What is A/B testing in Digital Marketing?

A/B testing, a method that is widely known for its revolutionary use of enhancing customer experiences that would end up with the better conversion. It is one of the best online promotional and marketing strategy tools for any of the business. A/B testing can be used to test anything from website’s heading to email newsletters or even campaign’s running with search engine ads and display advertising. In e-commerce, it can also be used to test the action buttons, checkout experiences, and overall user experiences. The results are the real-time data which can be used to compare the experiences and pick the best results to make the decision easy.

A well planned A/B testing can play a vital role in increasing the efficiency of the marketing efforts. Choosing the most effective method for an increased conversion in a promotion and implementing them makes the marketing efforts more profitable and successful.

The data-driven decisions are used considering many of the variables across the range of communication methods. Following the year 2000, Google has successfully started to use A/B testing to determine growing number of search results and analyses the best way to show them in the listing pages and the rest Is history!

How is A/B Testing used?

A/B testing helps to determine the little differences in each of the customer interactions in a marketing campaign that influence their behavior. The changes can be as small as the changes in the letters of a title in a newsletter or an email, the text structure in a banner or something can be as big as the image itself, call to action buttons or layout of a landing page. It is actually the idea to test two variation with a controlled group of customers and find out the successful one. It can be repeated again and again to improve the contents and improve marketing communication with customers.

The common practice of A/B testing which is proven to be useful:

E-Newsletters
Email marketing campaigns
Websites
Apps
Internet advertising (banner/PPC/AdWords

How does A/B testing work?

The first A/B testing of any business should come with identifying the key metrics of the business and how that business define its success factors of their marketing campaigns. It can be the number of clicks, sales, sign-ups, average time spends, downloads, and etc. This then is used to set up two or more variables with a controlled number of audiences to go through the variables. It is always recommended not to pick more than one metrics at a time or it’s always challenging to know the one that made the differences. To analyze the results of the best output, it should be tested simultaneously in similar variations and select the most successful variation for final use.

For instance, while testing web pages, the existing version shall be kept as the control and make the second version for the test, and split the traffic with a ratio or even you can divide the traffic equally. This is some of the highly technical tasks, and to ease the process, some of the tools are being used where all the process of creating variations, splitting traffic and measuring the results can be done for free, for example, Google Analytics Content Experiments. There are also widely used tools which are premium and specializes in conversion rate optimization which will run the A/B testing and will make a recommendation that will aid the total marketing efforts, some of the best tools around are Optimizely, Monetate, A/B tasty, Adobe Target, etc.

What to test with A/B testing?

Webpage or Landing Pages

If you are a Magazine your key metrics would be the number of sign-ups you will receive for promotional campaigns. Taking the different version of a promotion where your sign up page call to action button will be optimized and increase sign-ups. For instance, you might get the idea with a new slogan which would make it better from the existing one. Here, the new slogans would be the test (variation A) and the existing would be the control (Variation B). ¬ You can then drive traffic to them equally to both the variations for a period of time. After the test end, we can see the results and which drives more sign up. The best result would then be picked following the campaign or be the same as the earlier versions depending on the results.

Examples of the variables that can be tested

Subject titles and subtitles
Product descriptions
Text (length, style)
Offers
Price
Images
Call to action button (text, color, position)
Colour schemes
Forms (length, question)
Page layouts
Shipping/Return Policies

While running an A/B test, it is always necessary to keep the sample size statistically relevant, for instance, if you get 2-3 sign-ups in one day, then the result won’t be relevant due to the size, the greater the sample size, the more reliable the result. Also, the result will depend on the performance differences, for example from a campaign if we expect an increase in sign up of 5% from a blue button, we need to analyze the number changes to make the results relevant. When we test the results in tens of thousands, 5.6% is a significant change, but if we take a number that will do a test with 15-20 results, then the result won’t make any sense. For testing in low traffic, scientific results are very hard to achieve, in this cases, the repeat test can be carried to get an insight into the test and get the best conversion rate.

Newsletters and Emails

Newsletters and email marketing are one of the greatest marketing tools that can keep you on the edge over your competitors. Each small visuals and contents can make a huge difference in defining your success, from experimenting with titles which make the receiver to open the email or not, and lots of other titles, call to action buttons, colors, etc. can make the difference in click-through rate. Developing a successful test development plan and executing them can make your campaign lot more success with scientific results.

Insights

A/B testing does not only helps us to pick up the best performing variation for better conversion rate but also the data we get can be used to other areas of marketing efforts and make the decision easier. If we know a blue call to action button working best across other tested areas, we can implement the same things in other places that can make the results more optimized. And when we know what type of newsletters has been successful, we can design another campaign saving decision-making times over promotional materials.