Someone rightly said, “The most successful people are those who are good at Plan B.” In many instances in real life, we tend to believe one way is the right choice, and when it fails, we choose plan B. But what really matters is the success we achieve down the lane, irrespective of the path being taken. In the business space as well, business owners apply different strategies for better outputs. Most of them are a result of experience or intuition.

However, there is little room for intuition when businesses own gigabytes of data in the digital age. Most of the choices are data-driven and informed decisions.

A/B testing is one of the most appropriate ways of experimenting with different variants of the product or engagement for digital products. So let’s figure out what experimentation means and the expected outcome of these experiments for a digital product and company strategy.

In the early 2000s, Amazon began testing the placement of membership and payment offers on different sections of its website. The test boosted the revenue by tens of millions of dollars annually. Similarly, In 2012, Bing used A/B testing for improved results to display Ad headlines resulting in a 12% increase in revenue. 

The Anatomy of A/B testing for improved results:

1. Problem to solve	What are you trying to solve?
2. Hypothesis	What are you hoping this test will achieve?
3. Variant	What variable is changing
4. Objective	What metrics do you measure to evaluate success?
5. Targeting	Who, When, Where – run this test

Problem-solving:

For businesses, there are only two prime motives – solving a problem and achieving a goal. So, in the case of A/B testing as well, the process begins with solving a problem. 

For example, the primary feature has less adoption (problem), and the goal is to increase the adoption of the new feature.

So basically, to begin with: “ What are you trying to solve”?

Hypothesis:

Every campaign or test is executed in anticipation of achieving a positive result. A predefined list of hypotheses to set the direction of campaigning is much needed. 

Isolate variables:

Within a single campaign, if there are multiple variables and changes, whether it can be a combination of image, image size, text, color, and a lot more – businesses cannot validate the success or failure of the campaign for a single variable. Hence, it is a good idea to isolate and implement one variable change for a single campaign. 

Primary and secondary metrics:

Every business has a north star metric or a primary metric when an A/B experiment goes live. Hence making a list of secondary metrics which might influence the primary vital metrics and evaluating them closely can display the visible correlation between different metrics. At times the secondary metrics will eventually make conclusive positive/negative triggers for the primary metric.

Protecting the health of the product:

At times, changes in the A/B experiment can lead to an unexpected decline in the product’s health score, such as reduced website performance speed, javascript errors, backend errors, etc. These ad-hoc issues can damage the seamless experience on the user front. Hence, protecting the product’s health with beta testing on staging environments is of great importance for any A/B campaign about to go live. 

The ideal time to run campaigns:

For any campaign, the “duration of evaluation” is quite essential. When the campaign should be delivered for 80% of the targeted audience, or at times, it could be based on the defined impact, like a 10% increase in conversions. Thus the duration of the campaign is highly dependent on reach, or impact, or both at times.

Whom to target:

While running A/B tests, like any other campaign, choosing the right set of audience for experimentation is essential. Precise audience targeting yields accurate results. Figuring out the right audience is again challenging but not impossible with the amount of data being collected. 

Interpretation:

When performing A/B testing for improved results, you are then shown two versions of the website with little design or content changes to two groups of randomly selected people. 

After a while, version A and version B have their conversion rates as the outputs. The version with a higher conversion rate wins the race. But are the results reliable and meaningful?

That’s when the statistical significance jumps in. Merely a mathematical concept but very helpful: the probability that the difference between the conversion rates of two web page variations is NOT the result of random chance. In other words, it measures the likelihood that an effect you have observed is due to a fundamental change in consumer behavior.

In most cases, the confidence level chosen for A/B testing is 95%. That means for every 20 sets of observations, 19 times the winner is indeed more effective than the other. 

If it seems too technical to start with, we got you. There are many ready-to-evaluate A/B test calculators over the web. All you have to do is input your experiment conditions and set the confidence intervals to 90%, 95%, or 99% based on your choice of accuracy. 

Wrapping up:

At times  A/B testing is as simple as changing the color of a CTA button and seeing which colored button leads to more conversions. It can be as complicated as displaying personalized contact forms for different segments of users based on their behavior. The complexity is dependent on the end goal, target audience, and critical KPI metrics involved. 

However, the success of this kind of test depends on the statistical significance and how the businesses perceive success. 

About Upshot.ai:

Upshot.ai is an omnichannel, user engagement, and gamification platform that helps digital product owners and marketers improve their product adoption and conversions. Fortune 1000 companies such as GE, UHG, Puma, Sony, ITC, and Tenet Healthcare are using Upshot.ai and observed a massive increase in product adoption and YoY increment in revenues.

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