One of the greatest advantages of digital marketing is agility. You can get access to real-time data that tracks the impact of your campaign as it runs, giving you immediate feedback on what’s working, and what needs to change.

Digital marketing enables you to adapt and improve campaigns in real-time, even if they’re still live. For example, in a Facebook ad campaign, if your audience isn’t engaging, you can quickly switch up your pricing, creative content, or target audience, and keep doing so until the campaign has good traction. This means you don’t have to wait for the campaign to finish, or for your marketing budget to drain, before making improvements.

Essentially, digital marketing allows you to adopt a test-and-learn mindset, where you continue to refine, and experiment with your campaigns.

It also presents the opportunity to carry out something called A/B testing.
A/B testing is where you test two slightly different versions of the same campaign and then compare their results. You can then use these insights to modify and finalize the campaign for future success.

When you’re carrying out an A/B test, it’s essential to keep everything the same, except for the one variable you’re testing, like the call to action in an email.

Start by testing variables that are the easiest to change, and the most likely to have a meaningful impact on results.

You should try to test on a significant sample size, so that you can trust your results are accurate and statistically relevant.

When it comes to optimizing digital marketing campaigns, many businesses are also turning to artificial intelligence, or ‘AI’. This technology can help them generate content, analyze campaign performance and make improvements in real-time. AI can also test multiple variants at the same time - and by leveraging machine learning, you can then optimize content based on the results of these tests.

Machine learning is a powerful subset of AI, which identifies patterns and correlations within datasets to make a range of predictions about consumer and customer behavior.

As an example, you could use a machine learning tool to test several variations of the same web page, at the same time. This tool could then detect which of these variations is the most effective and display that version to future website visitors.

The key thing to remember is that the quality of AI’s performance relies on the data you feed it. This serves as training material for a machine learning model, so good quality data - and lots of it - naturally leads to better results.

Optimizing Your Campaigns