What’ s A/B Testing ?
A\B Testing means analyzing two marketing strategies to choose the best marketing strategy. A/B testing is one of the valuable concepts that every Data Science professional should know. In this article, I will briefly describe the A/B Testing with Python. Let’s start…
A/B Testing
In A/B testing, we analyze the results of two marketing strategies to choose the best one for future marketing campaigns. For instance, when I started an ad campaign on Trendyol to promote my Trendyol Store post for the very first time, my target audience was different from the target audience of my second ad campaign. After analyzing the results of both ad campaigns, I always preferred the audience of the second ad campaign as it gave better reach and followers than the first one. That is what A/B testing means. Your goal can be to boost sales, followers or traffic, but when we choose the best marketing strategy according to the results of our previous marketing campaigns, it is nothing but A/B testing.
Why should you consider A/B testing?
If B2B businesses today are unhappy with all the unqualified leads they get per month, e-Commerce stores, on the other hand, are struggling with a high cart abandonment rate. Meanwhile, media and publishing houses are also dealing with low viewer engagement. These core conversion metrics are affected by some common problems like leaks in the conversion funnel, drop-offs on the payment page, etc.
- Solve visitor pain points
Visitors come to your website to achieve a specific goal that they have in mind. It may be to understand more about your product or service, buy a particular product, read/learn more about a specific topic, or simply browse. Whatever the visitor’s goal may be, they may face some common pain points while achieving their goal. It can be a confusing copy or hard to find the CTA button like buy now, request a demo, etc.
Not being able to achieve their goals leads to a bad user experience. This increases friction and eventually impacts your conversion rates. Use data gathered through visitor behavior analysis tools such as heatmaps, Google Analytics, and website surveys to solve your visitors’ pain points. This stands true for all businesses: eCommerce, travel, SaaS, education, media, and publishing.
2. Get better ROI from existing traffic
As most experience optimizers have come to realize, the cost of acquiring quality traffic on your website is huge. A/B testing lets you make the most out of your existing traffic and helps you increase conversions without having to spend additional dollars on acquiring new traffic. A/B testing can give you high ROI as sometimes, even the minutest of changes on your website can result in a significant increase in overall business conversions.
3. Reduce bounce rate
One of the most important metrics to track to judge your website’s performance is its bounce rate. There may be many reasons behind your website’s high bounce rate, such as too many options to choose from, expectations mismatch, confusing navigation, use of too much technical jargon, and so on.
Since different websites serve different goals and cater to different segments of audiences, there is no one-size-fits-all solution to reducing bounce rate. However, running an A/B test can prove beneficial. With A/B testing, you can test multiple variations of an element of your website till you find the best possible version. This not only helps you find friction and visitor pain points but helps improve your website visitors’ overall experience, making them spend more time on your site and even converting into a paying customer.
4. Make low-risk modifications
Make minor, incremental changes to your web page with A/B testing instead of getting the entire page redesigned. This can reduce the risk of jeopardizing your current conversion rate.
A/B testing lets you target your resources for maximum output with minimal modifications, resulting in an increased ROI. An example of that could be product description changes. You can perform an A/B test when you plan to remove or update your product descriptions. You do not know how your visitors are going to react to the change. By running an A/B test, you can analyze their reaction and ascertain which side the weighing scale may tilt.
Another example of low-risk modification can be the introduction of a new feature change. Before introducing a new feature, launching it as an A/B test can help you understand whether or not the new change that you’re suggesting will please your website audience.
Implementing a change on your website without testing it may or may not pay off in both the short and long run. Testing and then making changes can make the outcome more certain.
5. Achieve statistically significant improvements
Since A/B testing is entirely data-driven with no room for guesswork, gut feelings, or instincts, you can quickly determine a “winner” and a “loser” based on statistically significant improvements on metrics like time spent on the page, number of demo requests, cart abandonment rate, click-through rate, and so on.
6. Redesign website to increase future business gains
Redesigning can range from a minor CTA text or color tweak to particular web pages to completely revamping the website. The decision to implement one version or the other should always be data-driven when A/B testing. Do not quit testing with the design being finalized. As the new version goes live, test other web page elements to ensure that the most engaging version is served to the visitors.
References
https://thecleverprogrammer.com/2022/11/14/a-b-testing-using-python/
