A/B Testing Tracking: Key Metrics and Strategies to Optimize Your Marketing Efforts
If you are here, chances are we have managed to pique your interest with a topic about a basic but powerful eCommerce technique: A/B testing.
The premise of this strategy seems self-explanatory, but it does take specific measures to execute, compare, and most importantly, track.
In this blog post, we will walk you through the top recommended metrics to keep an eye on in A/B testing tracking, alongside handy tips to help you save time and resources while achieving valuable results. Let’s get started!
What is an A/B Testing?
As eCommerce sellers, the concept of A/B testing is nothing short of familiar. A/B testing, AKA split testing, is the method that online merchants use to compare two versions of a web page.
The ultimate goal of an A/B test is to pinpoint the better one of the two, thus, applying necessary changes that benefit your business. While web pages are the most popular test subject, other assets such as ad copy, email format, and so on can be a part of an A/B test as well.
Since A/B testing can be carried out with different budgets, it is crowned as a surefire website optimization technique for business owners of all shapes and sizes. With a basic set of online tools and a small-scale test pool, insightful results are within reach for novice merchants. For those with more flexible financial means, it is a good call to hire experts to shoulder the work and expand the test audience. No matter which approach you go for, A/B testing is a highly recommended method to optimize your website in the long run.
Some benefits of A/B testing when successfully prosecuted are:
- Boosted conversion rates
- Improved user experience
- Increased revenue
- Data-driven decisions
For a comprehensive analysis of A/B testing, check out our blog posts:
How to Run a Proper Shopify A/B Testing on Your Store?
A Complete Guide to Shopify A/B Testing
10 A/B Testing Key Metrics to Utilize as eCommerce Sellers
Conversion Rate
Be mindful of the conversion rate - a deciding factor in running an eCommerce business, to reap rewarding success.
Conversion rate refers to the percentage of users who perform a desired action, or to convert, on your website. This concept has been thoroughly dissected by eCommerce experts and consequently, is regarded as a vital element that every eCommerce merchant needs to pay attention to. In A/B testing, it is the direct indicator of how effectively a version can get a user to take a desired action, hence the importance of this metric.
The benchmark for comparison is straightforward: whichever version achieves a higher conversion rate is proven to be the more efficient option to go with. The differences in details like content, layout, design choices, and features should also be taken into account to make changes accordingly.
Click-through Rate
Click-through rate showcases how engaged your customers are with your website, so it is undoubtedly an important factor in A/B testings.
Click-through rate (CTR) is another exclusive concept that was introduced alongside digital platforms, especially eCommerce. In layman’s terms, it is the number of clicks on a link (i.e. an advertisement) in comparison to the views it gets. Here is how it is calculated:
Click-through rate = (Clicks/Views) x 100.
If one variation in your A/B test is reported to have a higher CTR, it is proof that certain elements such as product images, CTA buttons, and blog headings appear to be more compelling to users. Thus, this is a solid metric to indicate the superior version in this area.
Learn more: Click-through Landing Pages: The Art of Improving Your Website Conversion Funnel
Bounce Rate
Bounce rate is the kryptonite of any online business, as it signals the inefficiency of one’s website’s performance.
Bounce rate is a pretty self-explanatory term, as it represents the number of visitors who leave a web page without taking additional actions that benefit your business. For eCommerce sites, it is best to keep this element to a minimum, as excessive bounce rate exhibits clear discrepancies on your website. Below is the equation to keep in mind:
Bounce rate = (Single page visits/Total number of visits) x 100.
In A/B testing, bounce rate is a critical metric that allows sellers to determine which details to retain and discard, whether it is content, layout, or navigation components. An informed decision is then made based on this data.
Goal Completion
Goal completion is crucial in tracking whether a user has completed a set of predetermined actions on your website or not, making it a well-rounded metric for an A/B test.
Goal completion is the utmost metric to take notice of in terms of measuring user engagement and website performance. From the moment buyers land on your site, a series of goals are introduced, from making a purchase, filling out a form, signing up for a newsletter, and the like. The outcome of each session contributes to the overall goal completion, providing direct insights about which version prevails. As a whole, this is an unmissable metric for A/B testing.
Abandonment Rate
Abandonment rate can tell you a lot about how your prospects behave in their shopping journey and identify the best version where this element is not prominent.
Another code-red element that businesses should keep at bay is the abandonment rate - a metric that records a user’s behavior from the start of a process (i.e. filling in a survey) to the moment they abandon it. In eCommerce, the cart abandonment rate is the most common during a user journey.
By comparing this metric across two different versions of the same web page, merchants will gain awareness of which details to improve to prevent user drop-offs. If one variation manages to achieve a lower abandonment rate, it is a sign to implement these practical functions into your site.
Active Users
Tracking active users gives you insights about a specific group of buyers who interact with your business the most, which is the ultimate reason to alter strategies for new improvements.
Active users are the group of people who interact and engage with your website the most, be it clicking on a link, or spending a considerable amount of time navigating through the page. With two different versions, this metric can be calculated to the utmost accuracy, given real user interactions. Take notice of the percentage of active users and their participation in each variation to conclude the more efficient one in an A/B test.
Scroll Depth
Scroll depth gives you a closer look at how far down a user scrolls on your web page. Its most useful data is to show whether they interact with your whole page or only a portion of it. As a result, merchants will be aware of the page’s most engaging section and the average drop-off point. For A/B testing, this metric stands out as the MVP in helping website owners pinpoint the design elements, content, and visual components that call for imminent improvement. The version with a shallow scroll is often a telltale sign that it is not competent enough, while one with a deeper scroll comes out as the winner.
Engagement Rate
See how the users engage with the different versions of the tests, and the preferred version will be apparent.
While active users are a useful metric in identifying the number of individuals who interact with your website, the engagement rate focuses on the quality of these interactions. With actions like clicks, likes, comments, shares, product views, cart adding, and the like taken into consideration, this metric is one of the most brilliant ones to determine the preferable A/B variation. A user-friendly experience via content-rich web pages is the absolute objective of this metric.
Retention Rate
Pay attention to the retention rate by monitoring the percentage of users who come back to your business over a certain period of time.
As business hustlers, you are no stranger to the term customer retention - an objective many strive for. Simply put, it is a metric that measures the percentage of users who come back to one’s website or app after the first visit. With this in mind, it is understandable that this metric would be the most vital in tracking customer loyalty for brands that aim for long-term success rather than short-term revenues. In the context of A/B testing, variations with certain design, content, and navigation system differences will assist you in selecting which factors encourage buyers to come back for more. Overall, customers are what drive your business forward, so this metric is not to be overlooked.
Revenue
Revenue helps you make the ultimate decision: whether to carry on with the A/B tests or not.
The last key metric in the list is revenue, the one metric that makes or breaks a business. No matter how much traffic your website gets, if there is no revenue, the store is bound to face impending challenges.
In an A/B test, revenue refers to the money generated through a customer’s actions, such as placing an order, signing up for a subscription, or paying for an online course. Merchants are advised to create two versions of pages like the checkout or promotional landing pages, where transactions are most likely to occur. With profits standing at the core, this metric is incredibly helpful for sellers to apply sales-driven changes to the targeted web page after thoroughly assessing the test subjects.
3 Best Practices for a Successful A/B Testing Tracking
Pick the Metrics Based on Your Objectives
While we have got you covered with the list above, it is vital to only pick the metrics that contribute to your objectives. It is a clever practice to set out KPIs that align with your pre-determined goals, whether it is to improve the conversion rate or monitor customer engagement rate. Use the following guidelines to easily distinguish which key metrics to opt for in an A/B test:
- To maximize sales: revenue, average order value (AOV)
- To track engagement: click-through rate (CTR), scroll depth, average session duration
- To improve user experience: bounce rate, goal completion, abandonment rate.
By knowing what you want, it is intuitive to find a way to achieve it.
Set a Reasonable Test Duration
Make sure to set up a clear-cut schedule to avoid an unreasonable A/B test duration.
The duration of an A/B test can vary based on each business’ objectives, so your task is to keep an eye on the test as closely as possible to decide whether to continue or conclude. If it is too short, you won’t be able to gather enough data to make a worthy decision; if it’s too long, your resources could be exhausted. If need be, use third-party tools that specialize in data analysis to obtain accurate results without having to manually monitor the test for a long period of time.
Don’t Forget to Segment Your Customers
Customer segmentation is the key practice to attain valuable results with A/B testings.
This tip is applicable to everyone, but especially large-scale stores with a diverse portfolio of customers. By segmenting your audience into different groups based on age, gender, location, purchasing behavior, etc., you will be able to paint a bigger picture of how each demographic responds to the test. Better yet, it can be done affordably, giving small-volume businesses the opportunity to acquire information on their customers and their brands to lead them on the right paths.
Master Your A/B Testing Tracking and Watch Your Store Grow
A/B testings go beyond running different versions of your website and call it a day. The insights that you manage to gather from these tests play a crucial role in empowering your business and helping you make optimal decisions. With a proper tracking process in which relevant key metrics are prioritized, it is simple to refine your strategies and grow your brand. Take the information in our article to master A/B testing, drive your business forward, and rise above the competition.
FAQs about A/B Testing Tracking
1. Conversion Rate
2. Click-through Rate
3. Bounce Rate
4. Goal Completion
5. Abandonment Rate
6. Active Users
7. Scroll Depth
8. Engagement Rate
9. Retention Rate
10. Revenue
For a more detailed analysis of each metric, follow this link: 11 A/B Testing Key Metrics to Utilize as eCommerce Sellers.
Having two many versions in a test at once
Faulty sample size
Insufficient test duration
Inconsistent data collection method