Top 6 Things to Do Before Running an A/B Test
The success of any e-commerce store can be directly related to digital experimentation programs.
One such experimentation program is A/B testing, or “split testing.”
Performing an A/B test requires overcoming critical barriers to business optimization.
In the following post, we will dive into the top 5 things to look out for before running an A/B test.
What Is A/B Testing?
A/B testing, also called split testing, refers to the process of comparing two or more separate versions of a variable and measuring the difference in performance.
In this case, a variable can include anything from an entire landing page, to a page element, email, or any other marketing asset.
During the experimentation process, individual groups help optimizers determine which version provides the maximum impact on business metrics.
By eliminating the guesswork, A/B testing allows data-driven decision-making with each web update.
If you need more information on the process, check out our guide on how to prepare for AB testing
Designation A refers to the original testing variable, also known as the control variable.
Designation B is a variation of the control variable, or its new version.
The version that ultimately yields positive growth after many rounds of split testing is regarded as the ‘winner’.
The key to optimizing your website, therefore, is implementing those winning changes.
Take for example the home page of your website. Depending on the number of designs you have, visitors will be fed one of two or more versions of the site.
After the testing period, you will obtain critical metrics such as clicks, traffic, and conversion on each version.
However, caution should be taken when selecting the right metrics, as they should be uniquely tailored to your kind of business.
For example, a B2B lead marketer’s metric is the generation of qualified leads. On the other hand, eCommerce is more focused on the sale of products.
Nowadays, A/B testing is the crucial component of CRO (conversion rate optimization).
CRO demands both quantitative and qualitative insights from users.
Information such as engagement rate, user behavior, pain points, and general satisfaction can improve the website’s performance.
A/B testing drives business revenue forward, so it’s not something you want to ignore.
Keep in mind, standard A/B testing is different than AB testing for headless CMS
. But for the purposes of this article, we won’t need to get into that.
Why Consider A/B Testing?
Depending on your business’ mission, different problems can arise.
For example, in the world of B2B marketing, the primary complaint is unqualified leads, whereas eCommerce stores might struggle with high cart abandonment rates, and bloggers may battle low viewer engagement.
So, what do these problems all have in common?
The core conversion metrics are affected by common problems like drop-offs, bounce rate, and leaks in the conversion funnel.
Here’s how A/B testing mitigates this:
1. Increase ROI from Existing Traffic
Acquiring high-quality, relevant traffic on your website is of the utmost importance when you desire to increase your conversion rate, according to the most experienced optimizers in the industry.
With A/B testing, you get the most out of your current traffic, without even having to spend extra dollars on luring in new traffic.
Even the slightest of changes to your landing page can make a significant contribution to overall business conversions.
A/B testing helps you uncover those details, resulting in higher ROI.
2. Eliminate Friction for Your Website Visitors
When a visitor comes to your website, they want to achieve a specific goal.
They may want to browse your web pages to read more about a particular topic, purchase that product, or understand more about the services you offer.
Regardless of their goal, visitors can experience hurdles while browsing.
This could include searching for hard-to-find buy now buttons, unclear CTAs, confusing copy, and much more.
A bad user experience is a direct result of the visitor’s inability to achieve their goals.
Conversion rates suffer considerably from this friction.
Tools such as heatmaps, Google Analytics, Google Optimize, or website surveys help solve those pain points.
User behavior analysis provides data for all businesses: travel, eCommerce, education, SaaS, media, and publishing.
3. Low-risk Modifications
Minor changes to your web page are the bread and butter of split testing.
You want to avoid redesigning the entire page, since this increases the risk of jeopardizing your current conversion rate.
When you conduct A/B testing, you lean into test data to optimize resources for maximum output, while making minimal modifications.
This is how you start a breeding ground for growing ROI.
A good example is while changing a product’s description, which is the optimal time to perform split testing.
When you change the product’s description, you have no clue how visitors will react to the change.
By running an A/B test, you can analyze their reaction and affirm which route to take during future tests.
A/B testing is also useful while making other low-risk modifications, such as small feature changes.
Launching a split test before introducing a new feature helps you ascertain whether or not the changes you’re implementing have positive feedback from your audience.
Incorporating a change to your website without proper testing may or may not be beneficial. A/B testing provides you that certainty.
4. Mitigate Bounce Rate
Bounce rate is one of the most telling metrics when it comes to judging your website’s performance.
A mismatch of expectations, confusing navigation, or too much technical jargon may increase your “bounce rate,” or the rate at which users abandon your site.
Since each website caters to different audiences, it’s impossible to find a one-size-fits-all solution to minimize bounce rate.
This exact scenario is where a split test would be beneficial.
By testing multiple variations of an element on your website, a multivariate test helps you find the best version of your site.
We previously discussed how it also helps eliminate visitor pain points, but it doesn’t end there.
The optimal version of your website improves your visitors’ overall experience, resulting in an increased time spent on your website, and in some cases, more paying customers.
5. Make Measurable and Significant Improvements
A/B testing is an entirely data-driven process that eliminates guesswork.
While gut feelings or instincts may be helpful, they can’t provide tangible indicators for future reference.
You can quickly determine a ‘winner’ or a ‘loser’ based solely on metrics.
As highlighted previously, these indicators can be the number of demo requests, time spent on the page, cart abandonment rate, and so on.
6. Redesign for Future Business Gains
Redesigning your website can include anything from a minor button color or text tweak to a complete overhaul of the source code.
carried out a test where a red CTA button outperformed a green one by 21 percent
. They took the results from a pool of more than 2000 page visits.
This is just one example of how A/B testing provides data-driven decision-making.
Even after your final design has been chosen, the testing is not finalized.
This is because a single, isolated test doesn’t ensure the most engaging version of your website, but continuous testing does.
Upon releasing the new version, you can test other web page elements for maximum performance as well.
Testing multiple variants over the course of a few weeks or even months is the best way to ensure you are gathering statistically significant results.
How To Prepare For A/B Testing?
While A/B testing, you should not regard the test as an isolated optimization exercise.
A/B testing is a part of a holistic CRO program.
Always plan, then prioritize
You cannot wake up one day and decide to test your web page the very same day. CRO doesn’t work like that.
It requires a decent amount of brainstorming, coupled with real-time data from visitors.
- The first step is to analyze the existing website data and gather visitor behavior data.
- Next, prepare a backlog of action items based on the website data.
- Then, prioritize each of these items and run the tests.
- Finally, conduct post test analysis to draw insights for the future.
The main goal is to have a structured approach to A/B testing by creating a testing calendar. It requires the following five steps:
Step 1: Website Performance Assessment
In this stage, you’ll start planning your split test program.
You need to assess the website’s performance relative to visitors’ interactions with it.
The assessment phase requires you to determine what is happening on your website, the main reasons behind it, and how visitors are behaving.
Your website performance should also match your business goals, making it critical that you clearly understand your business goals.
Google Analytics is an incredibly convenient tool to use in these situations.
Set up GA for your website based on your pre-defined goals, and let your key performance indicators dictate the pace.
Let’s take, for example, an online mobile phone case store. Let’s pretend their goal is to grow the store revenue by increasing the number of online orders of their phone cases. The KPI that tracks this goal is the number of phone cases that have been sold.
However, it doesn’t end there.
Defining website goals and KPIs is only part of the assessment.
Understanding your visitors is the other side of the coin.
We’ve previously outlined the importance of logging behavior data. Once it’s collected, record the observations and begin planning the campaign from that point.
Data always drives higher sales.
The next step in this phase is the preparation of a backlog. A backlog is the list of unperformed tasks, test ideas, or materials that haven’t yet been processed.
In terms of A/B testing, a backlog should contain a comprehensive accumulation of all the website elements that you decide to test.
After a data-backed backlog is prepared, you need to formulate a hypothesis for individual backlog items.
Once you’ve gathered enough data in this stage, you will be able to resolve any problems that arise regarding what happens on your website and why.
For example, after performing qualitative and quantitative research, you conclude that the lack of multiple payment options led to a loss of customers on the checkout page.
You can formulate the following hypothesis: “Adding multiple payment options reduces the drop off on the checkout page.”
Step 2: Prioritization
Prioritizing your test opportunities is the next step in creating an A/B testing calendar.
By prioritizing, you scientifically sort numerous hypotheses.
In step one, you’ve honed your goals and gathered website and visitor data. Along with the backlog, the hypothesis for each candidate marks the halfway point of your optimization roadmap.
By now, you should have all the necessary information to fix leaks in your funnel.
But keep in mind each item carries a different value in terms of business potential, making it imperative to determine which backlog candidates are suitable for testing.
While prioritizing items, a few things are crucial: potential improvement, page value, cost, traffic on the page, etc.
An essential thing in this step is the exclusion of subjectivity.
You need to be 100 percent objective in your prioritization framework. Gut feelings, personal ideas, and opinions about test ideas often get in the way of our decision-making.
As mentioned before, this is a scientific method that demands data-backed choices and requires maximum objectivity. Adopting a prioritization framework will keep it objective.
In the following section we will describe the three most popular frameworks recommended by experienced optimizers:
- CIE Prioritization Framework
- The LIFT Model
- PIE Prioritization Framework
1. CIE Prioritization Framework
CIE framework defines three parameters that are used to rate your test. All of the following parameters use a scale of 1 to 5.
- Confidence – How confident are you in your hypothesis and ability to achieve your expected outcome?
- Importance – How crucial is the test, based on the hypothesis or data (highest earning page, most traffic, highest AOV landing page, etc.).
- Ease – The complexity of the test. The lower the score, the more difficult it will be to implement the changes identified for the test.
2. The LIFT Model
The LIFT model
focuses on analyzing web and mobile experiences and developing solid A/B test hypotheses.
It utilizes six conversion factors for page visitor experience evaluation:
- Value Proposition
3. PIE Prioritization Framework
The primary question the PIE framework aims to answer is: “Where should I test first?
Like the CIE framework, PIE has three criteria you need to meet – potential, importance, and ease
- Potential describes a page’s ability to improve. The first step provides all the necessary data to determine your potential.
- Importance refers to a page’s value, i.e., how much traffic comes to the page. For example, let’s say you’ve identified a problem page, and it has low traffic. Here, low traffic is the exclusion factor, so you should devote your attention to pages with more traffic.
- The final criterion is Ease. Just like in CIE, it defines the difficulty of running a test on the page or element. Landing page analyzers are a great way to determine the present state of your landing pages. You can use them to estimate the scale of the necessary change and prioritize which changes to implement first.
Step 3: Resource Management
In recent times, CRO and A/B testing processes have gained mainstream attention, so A/B testing agencies have popped up to fulfill this need.
As a result, the talent pool for optimization is somewhat lacking.
Even though major players in the industry, such as Facebook and Amazon, benefit from optimization, the process is still in its infancy.
Most companies do not have dedicated optimization teams, or they are limited to small groups of people.
Asking for help from an external team is one way to combat the lack of expertise.
If you need help, ask for it! Start by checking out our article on how to outsource AB testing
Also, by having a prioritized backlog, you enable a small CRO team to focus their limited resources on items of the most importance.
More often than not, optimization is assigned to full-time employees who are busy with other projects. A direct consequence of this is decreased morale and aversion towards optimization.
Human resources aside, you need to consider the financial aspect of these processes as well.
Optimization specialists are not easy to find, nor are they cheap. However, when it comes to the actual software, your options are not as limited. There are hundreds of A/B testing tools, both high and low-end.
But picking one at random is not a sound strategy.
Choosing the cheapest option hurts your long-term perspective, as your ROI decreases every time you increase the demands of A/B testing.
Consider consulting an expert before signing a check for A/B testing software.
Step 4: Quality and Quantity Over Duration
In the case of A/B tests, duration is less important than statistical significance.
For example, you can run a test for a year, and only 2,000 people might visit the page in that period. Having too few visits will not provide enough representative data of your target audience.
Keep in mind you also need to run the test long enough to identify any evidence of a “result convergence.”
A result convergence occurs when the difference between two variations decreases over time. When you spot a convergence, it is an indication that the variation doesn’t have as much of a significant impact on the KPIs you’re tracking.
A/B testing can be performed in a matter of several weeks, as long as you have a steady influx of traffic.
To further illustrate this idea, consider studying A/B testing examples
from other websites. Most companies publish their A/B results on their marketing blogs so others can benefit from them.
Step 5: Result Analysis
The final step in systemizing A/B tests is learning from your completed tests.
Optimization is most beneficial when you understand previous cases and apply those lessons to the future.
It is good practice to let them run for a stipulated amount of time, stop them, and then analyze the data gathered in that period.
You will immediately notice the version of your web page that out-performed the others.
Now, your team needs to figure out why this version was the winner.
We can classify the outcomes under three categories:
- One of your variations has won with statistical significance
- The control variable is the best version and has defeated the variations
- The test has failed and produces intangible results. A/B test significance calculators will help you determine the significance of your test results.
The first two scenarios do not indicate that you are done testing.
A winning version of a web page can almost always be optimized further.
Improve the version and continue testing.
If the third scenario occurs, review steps one through four and identify where the mistake occurred.
A/B testing is the process of comparing two or more separate versions of a variable.
As a result, you can measure the difference in performance between multiple variations of the same web page.
In A/B testing, “A” refers to the control variable, while “B” refers to a variant of the control variable, giving it the name multivariate testing.
A/B testing eliminates all the guesswork and enables data-driven decisions.
There are six reasons why you should implement A/B testing:
- Increase ROI from Existing Traffic
- Eliminate Visitor Pain Points
- Low-risk Modifications
- Mitigate Bounce Rate
- Make Measurable and Significant Improvements
- Redesign for Future Business Gains
Preparing for A/B testing is not as simple as deciding to do a test.
A/B testing is just one component of the larger Conversion Rate Optimization process which requires a considerable amount of brainstorming and real-time data from visitors.
We’ve isolated five crucial steps every website owner needs to succeed in A/B testing:
- Step 1: Assessment
- Step 2: Prioritization
- Step 3: Resource Management
- Step 4: Quality and Quantity Over Duration
- Step 5: Result Analysis
A/B testing has yielded significant ROIs for companies such as Facebook and Amazon, and it would be silly to ignore it.