Attention marketers, salespeople, and entrepreneurs—there’s a vital goal that unites us all:
Achieving more conversions!!!
Whether it’s increased sales, sign-ups, event attendees, or other valuable actions, driving higher conversion rates is paramount.
While some may initially focus on increasing customer acquisition budgets, this approach can quickly consume your marketing resources without guaranteeing sustained growth.
That’s where Conversion Rate Optimization (CRO) steps in.
CRO is about maximizing the potential of your existing traffic by fine-tuning your website and strategies to ensure more visitors take desired actions.
In this article, I’ll uncover 30 crucial conversion rate statistics.
From industry benchmarks to average rates across niches, these insights will arm you with the knowledge to optimize your site conversion strategies effectively.
Let’s dive in and harness the power of CRO…shall we?
1. The average conversion rate for websites is 2.35%
Conversion rates vary across industries, but taking the average across several niches, the number sits at 2.35%. This means if your website is converting at the 2-3% mark, you’re doing okay (on a broad scale)
2. Top websites have a conversion rate of 11% or more.
Converting at 2 – 3% is fair if you’re starting out, if you want to play in the big leagues, and if the top 10% of websites convert at 11% and above.
This means you need to roll out a CRO strategy and implementation plan to achieve that.
3. Food & Beverage is the industry with the highest conversion rate at 7.9%.
Conversion rates differ by industry, and the food/beverage industry takes the cake here (pun intended).
4. The highest B2B average conversion rate by industry is professional services (4.6%)
In B2B, the professional services industry has the highest conversion rates at 4.6%.
5. The B2B industry with the highest form rate conversion is industrial at 2.8%
Online forms are a big part of any website (to collect leads, to get sign-ups, etc). The industrial niche has the highest conversion rate.
If you thought site speed only mattered for SEO, think again. Just a 1-second delay in your site loading time can hurt your conversions by up to 7%.
7. Companies spend just $1 on conversion rate optimization for every $92 spent on customer acquisition.
This shocking statistic highlights just how huge the gap is between the amount we spend on getting traffic and the amount we spend turning that traffic into customers. Many businesses are spending too much on traffic and not enough on conversions. Double your conversion rate, and you can afford to half your traffic.
8. Increasing your number of landing pages from 10 to 15 increases leads by 55%.
Making landing pages for each of your campaigns is good practice. Businesses that do this convert more of their traffic to leads.
9. If you want to build a good conversion rate for your Google Ads campaigns, go for a conversion rate higher than 5.31%. For perspective, the top 25% of companies advertising with Google Ads maintain a conversion rate of 11.45%.
10. When you include a video on your landing page, it can increase your conversion rates by up to 80%.
11. ¼ of companies cite rapid improvements in technology as their greatest barrier to improving conversion rates.
While increasing their website conversion rates, most organizations struggle to adopt novel technologies, while 20% had problems understanding online behavior at scale and 20% understanding points of improvement.
12. CTAs used as anchor text in blogs improve conversion rates by up to 121 percent more than banner ads.
A decade ago, banner ads proliferated the internet. They were so ubiquitous that most people just tended to tune them out because they got used to seeing so many of them online. This is a phenomenon called “banner blindness” and is one perfect reason why your CTAs are much better integrated into anchor text.
The majority of marketers (about 35%) divide the number of sales by qualified leads as the best way to calculate their lead conversion rate.
Other popular methods include dividing the number of sales into the total number of leads (32%), contacted leads (11%), and opportunities (10%), which sales conversion rate statistics further reveal.
39.3% do follow a process but have yet to structure and document it, and one in five marketers reported not following a CRO process at all.
As a research-oriented and data-driven approach, CRO works best when its stages are structured rather than handled reactively and positioned within the larger marketing efforts efficiently.
Depending on your type of business, you now know where to focus your marketing efforts for the best ROI.
Running up to a couple of tests per month is the most common test velocity among professionals, and about 9% of optimizers run more than 20 tests a month.
Running client-side tests are the most popular at 55%, while 17% run server-side tests and 27% do both, conversion rate optimization stats reveal. A/B tests are the most popular types and are used by almost all optimizers surveyed. A little over one-third also tests more than one variation.
17. Less than 0.11% of the total websites online are using CRO tools or running tests.
According to Builtwith, a tool that tracks the software that websites are using, they can only find 1,110,585 sites using CRO testing tools at this time.
Sources:
https://startupbonsai.com/conversion-rate-optimization-statistics/
https://99firms.com/blog/cro-statistics/#gref
https://marketing.dynamicyield.com/benchmarks/conversion-rate/
https://www.ruleranalytics.com/blog/insight/conversion-rate-by-industry/
https://www.convertcart.com/blog/ecommerce-conversion-rate-by-industry
Information related to conversion rate benchmarks is among the most protected data on the web.
There’s a reason for this secrecy. Not many website owners wish to share their performance metrics with competitors.
That said, there are tools to decipher the number of visitors a website receives and give an idea about its conversion rate.
The question now is: what is a reasonable conversion rate?
The answer: it varies. What’s great for one industry might be subpar for another.
One multi-billion-dollar company I once worked with had conversion rates of 41% for first-time visitors. And they still wanted more.
The key is understanding the average ecommerce conversion rate and benchmarking your performance against it. Once you know whether you’re meeting the average for your industry, you can work to improve conversion rates until you’re in the top 10 percentile of performers.
You need to improve if your website converts at around the average mark.
This article has been updated for 2024 to include new data for the average conversion rate across ecommerce industries, countries, etc.
First, you should understand that the conversion rate is highly contextual.
A store selling high-end electronics isn’t going to have the same conversion rate as one selling $10 t-shirts. Similarly, a store with a loyal email list of 100,000 hungry buyers will see far better conversions than one buying cold traffic off Facebook.
Some of the variables that impact conversion rate include:
Furthermore, “conversion rate” generally signifies the percentage of visitors who turn into customers. You might have different goals you are trying to optimize for (say, the percentage of visitors who add a product to a cart, download a lookbook, fill out a contact form, etc.).
The term “average conversion rate,” thus, can be a bit misleading.
If you’re having a hard time boosting your ecommerce conversion rate, this video is for you:
7 Ways To Boost eCommerce Conversion Rate (Part 1)
Conversion rates are a vital metric for understanding the effectiveness of your online presence.
A “conversion” isn’t just about making a sale—it involves any essential action a visitor takes on your website, turning them from a casual browser into a lead or customer.
Before we get into more details, let’s look at the overall conversion rate benchmarks:
An ecommerce conversion rate refers to the number of people who placed an order online at your store compared to the amount of traffic.
You can discover your ecommerce conversion rate using this formula:
Ecommerce conversion rate = orders/number of visits X 100%
Consider this scenario:
Your store gets 1500 visits and 75 places an order. Using the formula above, this will be;
75/1500 = 5%.
This metric has different names depending on your analytics.
Here’s an example of ecommerce conversion rate in Google Analytics:
The ecommerce conversion rate measures how well your online store is converting.
Success for an ecommerce business isn’t in the monthly traffic it gets but in the number of orders placed from that traffic. This is what defines conversion rate.
If you’re driving a ton of traffic with little to no conversions, it’s not a traffic issue on your site but a conversion problem that requires a deep dive into your site to find possible areas of improvement.
How do you know you need to investigate your site’s conversion rate?
These are just some reasons to consider your site’s conversion rate.
That said, you can’t implement changes suddenly. The first step to boosting your conversion rate is analyzing your website and customer behavior.
How will you analyze your website and customer behavior to improve conversion rates? Here are some ways:
All these actions aim to look for common patterns and complaints. Then, you prioritize them based on which impacts the bottom line more, create a hypothesis for the more significant issue, and then proceed to A/B tests.
This is how you tackle conversion rate issues. You don’t just make changes to your site—you need to hear from your site visitors about their experience and how you can improve.
Let’s see how industry, channels, and additional influential factors categorize the latest commerce conversion rate benchmarks.
Where does our data on conversion rate benchmarks come from?
This article uses data from over 300 websites tracked by Invesp, publicly available statistics from sources like Statista, and various analytics tools.
As ecommerce continues to grow globally, understanding industry-specific conversion rates becomes essential for businesses aiming to optimize their online sales strategies.
Here’s a look at how different sectors perform, reflecting variations in consumer behavior and purchase patterns across industries.
Conversion rate benchmarks in the fourth quarter of 2023 by industry (Source: Statista)
Here’s a quick overview of conversion rate benchmarks across various channels, including social media, paid search, email marketing, and direct traffic. It will give you an idea of what to expect and how different channels serve unique roles in the consumer journey.
Social Media Channels:
Paid Search (Google Ads):
Email Marketing:
Direct Traffic:
Overall, each channel contributes uniquely to the sales funnel. Understanding these contributions will help you allocate resources accordingly and develop strategies that resonate with your target audiences.
Let’s delve into the ecommerce conversion rates by different devices (Source: Statista).
Ecommerce conversion rates as of December 2023, by device (Source)
Desktop Devices:
Tablet Devices:
Mobile Devices:
Despite growing mobile usage, conversion rates on mobile devices are slightly lower than on desktop, stressing the importance of mobile-optimized shopping experiences.
Also, remember that these averages can vary based on industry, user behavior, and other factors. Regular monitoring and optimization are essential to enhance conversion rates across all devices.
If your ecommerce product is on the tech scene, paying attention to these ecommerce conversion rates by platform is crucial.
If you’re planning to expand your business globally, these average ecommerce conversion rates by region will come in handy:
The following section covers how to improve your website’s conversion rate.
You can take action to improve user experience on your website, which will eventually help you increase your ecommerce conversion rates.
Here’s a list of some of the best ecommerce conversion rate optimization strategies you can implement immediately. The list won’t be exhaustive; it is just a pointer.
Have you tried zooming into images to get clearer details yet couldn’t?
Did you end up making the purchase? Most likely, no.
The same thing happens on your website. When users cannot zoom in to see smaller details and your image resolution is poor, they will not buy.
The solution is to use images with higher resolution, 360-degree function, and zooming ability.
‘This product was good.’
‘I enjoy using this product.’
‘I couldn’t sleep well at night for years until I came across the Aquapedia mattress; now I need to set the alarm to wake up; if not, I’m not sure I’ll ever wake up. This experience is so good, I don’t want it to end.’
Which of these reviews do you prefer?
Which of these reviews will motivate potential customers to buy from you?
If your guess is as good as mine, the third review.
Use more social proof with stellar reviews on your money pages to increase conversions.
Many checkout stages have unnecessary steps that make the shopping experience unpleasant.
How do you compel your audience to make the purchase? Here are four quick steps to improving your checkout page.
The above charts show that social media converts fairly poorly compared to search and email; both underperform against direct traffic.
If improving conversion rates is your priority, focusing on better converting channels will yield better results. Dig into your analytics report to see where most of your traffic comes from. If your top channel is social and you have very little direct traffic, it might be a good idea to divert marketing resources to PPC or invest in an email campaign.
Similarly, email yields better conversion rates than social and search. Consider investing in an email marketing campaign to increase your store’s overall conversions.
Different product pages and product categories will have different conversion rates. Dig through your analytics to see what pages convert best. These should be the top priority in your marketing campaigns.
For example, if your t-shirts convert better than your shoes, promote the former on your site and your marketing.
At the same time, also consider what products contribute the most to your bottom line. A $1,000 product that converts to 2% is better for your store than a $10 product that converts to 10%.
Finding a product with a reasonably high order value and strong conversion rates can do wonders for your store.
When split testing, it is easy to fall into the trap of making small changes (such as changing a button color) and expecting significant returns.
Such an approach will rarely, if ever, yield unicorn-level conversion rates of 5-10% or higher. To get to that level, you have to look beyond cosmetic changes.
Try the following with your tests:
Optimize your site for mobile users even if you don’t invest in a mobile app. According to Criteo, mobile-optimized sites convert more than 100% higher than non-optimized sites.
However, smartphone traffic conversion rates are lower than desktops and tablets.
Improving the mobile shopping experience can significantly boost your bottom line. One way to do this is to invest in a mobile app.
While the upfront costs will be high, a mobile app offers several advantages over a mobile website:
This is one reason some retailers are switching mobile websites altogether and going ‘app only.’
Understanding and applying the right conversion rate benchmarks can be a game-changer for your e-commerce sites.
Whether you’re just starting or looking to refine your strategies, knowing where you stand with these benchmarks allows you to set realistic goals. Once you know where you stand, you can strive to reach the top of your business performance.
Remember, every percentage point increase in your conversion rate can boost your revenue. Therefore, constantly testing and optimizing your site based on these benchmarks isn’t just a good practice—it’s essential for staying competitive and growing your business.
Ready to explore conversion rate optimization more deeply? Visit the Invesp blog for more CRO insights, the latest trends, and actionable strategies to boost conversions and your ecommerce success.
1. How To Calculate Your Website Or Campaign Conversion Rate
2. How to Create a Robust Conversion Optimization Plan?
3. Google Analytics Metrics That Impact Conversion Rate Optimization
4. The Conversion Framework: 7 Principles to Increase Conversion Rates
5. The Science Behind Successful Ecommerce Conversion Rate Optimization
A/B testing is essential for Conversion Rate Optimization (CRO). Popular sites like Google and Amazon use it to optimize their website elements. While many companies use A/B testing, analyzing the results can be complex.
One small mistake can lead to incorrect conclusions and lost conversions.
This article will guide you through analyzing A/B test results and statistically significant results, regardless of the tool you use.
A/B testing, or split testing, involves comparing two versions of a web page or email to determine which version generates more conversions.
Let’s say you’re running a lemonade stand and figuring out how to get people to buy your refreshing drinks. You could try two different signs: one that says “Ice-Cold Lemonade” and another that says “Freshly Squeezed Lemonade.”
This is the essence of A/B testing: you create two versions of something (in this case, your sign) and see which performs better.
In the digital world, this “something” could be anything from a website’s headline to the color of a button. You show one version (version A) to half of your visitors and another version (version B) to the other half. Then, you track which version leads to more clicks, purchases, or whatever goal you’re aiming for.
Let’s examine a real-world example to see how A/B testing works in action and what makes it so powerful.
Our CRO team noticed a dip in conversions on one of our client’s product detail pages (PDPs) and suspected that the price placement might be causing friction for potential customers.
We decided to run an A/B test with different price placements to get to the bottom of this.
In version A (Control, we placed the price at the top of the page above the product image.
When visitors reached the “add to cart” CTA at the bottom of the PDP, they had to go up to see the price. It caused friction and made them abandon the page.
In variation 1 “B,” We placed the price and the reviews above the “add to bag” CTA.
In variation C, we placed the price above the “add to bag” CTA and the reviews below.
In variation “D,” We placed the price below the product image.
In variation 4 “E,” We placed the price next to the quantity field.
The results were quite eye-opening:
What did we learn from this?
By testing and making data-driven decisions, we achieved a 5.07% uplift in conversions, a considerable improvement that can translate into significant revenue growth for our business.
To know more about A/B testing and how to conduct A/B tests, you should read our in-depth guide to A/B testing.
Congratulations, your test won!
So, what’s next? Should you permanently remove the old design and ask your developers to implement the winning variation?
No, not yet!
Before you do that, you must ensure your results are correct. This means you must investigate and know the factors contributing to the win. Remember, A/B testing is not about running tests and hoping for wins. It’s also about learning.
You’ve run your A/B test, and now you get to see what happened. There are a few possible scenarios:
What it means: One of your variations has outperformed the original (control) version. This is typically determined by reaching a pre-set level of statistical significance (more on that later).
For instance, you tested the control against three variations (V1, V2, and V3), and V2 won.
The next thing you should do is re-run the test; this time, you should only test the control vs. the winning variation (V2, in this case).
If the initial results are correct, V2 will win again, and you can draw some learnings that you can propagate across the site.
You should also consider allocating 100% of the traffic to the winning variation. This means pausing the experiment, duplicating it, and resetting the traffic allocation.
What it means: Sometimes, depending on how reasonable your developed hypothesis was, more than one variation can outperform the control, but it needs to be clarified which one is the absolute best.
As good as it might sound, it can be confusing—in the sense that you might not know which variation to go with.
Look closely at the data to see if there are subtle differences between the winning variations. Is one slightly better in terms of conversions or engagement?
Looking at the above screenshot, it’s easy to choose variation four in such cases because it is the highest-winning variation. But is ignoring other winning variations (1 and 3) a good idea?
To gain insights into how your most valuable customers respond to the changes, segment your test results by:
This can reveal how different user groups respond to changes.
For instance, let’s say yours is a lead generation website, and you’re trying to test multiple variations of trust signals.
All V1, V2, and V3 are winners and showed an uplift in conversions.
In such cases, you can combine all the winning ideas into a single design you will implement on the site.
What it means: None of your variations performed better than the original version.
Don’t despair—even “losing” tests provide valuable insights.
An A/B test can fail when the variation(s) running against the control fails to beat the control design in terms of the primary and other goals set in the test. A good example is when the control/original version gets more conversion uplifts than the variation(s).
This can happen even if you follow all the A/B testing best practices and correctly run the test.
When your test loses, you should:
Here is what I mean by this:
Often, the solution you present in an A/B test is the most likely element to need to be corrected.
This is because solutions can be subjective, with multiple variables like location, copy, look, and UX. Most tests focus on evaluating solutions, as the underlying problem and research are usually thorough.
First and foremost, most tests run at Invesp are evaluated from a solutions standpoint. The reason for this is typically the problem uncovered, and the research conducted was thorough. The hypothesis is highly based and driven by data. The solution is the part that can be more prone to human assumptions.
Remember: a single hypothesis can have multiple solutions. Even logically sound solutions during design discussions may not resonate with site visitors. If a test fails, reconsidering discarded solutions can be beneficial.
For instance, let’s say a hypothesis has four possible solutions:
Because they want to learn which web element had the most impact on increasing conversions, optimizers sometimes test only all the possible solutions in a single test. In this case, the first test may aim at solutions 1 and 2. If the test has no positive results, once-discarded solutions 3 and 4 are tested.
In A/B testing, a hypothesis predicts how a change will affect conversion rates.
If your test results don’t turn out as expected, your hypothesis might be incorrect. This could happen if your prediction based on the data were wrong or if the data supported multiple predictions.
For example, if visitors aren’t clicking your CTA button, increasing its size might not help if the issue is the button’s placement or uncompelling copy.
Test failures can also occur if your variations aren’t based on a hypothesis. Testing random ideas with proper research is a good use of resources. Instead, conduct thorough research, formulate a solid hypothesis, and design your test accordingly.
In CRO, we use both qualitative and quantitative data. Validating both types is crucial before launching an A/B test. This involves confirming qualitative findings with quantitative data or vice versa.
For example, if Google Analytics shows a high bounce rate on a page, watching session replays can reveal the cause.
We can approach data revalidation in two ways:
When an A/B test fails, revalidating your research data is essential. If you initially used one approach, try the other for a different perspective. Ideally, utilize both approaches to gain a comprehensive understanding of the problem on your site.
Finally, it’s time to analyze your A/B test data.
When interpreting the results of your A/B test, there is a validity checklist you should tick to avoid false positives or statistical errors. These factors include:
It makes no sense to conclude any A/B test results without verifying their validity.
So, here’s a detailed insight into each factor you should consider when analyzing A/B testing results.
Whether you are running the A/B test on a low or high-traffic site, your sample size should be big enough to ensure that the experiment reaches a significant level. The bigger the sample size, the lesser the margin of error.
To calculate the sample size for your test, you will need to specify the significance level, statistical power, and the relevant difference between the rates you would like to discover. If the formula is too complicated, there are easy-to-use online sample size calculators.
If you do not calculate your test’s sample size, you risk stopping it too early before it collects enough data. Khalid wrote an article about this and had this to say about sample size:
“Any experiment that involves later statistical inference requires a sample size calculation done BEFORE such an experiment starts. A/B testing is no exception.”
Also, consider the following when evaluating the sample size:
The statistical significance level (or confidence, significance of the results, or chance of beating the original) shows how significant your result is statistically.
As a digital marketer, you’d want to be sure about the results so that the statistical significance indicates that the differences observed between a variation and control aren’t due to chance.
The industry standard of statistical significance should be 95% (or 90% in some cases). This is the target number you should consider when running an A/B test.
95% statistical significance means you are 95% confident that the results are accurate. It means that if you repeat the test repeatedly, the results will match the initial test in 95% of cases.
When can you end an A/B test? It depends on several factors but shouldn’t end prematurely or drag on too long.
Our CRO manager, Hatice Kaya, suggests running a test for at least an entire business cycle or seven days.
This can vary depending on the product or service, as some sell more on paydays and less throughout the month.
Every website has a business cycle—the time it typically takes customers to purchase. Some sites have lower conversions on weekends and peaks on weekdays. Run your test throughout the cycle to account for fluctuations and get valid data.
Seven days is a minimum. The actual duration depends on your site traffic. Lower traffic requires more extended tests.
Use an online A/B testing calculator to determine the optimal duration. For example, with 5000 daily visitors and three variations, a test should run for 18 days.
It’s a common belief that website conversions depend on traffic volume. High-traffic sites usually get more conversions, and vice versa.
However, when you run a test on high-traffic sites, you do not have to worry about the number of conversions; you should just focus on reaching the required sample size for that traffic.
But when it comes to low-traffic sites, to get more accurate results, you should keep in mind two factors:
Your test should reach the required sample size and have at least 2-300 conversions per variation (this is the pure minimum). It is even better if it reaches more than 300 conversions per variation.
So, now we have checked our test results and made sure that they are valid and don’t contain any statistical errors. Let’s move on to a deeper analysis.
Several external and internal factors impact every website you see. These factors include:
These factors increase data variance, leading to less accurate results. If your test runs during a holiday, consider relaunching it later to verify the results.
While analyzing macro conversions (sales, leads, subscriptions) is essential, examining micro-conversions provides more profound insights.
Micro-conversions vary by business and website type, but examples for e-commerce sites include:
Here is an example of micro-conversion goals you may need to analyze for an ecommerce site.
Although micro-conversions don’t directly increase your conversion rate, they help move prospects down the funnel, leading to more purchases. Understanding micro-conversions can also explain why a test is performed a certain way.
Not all A/B tests will be winners, and conversion specialists must accept this reality. However, losing tests can be valuable learning opportunities.
Anwar Aly, a conversion specialist at Invesp, advises,
“If the loss rate is normal, businesses should learn from lost tests, recognizing that loss is part of A/B testing and can sometimes be more valuable than wins. If the loss rate is high or constant, re-evaluate your testing approach, possibly starting with a new audit and utilizing qualitative data to validate test hypotheses.”
In this section, I walk you through a checklist to evaluate losing tests and what you can do differently.
A poorly thought-out hypothesis will result in poor AB tests and results. A characteristic of a poor hypothesis is the lack of insights driving the hypothesis.
This means that the company testing or the CRO agency often guesses what to test; it’s not a product of conversion research.
To create a better insight-driven hypothesis, you should use this format:
We noticed in [type of conversion research] that [problem name] on [page or element]. Improving this by [improvement detail] will likely result in [positive impact on key metrics].
So you can see what I mean; a real example of this would be:
We noticed in [the session recording videos] that [there was a high drop off] on [the product page]. Improving this by [increasing the prominence of the free shipping and returns] will likely result in [a decrease in exits and an increase in sales].
You’ll be surprised at how similar many variations are to the control.
What happened? Maybe a sentence was changed, or the color of the call to action button, but nothing major.
In this instance, getting a winning test is almost impossible because the variations don’t look different.
Check out this video to see the different categories of A/B tests we do to give you a different perspective:
It’s normal to go through heatmaps and session recordings to see how site visitors and users engage with a page pre-test.
Post-test? Not so common.
This is a viable missing link in understanding why a test failed.
When you conduct post-test heatmap analysis and session recording of pages tested, you can see whether users engaged with or noticed the element you were testing.
Visitors click maps show heat maps of what visitors click on and how far they scroll down your pages. Even more important are visitor session recordings, which allow you to watch visitors’ exact mouse movements and journey through your website.
Many businesses focus on variation design in A/B testing, but the execution is equally important. Launching an experiment isn’t the end of the process; mistakes can invalidate your results.
Running an A/B test is not always about finding a variation that generates more conversions; sometimes, it’s about learning the changes in user behavior.
You should constantly be testing to understand your visitors, their behaviors, and the web elements that influence their behavior change.
Regarding conversion rate optimization or CRO, even Google Trends tells an illuminating story.
Over the past five years, the search term “Conversion Rate Optimization” has experienced a significant uptick in interest, indicating a growing recognition of its importance among businesses across various sectors.
Not including CRO in your budget isn’t just a mistake; it’s a big one. There’s a good reason why all the top companies are doing some form of CRO or experimentation.
In this article, we’ll talk about the world’s best conversion rate optimization companies. To make it easy for you to choose one, I’ve divided the agencies into three categories:
Dedicated CRO Agencies: These experts focus solely on making your website perform better and bringing in more paying customers.
Smaller Agencies: They might be smaller, but they offer customized solutions and give you more attention.
Full-Service Agencies: If you want an all-in-one package for your digital plan, these agencies provide a wide range of services, including CRO.
As we go through these categories, you’ll see why CRO is essential and how it can help your business grow.
Since 2006, InvespCRO has optimized conversions for primary, medium, and small brands, including 3M, CampusBooks, Impact, Home Gallery Sores, eBay, O’Reilly, etc.
With a presence in Chicago and Istanbul, Invesp is the world’s number-one conversion optimization agency. Since our inception in 2006, we’ve led the charge in North America as the second CRO company to specialize exclusively in conversion optimization.
Our track record speaks for itself: Over the past 17 years, we’ve empowered 900+ companies across various industries, including retail, e-commerce, and automotive, to not only boost their conversion rates but also increase their revenue.
We’re not just about numbers; we’re about results. To date, we’ve conducted a staggering 32,000+ A/B tests, delivering game-changing insights to our clients.
Our founders, Khalid Saleh and Ayat Shukairy, aren’t just CRO experts in the field – they’re published authors. Their book, ‘Conversion Optimization: The Art and Science of Converting Prospects to Customers,’ is a must-read for anyone venturing into the world of conversion rate optimization.
In 2023, Clutch named us the world’s top conversion optimization agency. We’ve been recognized as the leading CRO company in Chicago, maintaining our position as a global leader since 2021.
Since 2007, we’ve developed the conversion framework (an in-house framework) that removes the guesswork from the conversion optimization process. It provides anyone interested in optimization with a specific methodology to produce consistent results.
1. Conversion rate optimization
2. User and conversion research.
3. Conversion rate audit and CRO strategy
4. Landing page optimization
5. UX design
6. CRO training.
7. Analytics powered by e-cens.
Helping globally recognized companies like Amazon, Apple, Google, and Facebook maximize conversions since 2006.
Conversion Rate Experts has worked with businesses in over 40 countries and 11 languages. This gives them a broad perspective many CRO agencies don’t have.
Their CRE methodology (broken into nine steps) has helped them generate 100’s of millions in revenue for clients.
They also have a proprietary wins database (based on 17 years of launching experiments) that helps them know what works in each situation. For each win, they record the objections faced, the techniques used, and the outcomes—all tagged by variables such as type of website, market vertical, business size, geography, conversion goal, and resulting improvement.
1. Conversion rate optimization (CRO), landing page optimization, and customer-journey mapping—with A/B testing and multivariate testing
2. Email marketing
3. Analytics, including tracking, traffic analysis, and conjoint analysis
4. Marketing research
5. User experience (UX), including usability testing
6. Website design and information architecture
With offices in the US, CRO Metrics has driven data-backed conversion wins for clients like Allbirds, Bombas, Calendly, Clorox, and Doordash since starting in 2010.
Since their inception in 2010, they’ve launched over 30,000+ experiments on hundreds of websites.
They also own an internal SaaS, which they use to launch experiments for their clients (Iris by CRO Metrics).
At CRO Metrics, their clientele is made up of ecommerce (30%), business services (25%), and consumer products and services (15%).
1. Analytics
2. Conversion Rate Optimization
3. Paid Media
4. Lifecycle Marketing
5. Experiment-Led Redesigns
Conversion is a global CRO agency formed in 2022 by integrating two separate agencies: Conversion.com in the UK and Widerfunnel in North America. Both original agencies were formed in 2007 by Stephen Pavlovich (Conversion.com) and Chris Goward (Widerfunnel).
Conversion has worked with leading brands like Microsoft, Canon, SAMSUNG, Toyota, etc. to optimize conversions.
With 100+ CRO specialists across the UK and North America, Conversion uses a unique blend of A/B testing, UX research, and personalization to improve our clients’ websites and businesses.
By experimenting with new messaging, design, functionality, and even pricing and products, they’ve helped their clients generate over $2 billion in additional revenue.
Their approach combines several frameworks to help clients make more decisions with confidence. Examples of frameworks they deploy for clients include the PIE framework, Levers framework, Easier scoring model, etc.
1. Conversion Rate Optimization
2. Enterprise program consulting
3. Product and pricing experimentation
4. Effective personalization.
5. Conversion centered design.
6. User experience research.
7. Liftmap
Founded in 2011 as CXL, Speero has always prioritized optimizing customer experiences.
Trusted by clients like – Miro, GrubHub, Dermalogica, MONSTER, etc.
In 2020, the company changed its name from ‘CXL’ to Speero to support its renewed focus on helping companies understand their customers better through data, research, and experimentation to drive long-term growth, not just short-term wins.
With offices across the UK, Europe, the US, and Asia Pacific, Speero works with medium to large enterprises globally.
Speero is popular on LinkedIn for its blueprints/frameworks, which they share. Examples include – models, blueprints, methods blueprints, etc.
1. Experimentation/CRO
2. Research&Strategy
3. Data and analytics.
Since 2013, theGood has combined indepth user research, analytics, and robust user testing to boost conversions for global brands like New Balance, Xerox, Easton, Swiss Gear, etc.
They have a mix of unique frameworks and methodologies like comprehensive ecommerce conversion audits and tailored conversion growth programs for clients.
1. Conversion growth program
2. Comprehensive conversion audit.
3. Data-driven redesign.
4. Conversion research and consulting.
5. Small business optimization
The metric we used to qualify these agencies as smaller is based on the number of their employees, which can be verified on LinkedIn.
Founded in 2014, Conversion Advocates has helped 100’s businesses in widely varying industries increase conversion rates, gain insights into their customers, and grow their revenue month-over-month using powerful data-driven experiments.
They use their IIEA framework to increase conversions, reduce costs, and help clients better understand their customers.
Ranked #3 Out of 2,562 CRO Agencies Worldwide in 2019.
Trusted by brands like Zillow, SurveyMonkey, MVMT, etc.
ConversionAdvocates promises a 90-day growth methodology that’s guaranteed to deliver results.
Here’s the average result from over 200 + companies who participated in their 90-day program – 8.7X Return On Investment, 26.6% increase in conversion rates, 37.7% validated win rate, 13.8 experiments launched, and nine research methods used.
1. Research and Analysis.
2. Cross-channel optimization.
Since 2014, Conversion Fanatics has driven improved conversions for companies like HarperCollins publishers, Ministry of Supply, Dr Axe, etc.
Kaizen principles drive them and believe in relentless continuous improvement (a good motto if your focus is delivering conversion rate optimization services).
They have helped optimize 250+ companies and have launched 15,000+ split tests.
1. CRO/experimentation
2. Brand design.
3. Traffic management.
4. Reporting and Analysis.
Established in 2010 by Rishi Rawat, Frictionless Commerce specializes in improving the conversion rates of product pages only.
They don’t bother about any other page on your website, just the product page, and to make your product page into the strongest sales pitch it can be.
At frictionless commerce, they work with eCommerce businesses whose best-sellers are doing $400,000 in sales or $1.8 million in annual sales.
1. Product page optimization
Hype Digital is a global CRO agency that has been helping companies increase their website and app conversion rates since 2017.
Founded in Cape Town, South Africa, Hype was the brainchild of Cameron Calder, former performance marketer for the CR7 brand, and was initially a Paid Advertising agency.
After feeling frustrated that the customers being sent to his clients’ websites were not converting as a result of poorly optimized sites, he and his team created a CRO model that relies purely on data to make decisions. Our heavily research-based model believes in removing all guesswork when making decisions.
Today, Hype has a presence in Cape Town, Tel Aviv, and Amsterdam and works with customers of all sectors worldwide. This allows us to understand what works and doesn’t work for each industry in each region.
Clutch currently lists Hype Digital as the top CRO agency in South Africa and is a proud partner with many website optimization, personalization, and testing tools.
Starts at $3500 (contact the Sales team for more information on different options)
Launched in 2014, SplitBase focuses strictly on high-growth eCommerce businesses and has helped several of them with impressive conversion rates, as seen on their site.
Cerebral, Pela, Dr. Squatch, and Vanity Planet are examples of businesses they work with.
1. Landing page design/optimization
2. Conversion-focused website design
3. Full-site optimization program
Known as the performance marketing agency that doubles revenue, KlientBoost was Founded in 2015.
As a full-service agency, their digital marketing services cut across conversion rate optimization, email marketing strategy, PPC, etc.
They boast 617 case studies plus 250+ active clients.
Some of the businesses they’ve helped include – AirBnb, UpWork, Stanford University, Hotjar, etc.
1. Paid Advertising
2. Conversion Rate Optimization
3. Search Engine Optimization
4. Email Marketing
In Digital Marketing, Neil Patel Digital is a household name. This agency was started by Neil Patel in 2015 and offers SEO, content marketing, paid ads, and CRO.
As the CEO, Neil Patel also offers consulting and has helped 5000 companies, including CNN, Intuit, Adobe, etc.
1. Earned media (SEO, PR, Email marketing, etc)
2. Paid media (paid search, paid social, streaming, etc.)
3. Data & Analytics (Conversion rate optimization, front-end development, user experience)
Since 2010, Inflow has provided web design, SEO, paid ads, CRO, email marketing, and GTM Audits for businesses.
They’ve helped ecommerce businesses like Atranil, KEH Camera, and Vitrazza improve their conversion rates and ROAS.
Their specialty includes but isn’t limited to – fashion & apparel, B2B ecommerce, health & wellness, Pet, etc.
1. SEO.
2. Paid ads
3. CRO.
4. Email marketing.
5. GTM Audits
Contact the sales team.
Linear Design provides paid ads and CRO services to its clients.
Trusted by brands like FlexPod, Order Mark, and Nextbite, Linear Design is focused on profitability, cost per acquisition, and delivering expected business outcomes for clients.
1. SEO.
2. Paid ads
3. CRO.
4. Email marketing.
5. GTM Audits
Founded in April 2021, Prismfly focuses on e-commerce businesses and promises 100% “Done-For-You” conversion rate optimization.
They’ve conducted 1000+ A/B tests with $275 million generated for clients, which include Revival, Miku, Xtrema, Luxy Hair, etc.
Prismfly averages a 20% conversion increase in six months.
1. Shopify Plus Web Development
2. Conversion Rate Optimization
3. Lifecycle Marketing
4. UX / UI Design
Established in 2016, Journey Further provides paid search, influencer marketing, technical SEO, UX, and CRO services to many clients.
Trusted by brands like Oddbox, Sky, Trainline, Casio, etc.
They’ve seen results like a 103% increase in revenue from paid search and a 597% increase in organic revenue from targeted landing pages.
1. Content
2. Paid social media marketing
3. CRO.
4. Digital PR
5. Design
Eric purchased Single Grain for $2.00 in 2014 when it was an SEO agency gasping for its last breath.
Today, SingleGrain works with powerhouses in virtually every sector to drive leads, boost sales and engagements, and help businesses genuinely connect with their clients.
Using state-of-the-art technology fueled by the unmatched passion of people who love what they do, they’re a digital marketing agency to reckon with.
They’ve helped companies like Uber, Amazon, and Siteminder to grow.
1. Content marketing strategies.
2. SaaS.
3. CRO.
4. Paid advertising.
5. Education.
Optimizing your website for conversions is a non-negotiable in today’s business world.
By partnering with top conversion optimization agencies, you can maximize your online success and drive growth for your business. Depending on your business needs, you can choose from the many companies we discussed in this article.
Don’t hesitate to invest in this crucial aspect of your online strategy to stay competitive and thrive in the digital world.
Conversion Rate Optimization (CRO) is improving a website or landing page to increase the percentage of visitors who take desired actions, such as purchasing, filling out a form, or subscribing to a newsletter.
Related Article: Guide To Conversion Rate Optimization: Everything You Need To Know
CRO agencies employ data analysis, user feedback, and various testing methods (such as A/B testing) to identify barriers to conversion. They then optimize website elements, improving user experience and increasing the likelihood of conversions.
CRO is vital for businesses because it maximizes the value of existing website traffic, increases revenue, lowers customer acquisition costs, and provides valuable insights into customer behavior.
Conversion rates can be influenced by website design, content quality, site speed, navigation ease, trust signals (like testimonials and security badges), call-to-action placement, and mobile responsiveness.
The timeline for CRO results varies based on the complexity of the website and the changes made. Generally, significant improvements might be observed in a few weeks, but ongoing refinements are common for long-term success.
Businesses of all sizes and industries, including e-commerce, B2B, non-profits, and service-based companies, can benefit from CRO services. Any business with an online presence can optimize for conversions.
Look for agencies with a proven track record, positive client testimonials, expertise in your industry, a data-driven approach, and a focus on user experience. Transparent communication and collaboration are also crucial.
Success can be measured through key metrics like conversion rate, bounce rate, average session duration, click-through rate, and revenue. A/B testing and other tools provide valuable insights into which strategies work best.
Yes, CRO techniques evolve with changing user behaviors, technology, and industry trends. Staying updated with the latest methods ensures continued effectiveness in optimizing conversion rates.
Yes, many agencies offer conversion optimization services tailored to small businesses and startups. The investment in CRO often pays off through increased conversions and revenue, making it a cost-effective strategy.
A/B testing is a valuable technique that compares two web page versions to determine which performs better. While not always necessary, it provides empirical data to inform decision-making and is widely used in CRO efforts.
While basic CRO strategies can be implemented independently, agencies bring expertise, experience, and specialized tools. For comprehensive, data-driven optimizations, hiring a professional CRO agency often yields superior results.
CRO metrics are vital gauges for your website’s conversion efficiency.
Understanding them is essential for pinpointing where and how to enhance your site for better performance and conversions.
Dive into this article to explore these key metrics and learn how to leverage them for an operative website and a healthier bottom line.
The CRO metrics, also known as the conversion metrics, help to evaluate a website’s efficiency in converting visitors into customers or achieving their desired goals.
By understanding and analyzing these CROs effectively, it is possible to maximize user experience and strengthen revenue growth for digital marketers aiming at improved sales figures and software companies wanting increased trial sign-ups.
Such data-driven observations of consumer actions and strong assessment capabilities are integral to web performance enhancement, which eventually contributes largely towards maximized conversions.
Conversion metrics are an essential and measurable factor to measure a website’s ability to generate customers or carry out other desired actions. For instance, the number of completed sales on an e-commerce site would be one primary metric for assessing optimization outcomes. By keeping track of such analytics, organizations can gain insights into their web performance to devise more efficient changes.
Additional tracking metrics like click rate, email open rates, and user experience tracking help in knowing even deeper particulars regarding the overall efficiency, with keywords being shared through social media platforms being included, too.
CRO metrics are invaluable for digital marketers, enabling them to make data-driven decisions while refining their marketing strategy. Close monitoring of these allows businesses to recognize and resolve user dissatisfaction, resulting in a decrease in bounce rate and an improved online experience.
Regular assessment of CROs helps optimize the allocated marketing budget, thereby delivering maximum returns on investment.
Understanding the essential metrics of conversion rate optimization is necessary to gain insight into how well your website performs. These basic CRO metrics are:
Conversion rate, average time on page, bounce rate, and pages per session. Tracking these measurements helps you identify areas that require improvement while analyzing performance levels.
Comprehending advanced CRO analytics will enable informed decision-making as it delves into advanced CRO analytics. Then, just a general overview of your web activity – allowing for greater evaluation of each metric’s contribution towards achieving business objectives over different periods within an online journey.
The conversion rate is the most important of all CRO metrics, as it looks at how many website visitors complete a particular desired action, like buying something or submitting information. Monitoring this number is vital to measure how effective your site and marketing efforts are. Understanding what works – and doesn’t work – can help you determine ways to make improvements for better user satisfaction.
Enhancing conversions involves optimizing Calls To Action (CTAs), improving UX design, and gathering customer feedback; these elements will give an overall boost to achieving that optimal conversion rate!
The bounce rate by traffic source is a significant metric that helps evaluate the success of distinct channels in producing engaged customers. High rates might indicate poor page loading times, lackluster user experience, or content that doesn’t apply to visitors.
Analytic tools and software can assist with studying these percentages so potential improvements to boost customer satisfaction and lessen bounces may be implemented quickly.
The average duration people spend on a web page provides an insight into how engaged and interested they are in the content. This metric is a great way to measure how efficient your website’s user experience, content, and load speed genuinely are.
If users remain on the page longer than usual, it may suggest that what you’re providing is exciting or captivating enough to keep them around. This time spent can positively correlate with conversions like buying something or signing up for newsletters. Analyzing your Average Time On the Page can help immensely when assessing just how good of an impression you make upon visitors! Looking at site loading speeds will inform these conclusions because slow-loading pages usually lead patrons away from staying too long on any particular page.
It can be aggravating when customers fill out part of a form but don’t complete it. This phenomenon, the abandonment rate on forms, is measured as a percentage to understand how many users initiate filling in information but later give up without submitting their data. A lot of times, what leads people to quit are long and complex questions that demand too much information or have no clear objective for why they should invest time in completing them.
Keeping tabs on this ratio helps identify problematic areas within our sales process or checkout flow, allowing us to make crucial improvements to ensure an enjoyable user experience, which could result in more conversions!
The metric of pages per session is the average number of pages viewed in one visit to a website. If this figure is high, it implies that users interact with your content more and spend longer onsite.
Methods for enhancing pages per session should focus on giving useful information, improving readability, including interactive visuals, and optimizing image sizes for fast loading speed.
Customer behavior CRO metrics enable businesses to understand better their website’s performance and user base, including analyzing Visitor RFM Analysis and Segmented Conversion Rates. With these tools, companies can pinpoint important customers for retention campaigns and segment users based on attributes or actions taken.
Tracking Average Revenue Per Visitor (ARPV) is crucial in conversion optimization because it provides a comprehensive measure of each visitor’s monetary value to a website. Unlike focusing solely on conversion rates, ARPV considers the revenue generated, offering a more nuanced understanding of a site’s performance.
This metric enables businesses to identify high-value traffic sources, optimize for revenue impact, and tailor strategies based on user behavior and segmentation. By emphasizing revenue rather than just conversions, businesses can make informed decisions that align with their overarching financial goals, ensuring that optimization efforts directly contribute to the bottom line.
Digital marketing strategies can be improved by analyzing customer data from a Visitor Recency, Frequency, and Monetary (RFM) perspective. This allows companies to assess customer behaviors such as their recent purchase history, how often they make purchases, and what is spent in each given timeframe. By using these insights, marketers can tune up campaigns for higher conversion rates with a better focus on results by optimizing the available digital media resources.
Analyzing this kind of data means targeting customers more effectively while bringing greater returns on investments made into online advertising platforms or social networks connected initiatives where real success can happen much faster than ever before considering modern industry standards when it comes down to ROI performance management across various communication channels currently existing out there today.
Monitoring the Customer Lifetime Value (CLV) metric helps businesses devise strategies to get maximum value out of customer relationships, target specific customers, and allocate resources judiciously for acquisition or retention purposes. This can be done by predicting future net profits over an entire duration with each client.
The Customer Acquisition Cost (CAC) is a key performance indicator that quantifies the average amount a company spends to gain one new customer. Tracking this figure allows businesses to gauge how efficiently they invest in marketing activities and adjust their tactics accordingly.
Segmented conversion rates are a sophisticated CRO metric that looks at segmenting people based on traffic sources or demographic information to measure the success rate of each group. With this understanding, companies can improve their conversion rates and become more effective in marketing activities. By using different criteria such as website hits or age/gender profiles when measuring conversions, businesses stand to gain invaluable insights into how well customers respond to various strategies they use for promotion.
The funnel conversion rate is a metric employed to observe the percentage of customers who proceed from one step in the sales process to another. This evaluation helps businesses spot any issues that may be preventing conversions and allows them to enhance their customer experience, leading ultimately to an increase in conversions.
With a solid grasp of fundamental and advanced CRO measurements, we can now look at the most effective approaches to utilizing them. This includes setting precise objectives, segmenting data according to criteria such as industry averages or customer personas, comparing performance with standards within your sector for reference points, and then running split tests (A/B testing) against these benchmarks.
The journey begins with setting concrete objectives for businesses looking to utilize CRO metrics effectively. By pinpointing high-value KPIs and clearly outlining their associated conversion data, these organizations can be confident that they are observing accurate information that will direct their optimization techniques and decisions.
Businesses can customize their content to a user’s preferences by segmenting data based on visitor attributes and activities to create an improved experience. This process could include elements such as source, device type, location, persona profiling, and analyzing different behaviors. By doing so, they can raise the rate of conversions by better engaging these various sections of their target audience.
Gaining insight into your performance and discovering areas to improve can be done by benchmarking CRO metrics against industry standards. Through comparison, you will better understand how well your numbers measure up compared with those of competitors in the same sector.
Companies can use A/B testing to determine the optimal website design and content to improve their conversion rate. Through this technique, they compare different versions of web pages to recognize which specific elements or changes will be most effective in meeting a particular goal, such as increased customer engagement.
Having a good understanding of CRO metrics and how to use them, let’s look at the tools available for monitoring these indicators. Web analytics tools and A/B testing applications offer important data points that can be used to evaluate website success and user behavior patterns. Giving us invaluable knowledge.
Web analytics tools are vital to monitoring and accumulating information about website visitors, granting a valuable understanding of user behavior. For tracking CRO metrics, some of the best web analysis resources include Google Analytics (GA4) and Baremetrics, which provide various functions for measuring and examining data related to conversion optimization.
Web analytics tools are not the only way to identify which website elements and strategies make for successful conversions. A/B testing is also an essential element of this process, with popular tools like VWO, FigPii, Optimizely, and AB Tasty offering valuable features such as advanced targeting capabilities and integration options. When selecting a tool from these or any other providers, it’s vital to consider how easy they are to use so that they can provide maximum benefits.
Look at some successful implementation examples of CRO metrics from a practical perspective. These cases reveal how businesses chose and tracked their conversion goals, started tracking accurately, and implemented the right tools to help them monitor these objectives. All this provides helpful guidance and can be inspiring when beginning such endeavors.
Monitoring CRO metrics is a vital part of any effective digital marketing strategy. This data helps businesses analyze their users’ behavior, website performance, and areas to improve upon. By equipping themselves with the proper techniques and resources, they can optimize their websites accordingly, leading to higher conversion rates while bringing positive results for business growth.
Using quantitative data and analytics, CRO (conversion rate optimization) focuses on metrics such as page views, bounce rate, and traffic sources to track user interaction and find ways to improve conversion rates.
CRO agencies are typically priced between $2000 and a maximum of $15,000 monthly. If you decide to manage the process in-house, it could cost around 30% of your overall marketing budget. When looking at CRO tools, they range between ten dollars and five hundred per month.
To work out the conversion rate, divide the number of conversions by the total number of visitors. Then multiply that result by 100 to get your percentage for CRO.
CRO metrics offer data-driven insights into how users behave when using a website and the effectiveness of its conversion rate optimization efforts. These are essential in order to boost conversions by improving performance, as they provide an analysis of the ability of websites to convert visitors into customers or achieve desired actions.
Knowing the conversion rate to gauge your website and marketing efforts is vital, and determine which elements require improvement to boost the user experience.
A/B testing vs. multivariate testing? This question plagues every CRO professional every once in a while.
When optimizing your digital assets, knowing whether to use A/B or multivariate testing is critical.
Are you looking to quickly determine the superior version of a webpage for low-traffic sites?A/B testing is your go-to.
Or do you aim to dissect complex interactions between various elements on a high-traffic page? Then, A/B and multivariate testing will provide your in-depth analysis.
This guide breaks down each method and offers strategic insights into deploying them for maximum conversion optimization.
TL; DR? Here are some quick takeaways:
A/B Testing: also known as split testing, compares two versions of a digital element to determine which performs better with the target audience.
It effectively optimizes various marketing efforts, including emails, newsletters, ads, and website elements. A/B testing is particularly useful when you need quick feedback on two distinct designs or for websites with lower traffic.
Key aspects of A/B testing:
Multivariate testing takes it up a notch by evaluating multiple page elements simultaneously to uncover the most effective combination that maximizes conversion rates.
By using multivariate testing, you can gain valuable insights into how different elements or variables impact user experience and optimize your website or product accordingly.
Key aspects of multivariate testing:
Unlike A/B testing, which compares two variations, MVT changes more than one variable to test all resulting combinations simultaneously. It provides a comprehensive view of visitor behavior and preference patterns, making it ideal for testing different combinations of elements or variables.
Deciding between multivariate and A/B testing depends on the complexity of the tested elements and the ease of implementation.
A/B testing is more straightforward and suitable for quick comparisons, while multivariate testing offers more comprehensive insights but requires more traffic and careful consideration of potential biases.
Choosing between A/B and multivariate testing depends on traffic, complexity, and goals.
A/B testing is ideal for limited traffic due to its simplicity and clear outcomes. Multivariate testing offers detailed insights but requires more effort and time.
However, before you set up either of the testing types, you’ll have to form a hypothesis. In the case of multivariate testing, you’ll also need to identify a number of variables you intend to test.
Prior to commencing your A/B or multivariate testing, it’s imperative to construct a hypothesis. This conjecture about the potential influence of alterations on user behavior is crucial for executing substantive tests.
An articulate hypothesis will include:
A compelling hypothesis also embraces the SMART criteria: Specificity, Measurability, Actionability, Relevance, and Testability.
It integrates quantitative data and qualitative insights to guarantee that the supposition is grounded in reality, predicated upon hard facts, and pertinent to the variables being examined.
A/B testing vs. Multivariate testing hypothesis example:
For example, if you’re running an A/B test, your hypothesis could be:
Changing the CTA button of the existing landing page from blue to orange will increase the click-through rate by 10% within one month, based on previous test results and user feedback favoring brighter colors.
If you’re running a multivariate test, your hypothesis could be:
Testing different combinations of headline, hero image, and CTA button style on the homepage will result in a winning combination that increases the conversion rate by 15% within two weeks, supported by prior test results and user preferences.
Selecting the correct multiple variables to assess in a multivariate experiment is crucial. Each variable should have solid backing based on business objectives and expected influence on outcomes. When testing involving multiple variables, it’s essential to rigorously evaluate their possible effect and likelihood of affecting targeted results.
Variation ideas for inclusion in multivariate testing ought to stem from an analysis grounded in data, which bolsters their potential ability to positively affect conversion rates. Adopting this strategy ensures that the selected variables are significant and poised to yield insightful findings.
To implement an A/B testing protocol, one must:
For a more detailed overview of how to run and set up A/B tests, check out our ultimate guide to A/B testing.
To set up multivariate tests:
After this, all the steps remain the same as in the A/B test implementation, including randomly assigning audience to different combinations, determining sample size, and then finally running the test.
Pro Tip: Implement trigger settings to specify when variations appear to users, and use fractional factorial testing to manage traffic distribution among variations. During the multivariate test, systematically evaluate the impact of variations and consider eliminating low-performing ones after reaching the minimum sample size.
Finally, it’s time to analyze your results.
For a thorough assessment of user interactions post-A/B and multivariate testing sessions:
They serve as indispensable tools by allowing you to observe real-time engagement metrics and dissect and comprehend findings after reaching statistical significance in an A/B test.
Interpreting multivariate test data calls for a distinct methodology. In multivariate testing, it is essential to evaluate the collective impact of various landing page elements on user behavior and conversion rates rather than examining aspects in isolation.
This testing method provides comprehensive insights into how different elements interact, allowing teams to discover effects between variables that could lead to further optimization.
When assessing multivariate test data, it’s necessary to:
This process helps optimize your website’s performance and improve your conversion rate through conversion rate optimization.
Both testing methods offer valuable insights, but they also share some pitfalls to avoid.
Here are some common mistakes to avoid when setting up your A/B or multivariate tests:
A/B and multivariate testing are potent methods that can transform how you approach digital marketing. By comparing different variations, whether it’s two in A/B testing or multiple in multivariate testing, you can gain valuable insights into what resonates with your audience.
The key is to embrace a culture of experimentation, value data over opinions, and constantly learn from your tests. This approach can optimize your strategy, boost your results, and ultimately drive your business forward.
Multivariate testing distinguishes itself from A/B testing by evaluating various elements at the same time in order to determine which combination yields the most favorable results, as opposed to A/B testing which only contrasts two variations.
Recognizing this distinction will assist you in determining the appropriate method for your particular experimentation requirements.
When swift outcomes are needed from evaluating two distinct designs, or when your website experiences low traffic volumes, A/B testing is the method to employ.
On the other hand, if your intention is to examine several variations at once, multivariate testing could be a better fit for such purposes.
When setting up an A/B test, it’s crucial to consider the sample size for reliable results and precision, control the testing environment, and use tools for qualitative insights like session recordings. These factors will ensure the accuracy and effectiveness of your test.
To thoroughly assess data from multivariate tests, consider how different combinations of page elements together influence user behavior and ultimately conversion rates. Determine which specific sets of page elements result in the most significant increase in conversions, while also noting which individual components contribute the least to overall site conversions.
Ensure that you allow sufficient traffic to accumulate in order to reach statistical significance. It’s important to factor in external variables such as seasonal variations or shifts in the marketplace, and also be mindful of technical elements like how testing instruments might affect website performance. Overlooking these considerations may result in deceptive test outcomes and false interpretations, which could squander both time and investment.
A Google search for “How to improve website conversion” returns 370 million results, most of which are generic advice.
While best practices and trends can be helpful, they only sometimes address individual website needs. A/B testing, a subset of conversion rate optimization (CRO), offers a solution by testing design changes directly with users.
This article explores the best A/B testing tools and top features to consider when looking for an A/B testing tool for your business.
As a subset of conversion rate optimization, A/B testing is no longer a new field.
It’s been around for a while, and you can access hundreds of tools to run A/B tests.
To properly define an A/B testing tool, I need to mention what an A/B test is.
An A/B test is when you combine two web pages with a difference in one element to see which performs better.
The A/B testing tool allows you to create different web page variations to see which performs better.
These tools automate splitting traffic, tracking user behavior, and analyzing results to identify statistically significant differences between variations.
Key functionalities of A/B testing tools include:
To get a more in-depth understanding of A/B testing, read our ultimate guide to A/B testing.
If you don’t want to skim to read the essential features of an A/B testing tool, you can also watch this quick video:
No matter how unique your A/B testing tool is, it won’t be worth it unless it’s easy-to-use, has an intuitive interface, and requires minimal technical expertise to set up and run tests.
At the same time, look for a visual editor that allows you to make changes without coding.
Your A/B testing tool’s visual editor should allow you to:
That said, the A/b testing tool should also come with a code-based editor that allows your developer to create a variation using JavaScript and HTML/CSS that will be responsive across devices.
A visual editor allows non-technical users to create variations easily, while a code editor enables developers to manipulate site code for responsive variations across devices. Complex tests made with a visual editor can lead to responsiveness issues.
The tool should support a variety of experiment types beyond simple A/B tests, such as:
Your tool should segment your audience and target specific groups based on multiple factors, including their behavior. This will help you tailor your content to particular groups.
Pro tip: Look for a tool that allows you to target based on demographics, behavior, or other custom criteria.
Everyone has a bias. The same can be said of CRO specialists and their statistical approach.
CRO specialists approach statistics in two fundamental ways: the Bayesian and Frequentist models. That said, every tool can record and compute its statistics. For some, it’s Bayesian; for others, it’s Frequentist.
When selecting an A/B test tool, consider your favorite statistical approach. If you’re a Frequentist, you don’t want to pay thousands of dollars for a tool whose analysis is Bayesian.
No A/B testing tool is perfect and foolproof—you’ll need assistance sooner or later, but it’ll happen.
So, no matter how exceptional the tool is, you should have direct access to support staff when encountering an issue. Their site should also have a knowledge base and documentation, making it easy to resolve significant matters yourself.
To stay competitive, businesses must constantly adapt to evolving customer needs. However, releasing untested features can negatively impact interaction and revenue.
A/B testing with feature flagging can help avoid this issue by allowing controlled feature rollouts and testing to ensure customer satisfaction. If your business regularly releases new features, consider an A/B testing tool with feature flagging capabilities.
The tool should provide comprehensive reports and visualizations that clearly illustrate the impact of your tests.
Look for features like heatmaps, click maps, and funnel analysis to better understand how users interact with your variations.
Many businesses today use multiple tools in their tech stack. Before picking your A/B testing tool, ensure it integrates with a range of relevant business tools you already use or intend to use.
Now that we know which features to consider when choosing an A/B testing tool let’s examine the tools that offer all these features and more.
Best For: Businesses looking for an all-in-one conversion optimization platform.
FigPii is an all-in-one tool for conversion optimization and behavior analysis that enables A/B testing, session recordings, heat maps, and even on-site polls and surveys.
Plus, it has a visual editor that lets you make changes to your website without knowing any code.
FigPii also stands out due to its flicker-free A/B testing. This means that when visitors come to your site, they won’t see any annoying flickering or delays as the different variations load. This is a big deal because it can improve the user experience and make your tests more accurate.
Key Features:
Pros:
Cons:
Pricing: The free plan initially allows you to run unlimited A/B tests with up to 15,000 monthly visitors. Paid plans start at 149.99 monthly for up to 30k visitors and unlimited A/B tests.
Best For: Mid to large-sized businesses looking for an advanced A/B testing tool.
Trusted by leading businesses such as Klaviyo, Disney, and L’Oreal, AB Tasty offers an omni-channel experimentation platform for desktop, mobile, and IoT devices.
The platform enables client and server-side testing, personalization (including AI-based segmentation and audience building), and audience activation, leveraging AI and machine learning capabilities.
Like FigPii, it also comes with behavior analysis tools such as heat maps, surveys, and session reports.
Key Features:
Pros:
Cons:
Pricing: Quote-based.
Best For: Large enterprises with complex, multi-channel testing and personalization needs.
Adobe Target, part of the Adobe Experience Cloud, is your one-stop shop for running A/B tests and creating personalized customer experiences.
It offers a comprehensive suite of tools, including A/B and multivariate testing (MVT), multi-armed bandit testing, server-side optimization, mobile optimization, on-device decisions, and connected device optimization.
Key Features:
Pros:
Cons:
Pricing: Quote-based.
Best For: Enterprise-level businesses looking for an all-in-one CRO tool.
Trusted by brands like Hyundai, Wikijob, and Microfocus, VWO is an AB testing and conversion rate optimization tool for enterprise brands.
It also has a visual editor that helps you create and edit variations without any coding knowledge.
Beyond simple A/B tests, you can also use it to implement other optimization strategies like personalization and user behavior analysis. For example, you can access tools like heat maps, session recordings, built-in forms, form analytics, etc.
Key Features:
Pros:
Cons:
Pricing: It starts at $154 monthly for up to 10,000 users. There’s also a freemium plan.
Ultimately, the best A/B testing tool for you will depend on your specific needs, budget, and level of expertise.
For example, if you’re looking for an easy-to-use CRO and A/B testing tool with a visual builder, you might prefer FigPii. Adobe is a preferred option for enterprise companies.
Have you ever wondered why some A/B tests skyrocket conversions while others fall flat? The secret isn’t just having the right tools—it’s following proven best practices.
Done incorrectly, AB testing can waste money, misuse staff hours, and, even worse, decrease conversion rates.
But when done right, A/B testing lets you easily experiment with different web page designs, split traffic to see how each performs, and gain valuable insights into what truly resonates with your audience.
Here are 11 best practices you must follow to ensure your A/B tests deliver tangible results.
Wrong expectations lead to disappointment and lost investment. Many marketers conduct A/B tests because they read or watched a case study where a company increased conversion rates.
While success stories are inspiring, they shouldn’t be your sole benchmark. Every website is different, and your results will depend on various factors.
Here are two approaches you can take to set the right expectations:
Since 2006, our team has worked with hundreds of global organizations in many industries.
Over the years, we’ve observed a common pitfall in many CRO programs: a lack of dedicated technical resources.
This often leads to slow implementation and missed opportunities.
Before you run an A/B test, consider the following:
Why does it matter? Without the right technical skills or resources, A/B testing can quickly become a bottleneck.
Some tests need complex code changes or integrations with other tools, so it’s crucial to understand these needs from the start. This helps set realistic timelines, allocate resources wisely, and ensure your testing program runs smoothly.
Your first task in effective A/B testing is identifying potential website conversion problems.
Here’s a structured approach to building your “research opportunities” list:
Step #1 Gather enough data and insights:
Step #2. Build your research opportunities list:
Based on your research, you will end up with an extensive list of items you can test on your platform. We refer to this list as the “research opportunities.
For each item, include:
We will use these three points to prioritize items on the research opportunities list. Each item on the list includes additional data, such as the page it was identified on and the device type.
Step #3. Prioritize your list:
Not all tests are created equal. Prioritize your list based on factors like:
While your research opportunities list gives you a broad overview of potential areas for improvement, you’ll need to analyze data more deeply to prioritize your A/B tests effectively.
This involves analyzing both quantitative and qualitative data.
Quantitative analysis: unveiling traffic patterns and bottlenecks:
Before optimizing any page, understand how much traffic it receives. For example, if a page gets only 20% of visitors, optimizing it solely addresses 20% of potential improvements.
Utilize your web analytics to track user journeys and identify where users drop off.
For an e-commerce website, set up the following funnels/goals:
For a lead generation website, set up the following funnels/goals:
Each of these goals aims to start dissecting user behavior on the website. However, this quantitative research gives you half the picture—you need to conduct a qualitative analysis for more reliable data.
Qualitative analysis: Gaining user insights
Quantitative data helped you uncover what users are doing. Now, you must conduct a qualitative usability analysis to understand the why.
For qualitative analysis, gather feedback directly from users through:
Ask about their experiences, pain points, motivations, and what factors influenced their decisions to convert or abandon the website.
To determine which item to tackle first, prioritize your research opportunities list.
We use 18 factors to prioritize the research opportunities list (click here to download our prioritization sheet).
Our evaluation criterion includes:
Prioritizing items on the “research opportunities” list creates a six to eight-month conversion roadmap for the project.
This doesn’t mean optimization stops after this period—instead, you’ll repeat this process regularly to ensure ongoing improvement.
Here is a partial screen capture for a conversion roadmap on one of our CRO projects:
Don’t just guess—test with a hypothesis!
A hypothesis is a predictive statement about a possible change on the page and its impact on your conversion rate.
Each item on your prioritized research opportunities list should include an initial hypothesis—a starting point for addressing a potential problem. As you delve deeper into each issue, you’ll refine this initial hypothesis into a concrete, actionable one that you can use to design your test.
Example: Evolution of a Hypothesis
Notice how the concrete hypothesis is more specific and actionable. It:
A coherent, concrete hypothesis should drive every test you create. Avoid the temptation to change elements unrelated to your hypothesis, as this can muddy your results and make it difficult to draw meaningful conclusions.
To ensure your A/B test results are reliable, determine the minimum number of visitors required to achieve statistical significance.
This involves two key steps:
1. Determining unique visitors for the test pages:
Don’t assume all website visitors will encounter your test. Use your analytics to determine the total number of unique visitors going through the particular page(s) you plan to test in a month.
2. Determining how many visitors you must include in your test:
Before launching any split test, determine how many visitors must complete the test before you can draw statistically valid conclusions. This is called “fixed horizon testing,” and it ensures you don’t end your test prematurely or drag it on unnecessarily.
To calculate this, you’ll need the following information:
Many online calculators and statistical tools can help you determine the required sample size based on these inputs.
Why does this matter? Testing with an insufficient sample size can lead to inaccurate conclusions and wasted resources. By calculating the A/B test sample size upfront, you can ensure your test results are statistically significant and provide actionable insights for your optimization efforts.
Once you have a clearly defined hypothesis, the next step is to design a new page to validate it. However, be careful to avoid getting sidetracked by other design elements; focus solely on the changes related to your hypothesis.
Follow this simple two-step design process:
White your A/B testing software may allow you to create millions of variations for a single page; don’t go overboard with creating variations.
Validating each new variation requires a certain number of conversions. This approach of throwing things at the wall rarely works. Throwing multiple options at the wall and hoping something sticks is seldom a successful strategy. Instead, focus on creating a limited number of well-thought-out variations that address specific hypotheses.
Remember, the goal is to uncover sustainable and repeatable results. The more variations you introduce in a test, the harder it becomes to isolate the impact of each one.
For most websites, we recommend limiting the number of variations to less than seven. This simplifies analysis and reduces the risk of statistical errors.
You are not done when determining a winning design in a split test. A best practice is to run your original page against the winning design in a head-to-head (one-on-one) test.
Why Re-test?
A/B testing is an iterative process. Continuously validating your results will help you make data-driven decisions and optimize your website for maximum performance.
The real power of conversion optimization happens when you discover marketing insights from your testing to apply across verticals and channels.
Always look for actionable marketing insights from your test. These are excellent ways to proceed with your next test.
Follow these 11 best practices to transform your A/B testing process from a guessing game into a tool for conversion rate optimization. Remember, successful A/B testing is an ongoing journey of experimentation, learning, and optimization.
If you’re looking for expert guidance and support to unlock the full potential of A/B testing, Invesp’s team of seasoned CRO specialists is here to help.
A successful A/B test requires careful planning and execution.
It’s essential to have a team capable of designing effective test scenarios, analyzing results rigorously, and iterating based on learnings. Poorly designed experiments waste time and resources.
In this article, we’ll outline key steps to create successful A/B tests that drive meaningful results.
Before diving into testing, ensure you have clear criteria for deciding:
Remember, A/B testing is just one part of the larger conversion optimization process. It should ideally come after foundational work like:
This ensures your tests target the right areas and are informed by a deep understanding of your audience.
For a more in-depth guide on our CRO process, you might want to check out a more detailed guide on how we conduct conversion optimization projects.
Before considering elements on the page to test, start by analyzing different problem areas on your website. This helps prioritize your efforts and focus on changes that will have the most significant impact.
There are several conversion optimization methodologies available to guide your analysis. At Invesp, we utilize the Conversion Framework, which systematically examines seven key areas of a webpage:
The Conversion Framework analyzes seven different areas on the page:
These seven areas will influence whether visitors stay on your website or leave. Different elements have diverse impacts based on the type of page you are evaluating.
Using the Conversion Framework, a conversion optimization expert can quickly pinpoint 50 to 150 problems on a webpage.
However, attempting to fix all of them at once would be overwhelming and inefficient. Instead, prioritize and focus on the top three to seven problems to start. This targeted approach allows you to make meaningful improvements and gather data for further optimization.
A hypothesis is a predictive statement about the impact of removing or fixing one of the problems identified on a webpage.
For instance, our client selling nursing uniforms experienced high cart abandonment rates. Usability testing revealed visitors were price-conscious and feared overpaying.
Original Design: The initial shopping cart lacked clear assurances about price matching and money-back guarantees. This fueled visitor concerns.
Hypothesis: Adding prominent assurances to the cart page would reduce price concerns and decrease abandonment by 20%.
New Design: We introduced an “assurance center” on the left-hand navigation, highlighting the price match and money-back guarantees.
Results: This change led to a 30% reduction in cart abandonment, validating our hypothesis.
However, hypotheses aren’t universal.
A successful hypothesis for one website doesn’t guarantee success on another. A different client, also aiming to reduce cart abandonment, implemented a similar assurance center.
The image below shows the original design of the cart page:
The following image shows the new design of the cart page with the assurance center added to the left navigation:
Surprisingly, this change decreased conversions by 4%. Several factors could explain this: the design, copy, placement, or even a fundamental difference in their target audience.
Validating hypotheses through testing and refining them based on results is core to conversion optimization. In this case, further testing of the assurance center’s elements would be needed to determine its true impact.
Tests that increase conversions are great, but even those that decrease them offer valuable insights into visitor behavior and the accuracy of our hypotheses. The most concerning results are those that show no significant change, as they indicate a need for deeper analysis or a different approach.
Remember, each website and audience is unique. Continuous testing and refinement are crucial for uncovering what truly drives conversions for your specific context.
Once you have the hypothesis, the next step is to create new page designs to validate it.
Be careful when you are creating new designs. Do not go overboard with creating new variations. Most split-testing software allows you to create thousands, if not millions, of variations for a single page. You must remember that validating each new variation requires a certain number of conversions.
We limit page variations to less than seven for high-converting websites. We limit page variations for smaller sites to two or three new variations.
How do you judge the quality of the new designs you introduced to test your hypothesis? You let your visitors be the judge through AB or multivariate testing.
Remember the following procedures when conducting your tests:
A/B testing isn’t a one-time fix, but an ongoing process of learning and refinement. By systematically identifying problems, testing hypotheses, and creating data-driven variations, you can unlock valuable insights and continuously improve your website’s performance.
Remember, each website and audience is unique. There’s no one-size-fits-all solution. Through rigorous testing and data-driven decisions, you can tailor your website to your specific audience and achieve your conversion goals.
Need expert guidance to optimize your website for maximum conversions? Invesp’s team of conversion optimization specialists is here to help.
If your website visitors are not converting, then obviously, there is something stopping them.
You can go ahead and ask your design team to create new designs, but the question remains: how do you know that the new designs will convert more visitors compared to the original design?
In this article, we’ll cover what is AB testing, why you should consider AB testing, categories of AB tests, what is statistical significance, how to launch an AB test, and many more.
Ready to learn? Let’s get started.
A/B testing, also known as split testing, is a method of comparing two versions of a webpage or app against each other to determine which one performs better. It involves testing different elements, such as headlines, images, or buttons, with similar audiences to identify which variant leads to higher conversions, engagement, or other desired outcomes.
ALSO READ: The Difference Between A/B Testing and Multivariate Testing
The original design of a page is usually referred to as the control. The new designs of the page are usually referred to as the “variations,” “challengers,” or “recipes.”
The process of testing which page design generates more conversions is typically referred to as a “test” or an “experiment.”
A “conversion” will vary based on your website and the page you are testing. For an e-commerce website, a conversion could be a visitor placing an order. But, for a SaaS website, a conversion could be a visitor subscribing to the service. For a lead generation website, a conversion could be a visitor filling out a contact form.
From the definition of A/B testing, you already know that A/B testing works by comparing two different versions of something, like a webpage or an app screen, to see which one people like or respond to better.
Here is an A/B testing example that will help you understand:
Imagine you have a homepage on an e-commerce website that receives 100,000 visitors a month. To determine if there is a way to increase conversions, the design team creates one new design for the homepage.
AB testing software is then used to randomly split the homepage visitors between the control and the new challenger. So, 50,000 visitors are directed to the control, and 50,000 visitors are directed to the challenger.
Since we are testing which design generates more orders (conversions), we use the AB testing software to track the number of conversions each design generates. The A/B testing software will then determine the winning design based on the number of conversions.
A major challenge eCommerce businesses face is the issue of a high cart abandonment rate.
This is bad for a business because it usually signals that the customer is not happy with something.
This is where the A/B test shines because it allows you to make the most out of your existing traffic without spending extra cash on acquiring new traffic.
Here are more reasons why you should conduct A/B tests:
There is nothing more painful than working on your site design, making it public, and realizing site visitors are not engaging with your content.
Many eCommerce sites are facing this issue. As a business, if you want to prevent this from happening, before rolling out new designs, create an A/B test and let your variation be the new site design against your present design (control).
Split your traffic using a tool like Figpii across the control and design and let your users decide.
This is one of the advantages of running A/B tests. It prevents you from launching untested new designs that could fail and dampen your revenue.
One of the major plagues eCommerce stores faces is cart abandonment.
This means site visitors and customers add an item(s) to the cart and don’t complete the checkout process.
How A/B tests help you here is simple. There are important elements on the product checkout page, like the check-out page text, where shipping fees are located, etc.
By creating a variation and playing with the combination of these elements and changing their location, you can see which of the pages (control or variation) helps to decrease the cart abandonment rate.
Without A/B testing your redesign ideas, there’s no guarantee that it’s going to improve cart abandonment rates.
If you’re seeing a decent conversion rate with A/B testing, you can increase your conversion percentage.
You can A/B test page layout, copy, design, location of the CTA button, etc.
Without A/B tests, if you should make a design change or copy change, there’s no guarantee that there will be improvements.
The learnings you get from an A/B test on one of your product pages can be implemented or modified on the product pages of more expensive products.
This goes a long way in improving your customer AOV and your revenue bottom line.
AB tests come in different types, and they can be done in different environments.
An A/A test is a control test where two identical versions of a webpage are compared. It’s done to ensure the testing process is working correctly. For example, in an A/A test, both version A and version A’ of a webpage are shown to users simultaneously. If the results show significant differences between these identical versions, it suggests a problem in the testing setup.
A/B testing involves comparing two versions of a webpage (A and B) to determine which one performs better. For instance, in an e-commerce A/B test, version A might have a green “Buy Now” button, while version B has a red one. By analyzing user interactions, businesses can identify the color that leads to higher click-through rates and conversions.
A/B/n testing expands on A/B testing by comparing multiple versions of a webpage (A, B, C, etc.). For example, an online news platform might test different headlines (A, B, C) simultaneously to see which one attracts more clicks, providing insights into user preferences among multiple options.
Multivariate testing involves testing multiple variations of different elements within a webpage. For instance, a travel website could test various combinations of images, headlines, and call-to-action buttons on its homepage to find the optimal mix that increases user engagement and bookings.
Targeting tests involve showing different versions of a webpage to specific audience segments. For example, an online clothing store might show different homepage versions to new visitors, returning customers, and newsletter subscribers, tailoring the user experience based on their preferences and behaviors.
Bandit tests, also known as Multi-Armed Bandit tests, dynamically allocate traffic to the best-performing versions during the testing period. For instance, an online gaming app might use a bandit test to optimize the display of in-game ads, ensuring that the most effective ad is shown more frequently to maximize revenue.
Split page path tests involve testing different user journeys or paths within a website. For example, an e-learning platform could test two different pathways for users to access course materials: one through a step-by-step guide and another through a video tutorial. By comparing user engagement and completion rates, the platform can optimize the learning experience based on the preferred path.
It’s important to mention that each and every type of experiment listed can provide reliable data that can be used to attain valuable insights.
Below is a simple and straightforward process you can begin using to perform an A/B test.
Collecting quantitative and qualitative data is key in knowing what to A/B test.
With the user behavior insights you gather from going through your site analytics and the results from qualitative research, you’ll easily find out the major causes of user frustration on your site.
One qualitative research method I recommend is analyzing heat maps and session recordings. This way, you can easily see visitor behavior data – where they click, scroll depth, etc., all give you ideas for future tests.
With tools like Google Analytics 4, you’re able to track the pages that have the highest bounce rates and least user activities. These are pages you can improve.
Now you’ve gone through your analytics, you’ve seen the pages that can be improved, your qualitative results are back, and you’ve seen and heard from your customers about their experiences.
It’s now time to create a test hypothesis. An effective A/B testing hypothesis consists of three key components:
Here is an example of a solid AB testing hypothesis:
Certain customers abandon their shopping carts due to a lengthy checkout process (challenge). Simplifying the checkout form by reducing the number of required fields (specific solution) is expected to increase the conversion rate by 20% (assumed impact).
Using an A/B testing tool like FigPii, you can easily create the variation of the page you want to test. You can also make changes to the element you want to focus on. This might be changing the color of a button, changing out the copy hiding the navigation, etc.
It’s time to run the experiment. Your A/B testing software will randomly allocate your site visitors based on the percentage you provided. Their interaction with the control or variation is recorded and computed, which determines how either is performed. It’s important to mention that you should never stop your test until it hits the required sample size and achieves statistically significant results.
When your experiment is over, it’s time to look at the results and see how the control and variation performed.
This is a crucial stage because a lot can be learned from a winning and losing test.
Your A/B testing tool will show you how each performed and if there’s a statistical difference between the two.
If you ask any conversion rate experts, they will probably recommend that you don’t stop before it reaches statistical significance.
You can think of statistical significance as the level of certainty that your test results are not affected by a sample size error or any other factor. As a rule of thumb, an A/B test should have statistically significant results of 90% (and above) for the change to impact the performance of a website.
The amount of traffic coming into a landing page you’re testing will determine how long it takes to reach statistical significance. The higher the traffic, the faster it would take – and vice-versa.
In this case study, we tested if the price placement on the PDP was the reason behind the decline in conversions.
We decided to go ahead and test placing the price in different areas in the PDP and see how it would impact conversions.
In the control “A”:
The price was placed at the top of the page above the product image.
When visitors reached the “add to cart” CTA at the bottom of the PDP, they had to go all the way up to see the price. It caused friction and made them abandon the page.
In variation 1 “B”:
We placed the price and the reviews above the “add to bag” CTA.
In variation C:
We placed the price above the “add to bag” CTA, with the reviews below the CTA.
In variation 3 “D”:
We placed the price below the product image.
In variation 4 “E”:
We placed the price next to the quantity field.
Results:
Variation 1 “B” uplifted conversions by 3.39%.
Then Variation 2 “C” outperformed the original and the other variations by a 5.07% uplift in conversion rate.
Variation 3 “D” uplifted conversions by 1.27%.
And Variation 4 “E” uplifted conversions by 0.95%.
While the price is simple and obvious, you should not overlook how a product’s price is displayed on your PDPs. Important elements such as price deserve consideration in an e-commerce design.
Don’t assume that the current placement of your elements is the best for your users.
You still need to test it and see what resonates best with them.
We did it and got a 5.07% uplift.
At its core, AB testing software tracks the number of visitors coming to each design in an experiment and the number of conversions each design generates. Sophisticated A/B testing software tracks much more data for each variation. As an example, FigPii tracks:
The split testing software uses different statistical modes to determine a winner in a test. The two popular methods for determining a winner are Frequentist and Bayesian models.
The split testing software tracks conversion rates for each design. However, declaring a winner in a split test requires more than generating a small increase in conversion rates compared to the control.
This model uses two main factors to determine the winning design:
Think of confidence level as the probability of having a result. So, if a challenger produces a 20% increase in conversions with a 95% confidence, then you assume that you have an excellent probability of getting the same result when selecting that challenger as your default design. It also indicates that you have a 5% chance that your test results were due to random chance and a 5% possibility that you found a wrong winner.
This approach uses two main factors to determine the winning design:
Leonid Pekelis, Optimizely’s first in-house statistician, explains this by saying.
Bayesian statistics take a more bottom-up approach to data analysis. This means that past knowledge of similar experiments is encoded into a statistical device known as a prior, and this prior is combined with current experiment data to make a conclusion on the test at hand.
We typically rely on multiple metrics when determining a winning design for a test. Most of our e-commerce clients use a combination of conversion rates and revenue per visit to determine a final winner in an experiment.
Selecting which metrics will depend on your specific situation. However, it is crucial to choose metrics that have an impact on your bottom line. Optimizing for lower bounce or exit rates will have little direct and measurable dollar value to most businesses.
The team at Bing was trying to find a way to increase the revenue that the site generates from ads. To do so, they introduced a new design that emphasized how search ads are displayed. The team tested the new design vs. the old design. The split test results showed a 30% increase in revenue per visit.
This, however, was due to a bug in their main search results algorithm in the new design. This bug showed visitors poor search results. And as a result, visitors were frustrated and were clicking on ads.
While the new design generated a higher revenue per visit, this was not a good long-term strategy. The team decided to stick to the old design instead.
Most AB testing software automatically divides visitors equally between different variations.
There are, however, instances where you need to assign different weights to different variations.
For example, let’s take an experiment that has an original design and two challengers in it. The testing team might want to assign 50% of the visitors to the original design and split the remaining 50% between variations one and two.
Some Conversion optimization experts debate this question at great lengths.
Looking at your analytics, you can typically notice that different visitor segments interact differently with your website. Returning visitors (those who visited the site previously) generally are more engaged with the website compared to new visitors.
When launching a new AB test, you will notice that in many instances:
The fact that new visitors convert at higher rates with new designs compared to returning visitors is attributed to the theory of momentum behavior.
If your website gets a large number of visitors, we recommend that you launch new tests for only new visitors and observe how they react to it. After that, you can start the test for returning visitors and compare their reactions to the new designs introduced in the experiment.
Alternatively, you can also launch the test for all users and then segment the results post-test based on new/returning users too, instead of treating them as two different tests. This is the preferred method used by most conversion rate experts.
A/B testing takes time to plan, implement, and get learnings from the result. This means making mistakes is not something your business can afford because it can set you back in revenue and time-wise.
Below are some A/B mistakes you want to avoid as a business.
Seasoned experimenters know not to test anything without having a hypothesis for it. An A/B test hypothesis is a theory about why you’re getting a result on a page and how you can improve it.
To form a hypothesis, you’ll need to pay attention to your site analytics and see the important pages that are getting lots of traffic but have a low conversion rate or the pages that are getting loads of traffic and have a high bounce rate.
Then you go ahead and form your hypothesis about why you think it’s happening and what changes can be made to see a lift in conversions.
Going straight to create an A/B test, skipping the step of insight gathering (qualitative and quantitative), and forming a hypothesis could have a negative impact on your site’s conversion rate.
In CRO, it’s bad practice to copy your competitor’s design because they saw a 46% uplift in their conversion rate.
The reason for this is that implementing a site redesign or page design without knowing about the hypothesis and what was being tested could radically impact your bottom line and user experience.
But there’s a walk around this. If you’re just starting out with A/B testing, or you’ve been doing it for a while, and you see your competitor has seen good conversion from an A/B test, instead of going ahead to implement the same changes they made on their website, you could use their now control page as a variation in A/B test against your current design.
This is a safe way to go about it and get learnings without fully redesigning your site or a page and without destroying your bottom line and user experience.
One absolute way to mess up your A/B test is by changing your testing parameters midway.
This messes up your results.
Parameters you can mess up;
Note: Changing your testing parameters spoils your results. If you must change something, start the test again.
You observe your A/B test running, and your gut tells you that the variation leading is good enough to stop the test.
This is a mistake. The experiment must be allowed to run to achieve statistical significance. This is the only way the results can’t be declared invalid.
As A/B testing becomes more popular, a lot of cheap and low-cost tools are flooding the market. Running your A/B tests with such tools, you run the risk of impacting your site performance negatively.
The fact is, both Google and your site visitors want your website to load fast, but some A/B test software creates an additional step in loading and displaying a page.
This leads to the flicker effect, also known as the Flash of Original Content (FOOC), where for some seconds, the site visitor gets to see the control page before the variation appears.
This leads to a bad user experience, which slows the page load time, which ultimately impacts conversions because site visitors are known not to be patient.
We typically recommend running holdback split tests for larger websites that receive thousands of conversions per month. In these types of tests, you launch the tests to a small percentage of your site visitors. For example, you start with launching the test to 10% of your visitors. If the results are encouraging, then you expand the test to 25%, 50%, and 100% of your website visitors.
There are several advantages to running hold-back A/B tests:
If you choose to run hold-back A/B tests, make sure that you start a new test each time you change the traffic allocation going through the experiment to avoid any statistical problems with the results.
There is a lot of math that goes into determining how many variations should be included in an A/B test. The following are general guidelines you can apply. However, more details will be covered in a later section:
Calculate the monthly number of conversions generated by the particular page you plan to test:
If you have less than 200 conversions a month, your website is not ready for A/B testing. Focus on driving more visitors to your website.
Example: Your website generates 1,000 conversions per month:
Again, this is a simplification of the calculation, but it will give you a good starting point.
Keep reading to find out more and learn more in the following chapters about A/B testing…
AB Testing Best Practices: Optimize your strategies with AB testing best practices – unlock insights, enhance user experience, and boost conversion rates for maximum business success.
AB Testing Process: Unlock the power of AB Testing Process: streamline experimentation, enhance decision-making, and drive optimal outcomes for your business.
AB Testing Tools: Discover top AB Testing Tools: streamline experimentation, optimize user experience, and drive conversions for business success.
AB Testing Vs. Multivariate Testing: Navigate the testing landscape with insights on AB Testing vs. Multivariate Testing – choose the right strategy for optimal results in experimentation.
AB Testing Results Analysis: Master the art of AB Testing Results Analysis: uncover actionable insights, refine strategies, and elevate your experimentation game for business success.
AB Testing Velocity: Accelerate growth with AB Testing Velocity: optimize iteration speed, enhance decision-making, and propel your experimentation strategy to new heights.
AB testing, also known as split testing, compares two versions (A and B) of a webpage or marketing element to analyze user behavior and determine which version performs better in achieving specific goals, such as improving the conversion rate or click-through rate.
Focus on elements like landing page layout, ad copy, and subject lines that directly impact user engagement. Identify specific challenges in your marketing campaign to formulate effective hypotheses for testing.
Statistical significance ensures that the differences observed in test results are not due to chance. It provides reliable data by confirming whether the changes observed in user behavior or website visitors are statistically significant results, not random fluctuations.
Calculating an appropriate sample size is crucial. Use statistical methods to ensure the data collected is robust and representative of your target audience. Tools like Google Analytics can assist in understanding your visitor behavior data and guide your decisions.
Yes, A/B testing can be conducted on multiple pages. Analyze user interactions across various pages to gain quantitative user insights. Ensure to maintain the same user experience consistency to achieve accurate results.
Run the test until you achieve statistical significance. Factors like landing pages, test results, and user behavior impact the duration. Larger changes might show results quickly, while subtle ones require longer durations to gather enough data for analysis.
A/B testing helps optimize your marketing campaign elements, such as ad copy and landing page, leading to improved conversion rates. By identifying the most effective strategies, you can enhance user engagement and ultimately boost your ROI.
