A/B or split testing has been the standard way to optimize marketing campaigns for years. Google first ran an A/B test in 2000 to identify the optimum number of searches to display on its result pages. Today A/B testing is common practice in many different digital marketing channels including display ads, landing pages, email marketing, and pretty much anywhere that copy, images, or placement can be adjusted. A basic example of A/B testing would be splitting visitors to a website into two groups (A and B) and showing each group a slightly different version of the homepage. Everything else might be the same on the page apart from the header image. Let's say group A sees an image of a group of smiling people and group B sees an image of a city skyline.

Marketing your business online is a costly business. Gone are the days where you could rely on organic reach. You have to pay to play the game. But getting your online advertising spend wrong could cost you a lot of money for little gain. There are a range of artificial intelligence (AI) tools designed to counter that. In this article, we're going to take a look at some of the AI tools that can help you manage your online advertising spend.

Artificial intelligence (AI) is clearly making its mark on retail marketing, and it's having the biggest impact in the digital world due to the inherently trackable and quantifiable nature of activity on websites, in digital purchasing and within user accounts. The volume and cleanliness of the e-commerce data make this a perfect environment for leveraging AI. That's why it's no surprise that Amazon is one of the paramount AI powerhouses in the world. The volume of data generated by logged-in user accounts on their website -- such as information on purchases, add to cart actions, clicks and searches -- is huge and allows them to tailor and customize their offerings and grow the business. Brick-and-mortar businesses don't have the same advantages.

User Experience (UX) designs, by and large, aims to make digital experiences smooth and pleasant. Everything from the interactions on a user interface to the colors and features used in the product impact conversions. But there always remains a gap in how efficient or intuitive these interactions ought to be and how effective they actually are in converting visitors. It is probably why no UX design is perfect. There will always remain a friction point in the journey that keeps users from staying engaged.

In last week's article, we discussed the benefits and drawbacks of split testing and the advantages of using AI-powered workflow optimization to achieve higher conversion. And to make it relevant I included an example email course, to show you how you can increase the open rate of your emails in drip campaigns. This article continues with one more use case where you can take a look at how to increase the click rates of your onboarding emails with machine learning. So let's move along the buyer journey from the consideration to the decision stage. Like in the case of the email course the purpose of an onboarding email sequence is education.

I think we can all agree on the fact that split testing is an effective method to find out what works best and get more out of your existing traffic. It's extremely useful since it can be applied to a number of different things: subject lines and content of emails, landing pages, home pages, creatives for ads and the list goes on. Also, you can find articles, case studies on split testing for almost anything, except drip campaigns. It's because experimenting with automated emails takes a lot of time and preparation. It is nearly impossible and too technical with most marketing automation tools.