A/B Testing Guidelines for Data Scientists

A/B Testing is a widely acclaimed method in marketing. It consists of comparing two versions of an experiment or an advertising campaign in order to determine the most effective version according to a previously defined objective.

A/B Testing or split testing is a highly recommended method in digital marketing to test a web page or a mobile application with a sample of Internet users.

The operation consists of offering the same sample two versions of a website, for example. Half of the visitors will consult page A and the other half page B. The analysis of the feedback will make it possible to identify the page which received the favor of the testers according to a predefined indicator (traffic or sale for example). In absolute terms, split testing never stops once the “winning” page has been identified, it will have to be put in competition with another to strive for the best possible return on investment.

Web behemoths such as Google, Cdiscount, Ebay or even Amazon are mobilizing entire teams for split testing in order to be part of a process of continuous optimization of their platforms for the sale of products, lead generation or even user experience.

How create an A/B Test?

To avoid going off track and get started with A/B Testing with confidence, we often employ the following pipeline:

Collect data: It is recommended to rely on statistical analysis of data to determine high traffic areas. This allows for faster data collection

Identify the objectives: generate leads, traffic? The objectives of a campaign are the parameters that will serve as indicators to decide between the two versions tested.

Generate the hypothesis: Once the objective is determined, it is time to generate test ideas and hypotheses. Brainstorming usually yields good results.

Create variations: the desired changes will be made to an element of the site or mobile application. Example: changing the color of a button, swapping the order of elements on the page, hiding navigation elements, etc…

Run the experiment: At this point, users will be randomly assigned to control or vary your experiment. Their interaction with each experiment will have to be measured.

Analyze the results: a threshold of users or a period of time will mark the end of the experiment. Now is the time to analyze the results. A/B testing software will present the data of the experiment highlighting the statistically significant differences. If the experiment generates an unusable result, new hypotheses will have to be generated to be tested.

I know it seems too much steps, but we need to be certain that these tests are somewhat relevant to the stakeholders and for the business in general.

Stay with me!

What are the advantages of creating an A/B Testing?

A/B testing is a simple yet powerful testing method that has proven itself. It allows in particular to:

1 - Build hypotheses and better understand how certain elements can influence a user’s behavior.

2 - Continuously improve a given experience by improving a pre-determined objective over time.

3 - Identify changes that affect visitor behavior. It is possible, in this sense, to combine the effect of multiple winning changes from experiences to strive for perfection.

What can we test?

In a marketing, we can test almost everything: titles, calls to action, body font, images, etc. Everything that can be modified is testable! But that doesn’t mean you have to spend months testing every element. Rather, it is recommended to focus on the parts that are most likely to have a significant impact.

On websites, this often includes: the headline, your call to action, graphics, sales sheet, or product description.

In an email, it is a question of including the same elements. In an ad, especially a text ad (like a search ad), there are fewer things to edit. It is, however, necessary to test the main title or the offer itself. The subject of the email is also one of the most tested elements in view of its impact on the opening rate.

You can also test the elements in relation to each other. For example, we can test newsletter A with landing page B, then newsletter B with landing page A, etc.

A/B testing is an effective way to gauge your audience’s reaction to a design or content idea because it doesn’t disrupt your users’ experience or require you to send them unwelcome surveys.

Just try implementing in your business and let the results speak for themselves.

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