to hover between 1% and 3%. ^[15]
A threshold of 95% is generally adopted. ^[15]
Should the sample size be low, exercise caution when analyzing the results, even if the test indicates a reliability of more than 95%. ^[15]
The study is thus based on observation, with a reliability of 95%. ^[15]
So long as the test has not attained a statistical reliability of at least 95%, it is not advisable to make any decisions. ^[15]
If a test takes too long to reach a reliability rate of 95%, it is likely that the element tested does not have any impact on the measured indicator. ^[15]
Over 50% of companies use test prioritization frameworks. ^[1]
77% of organizations do A/B testing on their website and 60% on their landing page. ^[1]
59% of organizations run A/B testing on emails. ^[1]
Simple subject lines get 541% more responses than creative ones. ^[1]
In fact, the report shows that the yearly growth will remain steady at 12.1% by the end of 2025, when it is expected for the industry to reach a little over a billion US dollars. ^[1]
Usability testing is the gateway to optimizing conversion rates for 49% of companies. ^[1]
60% of organizations find A/B testing to be highly valuable for optimizing their conversion rates. ^[1]
Almost two thirds (63%). ^[1]
Just 7% disagree, admitting that the implementation of A/B testing is a daunting task. ^[1]
Successful A/B testing can bring a 50.7% increase in the average revenue per unique visitor for ecommerce sites. ^[1]
A/B testing trends reveal onethird of A/B testers first start evaluating elements such as the callto action button, 20% test headlines, 10% test layouts, and 8% website copies. ^[1]
Another study shows that to reach statistical significance or a 95% reliability rate, you need an A/B testing sample size of 5,000 unique visitors per variation and 100 conversions on each objective by variation. ^[1]
Test prioritization frameworks are used by over 50% of companies. ^[1]
A/B testing analysis indicates a great number of companies are improving, while A/B testing statistics point to a solid 56.4% of companies using a test prioritization framework. ^[1]
The good news is that compared to the year before, the number of companies that were not using a test prioritization framework (43.6%). ^[1]
77% of organizations perform A/B testing on their website and 60% on their landing page. ^[1]
Corporate websites are the most common target of A/B testing, with 77% of organizations running such tests in a bid to improve their CRO. ^[1]
71% do 2 3 such tests monthly. ^[1]
It’s no surprise then that 60% of all organizations experiment with this methodology in a bid to improve their website’s performance. ^[1]
Emails with a real person’s name as the sender can generate 0.53% more opens. ^[1]
For example, sending a more personal email from an individual instead of a company in the ‘Sender’ field increases open and click through rates by 0.53% and 0.23%, respectively. ^[1]
Email related A/B testing trends show that 59% of organizations are already implementing this practice. ^[1]
Another 58% perform A/B testing on paid search campaigns. ^[1]
Just under 40% of companies worldwide test an email’s subject. ^[1]
A solid 39 percent of companies worldwide start by testing the email’s subject line as the most important element, the bait used to lure customers into clicking. ^[1]
the send dates and time, 32% focus on the sender address, while 39% look at the images in the email. ^[1]
Other things that are subjected to A/B testing are offers (28%) and preheaders (23%). ^[1]
Bing increases its revenue by 12% with an A/B test. ^[1]
They tested out various photos, videos, and website layouts, which increased the campaign’s website sign up rate by a whopping 140%, boosting the funds gathered by a massive $75 million. ^[1]
One of the many A/B tests they ran was in relation to the buttons ‘Learn More’, ‘Join Us Now’, and ‘Sign Up Now’ which revealed that ‘Learn More’, as opposed to the default ‘Sign Up’ collects almost 20% more signups per visitor. ^[1]
For example, SAP increased their conversion rate by 32.5% by using the color orange, while statistics behind A/B testing conducted by Performable showed that the company improved their conversion rate by 21% by using the color red. ^[1]

I know you want to use A/B Testing Software, thus we made this list of best A/B Testing Software. We also wrote about how to learn A/B Testing Software and how to install A/B Testing Software. Recently we wrote how to uninstall A/B Testing Software for newbie users. Don’t forgot to check latest A/B Testing statistics of 2024.

Reference

conversionsciences – https://conversionsciences.com/ab-testing-statistics/.
truelist – https://truelist.co/blog/ab-testing-statistics/.
wikipedia – https://en.wikipedia.org/wiki/A/B_testing.
towardsdatascience – https://towardsdatascience.com/a-b-testing-a-complete-guide-to-statistical-testing-e3f1db140499.
invespcro – https://www.invespcro.com/blog/ab-testing-statistics-made-simple/.
cxl – https://cxl.com/blog/ab-testing-statistics/.
analytics-toolkit – https://blog.analytics-toolkit.com/2017/statistical-significance-ab-testing-complete-guide/.
netflixtechblog – https://netflixtechblog.com/interpreting-a-b-test-results-false-positives-and-statistical-significance-c1522d0db27a.
vwo – https://vwo.com/ab-testing/.
analyticsvidhya – https://www.analyticsvidhya.com/blog/2020/10/ab-testing-data-science/.
neilpatel – https://neilpatel.com/ab-testing-calculator/.
surveymonkey – https://www.surveymonkey.com/mp/ab-testing-significance-calculator/.
convertize – https://www.convertize.com/ab-testing-statistics/.
inferentialthinking – https://inferentialthinking.com/chapters/12/1/AB_Testing.html.
optimizely – https://www.optimizely.com/optimization-glossary/ab-testing/.
abtasty – https://www.abtasty.com/ab-testing/.

How Useful is a B Testing

At its core, A/B testing is a method used to compare two versions of a web page, email, or advertisement to determine which one performs better. By showcasing the two versions to different sets of users and measuring the response rates, marketers are able to gather valuable insights about what resonates with their audience and ultimately improve their conversion rates.

One of the biggest advantages of A/B testing is that it allows for data-driven decision-making. Instead of relying on gut feelings or assumptions, businesses can test their ideas in a controlled environment and see what truly works. This not only helps in optimizing customer experiences but also ensures that resources are allocated efficiently, leading to higher ROI.

Furthermore, A/B testing can uncover hidden opportunities for growth. By testing different variables such as headlines, call-to-action buttons, or even color schemes, marketers can fine-tune their messaging and design to appeal to a wider audience. Small changes can often yield significant results, making A/B testing a cost-effective way to drive conversions and boost sales.

In addition, A/B testing promotes a culture of continuous improvement within organizations. Instead of settling for mediocrity, businesses are encouraged to explore new ideas and test them rigorously. This mindset of experimentation and learning can lead to innovation and competitiveness in the long run.

However, A/B testing is not without its limitations. It requires a certain level of expertise and resources to set up and interpret the results accurately. Testing too many variables at once can also lead to inconclusive findings, making it challenging to draw meaningful conclusions. Furthermore, there is always a risk of potential biases or errors in the testing process, which can skew the results and hinder decision-making.

Despite these challenges, the benefits of A/B testing far outweigh the drawbacks. In today’s fast-paced digital landscape, where competition is fierce and consumer expectations are constantly evolving, having a data-driven approach to marketing is essential. A/B testing allows businesses to stay ahead of the curve, adapt to changing trends, and deliver personalized experiences that resonate with their target audience.

In conclusion, A/B testing is a valuable tool for businesses looking to optimize their online presence and drive results. While it may require time, effort, and expertise, the insights gained from testing can be instrumental in refining strategies, improving user experiences, and ultimately achieving business goals. As technology continues to advance and consumer behavior continues to evolve, A/B testing will remain a vital component of any successful marketing strategy.

In Conclusion

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We tried our best to provide all the A/B Testing statistics on this page. Please comment below and share your opinion if we missed any A/B Testing statistics.

A/B Testing Statistics 2024 – Everything You Need to Know