The pursuit of higher conversion rates has become a ubiquitous mantra in the ecommerce world. With businesses striving for growth and success, conversion rate optimization (CRO) has emerged as a popular and powerful strategy.
This article is all about conversion rate optimization: we’ll cover the conversion optimization process, talk about how AI is transforming the world of conversion rate optimization, and share the best CRO tools you can use to increase your conversion rate.
Let’s jump in!
What is a good conversion rate?
Exactly what qualifies as a “good” conversion rate depends on a lot of factors. It can be an individual goal set by you, a benchmark using your industry’s average conversion rates, or something completely arbitrary.
Keep in mind that there are additional factors that come into play when we talk about conversion rates, like the device used for the purchase, geographical location, traffic source, etc.
Shippypro has rounded up results from a variety of ecommerce niches including agricultural supplies, food and drink, baby and child, and many more. Based on their data, we can see that average conversion rates range from 0.62% to 3.84%.
Source: ShippyPro Blog
What are the benefits of conversion rate optimization?
Let’s review some key benefits a good conversion rate optimization strategy can bring to your business:
Increased revenue: By fine-tuning your website to convert more website visitors into customers or leads, you can increase sales without the need for more website traffic or increased advertising spending.
Improved ROI: Selecting the right CRO strategies can significantly enhance your return on investment. When you convert a higher percentage of your existing website visitors, you get more value from your current marketing efforts, making every dollar spent more efficient.
Increased customer lifetime value: By ensuring that visitors become long-term, loyal customers, you not only increase their value to your business but also foster lasting relationships.
Lower cost per lead: As your website becomes more effective at converting site visitors, you pay less for acquiring each lead, freeing up resources for other essential initiatives.
Better user experience: Conversion optimization often involves optimizing the usability and overall user experience of your website. This, in turn, leads to higher customer satisfaction, increased loyalty, and a stronger connection between your brand and your target audience.
These benefits collectively make conversion optimization one of the most vital marketing strategies for an ecommerce website.
Understanding the conversion rate optimization process
There are 5 steps to the conversion rate optimization cycle. Here’s what goes down at each step:
- Research phase: This is where you discover the parts of your conversion funnel that need tweaking.
- Hypothesis phase: This is where you form a working hypothesis based on your metrics and research.
- Prioritization phase: This is where you figure out what to attack first for your optimization.
- Testing phase: This is where you put your hypothesis up against the existing version of your website.
- Learning phase: This is when you deploy the winning variant and gather information for future tests and planning.
Now let’s take a look at each stage in more detail.
Stage 1: Research and data gathering
The very first part of conversion optimization requires you to properly gather data to inform your testing.
Although this stage can be a long process, it’s worth doing it properly to save yourself a lot of time and headaches down the road.
The cold, hard numbers—also known as quantitative data—can give a clear picture of your customer click-through rates, the length of time people spend on your site, and much more.
This type of data can be collected through heatmaps, surveys, KPI analytics, and A/B tests.
The cold hard numbers are great but don’t necessarily paint the clearest picture. That’s where qualitative data comes in. This type of data leaves more room for interpretation and can be analyzed subjectively.
Instead of just accepting what the numbers say, qualitative data asks “why”:
- Why are the traffic numbers so low?
- What problems could users be having on the site?
- Why are the cart abandonment rates so high?
These are questions that qualitative data can help answer.
Some popular ways to collect this data include customer interviews, focus groups, and other observational methods.
When you’ve effectively collected this data, you’ll need to understand what it actually means. Let’s take customer surveys for example. After you’ve collected survey responses, you’ll want to look at patterns, objections, and language used by participants.
Take note of repeated words, emphasized words, “points of resistance,” or places that made browsing or shopping difficult.
Lastly, you’ll want to closely analyze the tone of the language used in these surveys. This can serve as a powerful element for social proof, sales copy, and other personalized messages.
Stage 2: Hypothesis
A hypothesis is a tentative assumption about the effect a change might have.
In conversion optimization, this means a working theory (based on your research and data) of something like “If I change X, it will have Y effect.”
Once you’ve built your list of hypotheses, you need to prioritize that list from most urgent to not-so-pressing. We’ll go into this in the next stage, but first, let’s look at the components of a hypothesis.
- The variable (IF): This is a website element that can be modified, added, or taken away to result in a desired outcome.
- The result (THEN): The predicted outcome (e.g.: more clicks on a call-to-action, more signups on a landing page, etc.).
- Rationale (DUE TO): This is where you show that the result occurred because it was informed by research (quantitative and qualitative).
Source: Optimizely Blog
It looks like a simple enough thing to do—and it can be, when done right… but many ecommerce business owners fail to give enough detail in their hypotheses to really move the needle on their conversion optimization process.
Here’s an example of a weaker and far-too-common hypothesis:
“If I make our landing page copy more personal, then we’ll get more click-throughs due to customers saying on surveys that they felt the previous copy was too generic and felt cold.”
Now a stronger and more detailed hypothesis might look something like this:
“If we make the copy shorter, highlight key features, and put the call-to-action above the fold, we will see conversions go from the current 0.8% to the site average of 2.6%, due to data gathered from heatmaps, survey results, and quantitative data, which shows that product page #3 is too long and visitors aren’t converting because they don’t scroll to the bottom to see the call-to-action.”
Stage 3: Prioritizing ideas
Priorities help us make good decisions and guide our life choices. What we prioritize makes us who we are. In the conversion rate optimization process, this same principle applies.
Not only does prioritizing ideas and hypotheses help you solve the most pressing issues in your business, but it also creates a precedent that you can follow for future optimization practices.
Done properly, you’ll have an effective testing system ready to go for everything.
Now let’s look at some popular prioritizing frameworks!
Not the delicious dessert, PIE is short for Potential For Improvement, Importance, and Ease.
Source: Practical Ecommerce
The “Potential for Improvement” aspect looks at how likely it is that the hypothesis will result in an overall improvement. “Importance” refers to the gravity of the observed problem, and “Ease” looks at how much effort is required to implement the hypothesis (hours, days, weeks, etc.).
Using a scale from 1 to 10, hypotheses are ranked from lowest to highest.
The problems with this model?
The Potential part is often difficult to quantify/estimate. Secondly, the prioritization scale of 1 to 10 can lead some businesses to prioritize minor issues in hopes of achieving significant results.
Travel website HotWire has an additive prioritization method that takes the emotion out of A/B testing.
If an idea meets a requirement, it’s given 1 point. If it doesn’t meet a requirement, it gets zero.
These points are then added up in a spreadsheet to give each idea an overall score out of 10. These ideas are then ranked according to the overall score. To learn more about this framework and to build a similar one, check out this blog post.
This method works best for large companies that have hundreds and thousands of optimization ideas in the backlog but also for smaller companies that want to implement ideas quickly.
Created by Peep Laja, this framework focuses on asking a set of questions about user behavior to better prioritize ideas.
The goal of this method is to make any “potential” or “impact” rating more objective, foster a data-informed culture, and make the “ease of implementation” rating more objective.
The questions in the framework look like this:
- Is the change above the fold? → Changes above the fold are noticed by more people, thus increasing the likelihood of the test having an impact.
- Is the change noticeable in under 5 seconds? → Show a group of people the control and then variation(s). Can they tell the difference after seeing it for 5 seconds? If not, it’s likely to have less impact.
- Does it add or remove anything? → Bigger changes like removing distractions or adding key information tend to have more impact.
- Does the test run on high-traffic pages? → Relative improvement on a high-traffic page results in more absolute dollars.
A solid aspect of this framework is that it’s fundamentally rooted in data.
It asks if every single observed issue was discovered in user testing, heat mapping/ tracking, or any other analytics tool. This turns prioritization efforts from “I think that we should focus on X” to “The numbers say that Y and Z are the two most likely causes of our low conversion rate.”
TIR stands for Time, Impact, and Resources. The ranking system in the TIR model runs from 1 to 5.
Time: How many calendar days, man-hours, development hours, etc. will be necessary for this test to achieve maximum impact?
“A score of 5 would be given to a project that takes the least amount of time to execute and to realize the impact.”
Impact: The amount of revenue (or reduced costs) that will change in the event of a successful test. Are you testing on the whole customer base or just a segment? Are you looking at a 3% increase or 15%?
“A score of 5 would be given to a project that will generate the most benefit in terms of revenue or reduced costs.”
Resources: How much are the tools, people, and everything else associated with this test going to cost?
“A score of 5 is given when resources needed are few and are available for the project.”
Founded by Conversion Rate Optimization veteran Bryan Eisenberg, the TIR model encourages you to dig into the human aspect of conversion rate optimization.
This model makes you think about three important questions before testing:
- Who are we trying to convince?
- What particular action do we want them to take?
- What action do they actually want to take?
What you’ll often find is the action that you want visitors to take isn’t necessarily the same action they want to take. This is where real customer feedback comes in handy.
When you really dig deep and mine those actionable points from your customer surveys and other data collection tools, you can make more powerful improvements to a particular page, email funnel, etc.
Step 4: Implementation and testing
Now that you’ve prioritized your hypotheses and you know which tests are the most pressing, it’s time to put those hypotheses into action.
Potentially game-changing tests require you to have the best tools at your disposal. We’ll review those tools later on, but first, let’s look at the types of tests you can run.
A/B testing vs. split testing vs. multivariate testing
Source: Wingate Media
A/B testing is when you compare two or more versions of the same page by looking at the conversion rates and metrics that matter to your business (such as clicks, views, signups, etc.).
For example, if you change the title on a landing page, you can target all landing pages at once and they will be considered as variations of the same group. This group is a name or observation title you give to a particular test (example: Landing page title testgroup1). Hopefully, you have a much cooler group name, but you get the picture.
A/B tests are great if you want to test radical ideas for conversion optimization and also if you want to make small changes. And they’re a great way to get fast results and minimize test time.
If you have a large amount of traffic to your site and want to test key sections on a page, this is where you’d run multivariate tests. A/B testing looks at making singular changes to a whole page whereas multivariate testing looks at changing key sections on a page and how they interact with each other.
This being said, multivariate testing is more complicated than A/B testing because there are more layers involved. When you test different key sections, you can get a huge number of possible combinations that may prove too overwhelming to deal with if you’re not an experienced marketer.
Check out this post to get an idea of what a multivariate test looks like.
Split testing is where you test one element on a page and see how the results for that page are different from the original version. This may look similar to A/B testing… because it’s the same.
The terms are often used interchangeably, and in fact split testing and A/B testing are intrinsically the same.
The difference between A/B testing (aka split testing) and multivariate testing is that the former tests one variation whereas the latter tests multiple combinations at once.
How long should A/B tests last?
This is a tricky question because a lot of factors come into play.
These factors include things like sample size, statistical confidence, seasonality, representativeness of your sample, and timing. There’s no clear answer as to how short or long an A/B test should last because… drum roll… it depends on your industry and a host of other factors.
However, that doesn’t mean that running a test for one or two days is enough. Generally, a few weeks to a month can be regarded as a safe range for a test, assuming data collection was done correctly, the conditions weren’t out of the ordinary, and the test was carried out by experienced marketers.
Determining the validity of your tests
Determining the validity of a test can be done in 3 steps:
- Calculate the minimum sample size: Define what level of confidence you’d like in your test results (ex: 90-95% is largely considered a solid target to aim for) and calculate a sample size based on that number. This will give you the minimum number of visitors that your variations need.
- Check for discrepancies in segments: Before completing the test, you should know how to segment your visitors. With the minimum sample and segments, check for major discrepancies and if there aren’t any, keep the rest running.
- Assess your business cycle: As mentioned above, business cycles and seasonality can play a large role in the validity of any optimization tests. Run the test in different cycles and compare how they fare against one another (ex: are visitors and sales the same in Q4 with Christmas/New Year as they are the rest of the year?)
Conversion rate optimization is not an easy thing to tick off your checklist.
It’s a perpetual process that will kick your ass many times, but it will also take your business to the next level if you learn to embrace it. This goes for newbies and professionals alike.
Stage 5: Learn and review
If you’re looking to increase the number of people who sign up for a free trial for a product, you might want to set up goals for people who make it to the signup page and people who actually make it across the line and sign up.
In whichever testing platform you use, you should see the running test and some sort of indication as to whether that new variation has improved conversions or not.
Carefully look at the two numbers (for the original vs the new variant) and look at the percentage of growth as well as the potential it has (also in percentages) to beat the original. If that percentage is short of the ideal 90-95% goal, keep optimizing and keep running tests to hit that goal.
If you end up with inconclusive results, here are a few things you can do:
- Segment the data: Individual segments often reveal clearer data than lumped segments. Look at segments like traffic sources, devices, and other things that make sense in your business. Sometimes you need to dig even deeper into the numbers to find clarity, especially with A/B tests.
- Don’t test things that don’t matter: Another reason for inconclusive results is often tests that were run on things that didn’t actually matter to the business. Make sure all of your tests are backed up by hypotheses and are clearly prioritized before getting itchy fingers to test every single thing on your page.
- Challenge your hypothesis: If you follow a process and still get inconclusive results, it could be time to revise your hypothesis and even scrap it all together. You could test new variations on the same hypothesis or go back to the drawing board to better understand the data you collected and form a stronger hypothesis.
How AI is transforming the conversion rate optimization process?
Conversion rate optimization has witnessed a transformational shift with the advent of artificial intelligence (AI).
The CRO process is multifaceted, as we’ve discussed in this article, and this process demands substantial time and effort. It can easily become a full-time job.
AI can automate approximately 99% of the repetitive CRO tasks, including data collection, idea generation, and A/B testing. By delegating these tasks to AI, you can free up your valuable time to focus on critical activities that require your human touch.
Let’s take a look at a few use cases of AI that will help to increase your site’s conversion rate:
- With the help of AI, you can personalize your landing pages for each and every visitor. You can tailor any headline, description, or text based on the interest of your visitor—100% automatically.
- You can automate A/B testing. Just pick the elements on your website you want to test, and let AI do the rest of the job.
- Use the power of AI to optimize your product pages—create better headlines, descriptions, and benefit lists, and run A/B tests to create the ideal product page.
- You can tailor the messaging of your popups to each visitor’s interest automatically with the help of AI and monetize your website traffic better.
The 7 best conversion rate optimization tools you can use
Now let’s check the 7 best conversion rate optimization tools that can help you with the conversion rate optimization process.
1. OptiMonk AI
OptiMonk AI is an AI-powered CRO platform designed to automate processes like A/B testing and personalization to maximize your website conversions.
With OptiMonk AI, you’ll get more conversions, a higher marketing ROI, and a better customer experience. And here’s the twist—you can achieve all that without spending dozens of hours on optimizing your website, without hiring a pricey CRO expert, and without buying all the CRO courses in the world.
Optimizely is one of the world’s leading experimentation platforms, allowing marketing and product teams to test, review, and deploy all sorts of digital experiences. More specifically, Optimizely gives you access to a suite of A/B testing tools that allow you to effectively target your messaging and launch more personalized campaigns.
Whether it’s your homepage or your sign-up page, you need a solid tool that can help you “squeeze the most juice” out of it. Unbounce is a leader in landing page optimization and allows you to easily customize and test different versions of your most important web pages. You can then study which versions work best (and why) to improve your conversion rate.
If you’re someone who doesn’t want to fuss around with coding or graphic design, Unbounce becomes even more appealing as you can get beautiful, responsive landing pages within minutes.
Lyssna is a remote user research platform that takes the guesswork out of design decisions by validating them with actual users. Also known as the swiss army knife of user research, you can perform a variety of tests such as first click tests, preference tests, and five-second tests to confirm your hypotheses.
With this tool, there’s no more “I think that’s the color users like” and more “the research and the tests we perform prove that users are most responsive to this color palette.”
Are you picking up the overarching theme of this post yet? It’s all about coming as close to certainty as possible in your decisions because your data sources support your ideas!
5. AB Tasty
AB Tasty is an all-in-one conversion rate optimization platform that allows you to run a multitude of tests on just about anything. With AI-powered implementation and personalization, you can quickly get user insights, experiment, personalize, and increase engagement and conversions.
With some of the biggest names in various industries using AB Tasty as their testing platform, you can be sure you’re using one of the best products on the market when it comes to conversion rate optimization.
VWO is another all-in-one platform for conversion rate optimization that helps you conduct visitor research, build an optimization roadmap, and run continuous experiments.
We spoke about the importance of creating a solid conversion experimentation system so that you don’t always have to start from scratch for future experiments. Well, you’ll be glad to know that VWO is rooted in process-driven optimization and can help you find ways to constantly improve your user experience.
Moreover, you can run A/B tests at scale without reducing performance because VWO is built to handle enterprise-level tests.
Rebrandly is a well-known platform for creating and managing custom short URLs. It’s also a powerful analytics tool that can help you run conversion rate optimization tests on your website.
Custom short URLs (or branded links) can be used to collect a ton of source data based on clicks—data you can use to test small but important elements on any page you create, from buttons to navigation menu options and much more.
What is conversion rate optimization?
Conversion rate optimization (CRO) is the process of enhancing the effectiveness of a website or marketing campaign to increase the percentage of website visitors who take a desired action. This action could be making a purchase, signing up for a newsletter, filling out a contact form, or any other goal defined by the business.
What are some common challenges businesses face with CRO?
Businesses often face challenges like identifying the most impactful areas for optimization, allocating sufficient resources, interpreting and acting on test results effectively, and maintaining a continuous optimization process. Additionally, understanding user behavior and preferences can be challenging.
Is CRO only for ecommerce websites?
No, CRO strategies are valuable for all types of websites, not just ecommerce businesses. Any website aiming to achieve specific goals, such as lead generation, sign-ups, or content engagement can benefit from increasing conversions.
How do I measure the ROI of CRO efforts?
Measuring the return on investment (ROI) of CRO involves comparing the costs of optimization (including tools, labor, and resources) to the monetary gains from increased conversions. Calculate the increase in revenue or value generated as a result of CRO efforts and compare it to the expenses incurred. A positive ROI indicates that your CRO efforts are effective.
In this article, you learned about the 5 stages of the conversion optimization process.
Remember: conversion rate optimization is an evolving process. There’s a huge learning curve to just about every stage of the CRO process and it can be overwhelming at times. Commit to learning the process, commit to regularly optimizing and always aiming for better results, and commit to growth.
Don’t forget that with the help of OptiMonk AI, you can automate the CRO and website personalization process and turn visitors into customers more effortlessly. If you’re interested in OptiMonk AI, click here to learn more about it!