Experimentation and A/B Testing: A Must-Use E-commerce Growth Strategy

Controlled experimentation allows marketers to iterate and research fast, leading to a data-informed decision
Experimentation and A/B Testing: A Must-Use E-commerce Growth Strategy
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When it comes to e-commerce,A/B testing and experimentation can really open up some amazing competitive advantages.A/B testing is the process of comparing two versions of the same webpage against each other, to identify which one is better performing than other. It is also called split testing.

Controlled experimentation allows marketers to iterate and research fast, leading to a data-informed decision. It is widely used for augmenting the performance of websites, emails, mobile applications, and much more. If incorporated in the right manner, controlled testing can set the foundation of a data-driven marketing strategy to improve overall customer acquisition, boost conversions, increase sales, and lead generation results.

In this article, we are going to deep dive into some key tactics that can help you create a more effective experimentation strategy.

Basics of A/B testing process

In A/B testing procedure, you should start by identifying what you need to test and what your objective is for testing it. Once defined, you are then required to create two different versions of the selected element also known as the control group. Showcase these versions to two similarly sized audience groups and examine which one performed better over a period of time.

Some of the key elements to conduct A/B testing are Pop-ups, images, product descriptions, Headlines, CTAs, social proof, dimensions such as color, placement, email marketing, size, landing pages, etc.

Example 1- To check if the size of the subscriber button affects the click-through rates. To A/B test this, you will start by designing different sizes of subscribe buttons. Then, you will showcase these versions to a predetermined percentage of equally divided, site visitors. After the testing period, you can analyze your results by analyzing which button size caused more visitors to click.

Why is A/B testing so crucial?

A/B testing is a very potent tool to achieve higher eCommerce growth. The goals of A/B testing are a crucial component of the entire process. Some of these goals include:

  • Increased web traffic: Testing different types of web page titles and blog posts to identify the website traffic and increase it.
  • Lower bounce rate: Testing different web page features, fonts, design, feature images, to identify customer’s bounce rate and combat it for high retention. 
  • Higher conversion rate: Testing different colors, locations, texts, etc, on your CTAs to identify who clicks on these to reach the landing page and therefore define your conversions.
  • Lowered cart abandonment: Testing different product photos, check-out page designs, display of shipping costs, to improve cart abandonment rate.
  • Increased add-to-cart: Testing various placements, size, design of add-to-cart button to increase add-to-cart. You can also experiment with the ‘buy now’, ‘add to cart’, ‘add to wishlist’ tabs to determine the best performing CTAs.

 

How to conduct effective A/B testing?

Here are some tips and techniques that may help you undertake A/B testing for your e-commerce website effectively:

Select your variable: While there are several variables that you would want to test, it is ideally important that you zero down on one single variable. This is important in order to correctly determine the responsible variable for the change in performance.

For example, if you are looking to increase newsletter sign-ups on your website. Then the design of the sign-ups will be your variable.

For websites with higher traffic, you can test more than one variable however the variants will also increase accordingly.

Identify the goal: Your next step in A/B testing is to identify the problem you want to resolve and decide on a measurable goal. For example, you want to increase the click-through rate of the newsletter sign-ups on your website to get higher lead generations.

Once the goal is identified, create a suitable hypothesis and examine your results based on this prediction.

Create two different versions: Once your variable and goal is defined, you can then use them to set up your “control” and “challenger.” Your unaltered version, or version A, is your ‘control’ while your alternate version, or version B, is your ‘challenger.’

In this case, the current design of the sign-up is your control and the new design of your sign-up is your challenger.

Determine your sample size and time period: For conclusive results, you need to split your audience randomly into equal-sized groups. This is done to ensure that you arrive at statistically significant results for both finite and infinite audiences.

Some more pointers to abide by while determining the sample size and time are:

  • Each variant should have a minimum sample size.
  • Both variants should be offered to different audiences but at the same time period.
  • Make sure that you run your test long enough to get substantial sample size for significant results.
  • Always expose the same variant to the same customer.

Analyze the results and take action: Identify and conclude on which variable won- the challenger or control. Once you identify the version that works best with your audience, you can then implement that variation.

For example, if version B reflects more newsletter sign-ups than version A, then you might want to consider using that variation on your homepage.

However, if none of the versions is statistically better, you can conclude that the variation doesn’t have a significant impact on the metric you are tracking or even run a new test.

To conclude, A/B testing and experimentation has a significant impact on not just the conversion rates but also the entire website experience as a whole. Constantly conducting A/B testing helps you iterate site changes and provides you a competitive advantage. By strategically employing these techniques, you can improve both your topline as well as bottom-line significantly.

 

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