Can Machine Learning Help Us Avoid Common Email Marketing Mistakes?

Machine learning is being used more extensively in email marketing. A couple months ago, Nathan Sykes of Curatti wrote an article on the benefits of machine learning and other big data tools for email marketing.

But how many solutions can machine learning really offer? That will depend largely on the way it is integrated into the email marketing funnel.

Did you know that email marketing is far more effective than social media marketing, especially with machine learning technology at its disposal? Email marketing has been around for some time. Despite that fact, not so many businesses are using it, which means that they are missing out on a lot. Email marketing has never ceased being useful. However, for any business to benefit, it has to employ the best email marketing practices, which involves using machine learning technology to improve your funnels. On top of that, here are some mistakes that should be avoided with the right big data techniques.

1. Not doing follow up on email deliverability

Analysing metrics on email deliverability is vital for a successful email marketing campaign. Email deliverability is basically linked to email validation. Which, as the name suggests, seeks to confirm if the emails in a list are valid. Why is it important? In your email marketing campaign, you want to reach as many prospects and customers as possible. Unfortunately, not all the emails that are sent reach the recipients: some are sent to the spam inbox.

The goal of checking email deliverability is to confirm that the emails that you are sending are not being rejected. If they are not reaching your audience, you will be able to establish why and make necessary amends.

Machine learning helps a lot in this area. Machine learning algorithms help you identify the types of users to focus on and edit your copy to make sure emails are ready.

2. Not evaluating your email marketing bounce rate

Big data will also help lower your bounce rate, which is very important. Bounce rate in email marketing will render your campaign ineffective. Why? It is simple: your customers do not get your message. It can be frustrating, especially if you have no clue what is going on behind the scenes. Bounce rate is a sensitive metric to track. Bounce rates mostly occur in events such as a customer no longer using the email address or it being inactive for a long time.  This is one of the biggest benefits of machine learning in email automation platforms.

By doing follow-ups, you will be able to diagnose such issues and come up with a solution to them. A bounce rate of 2 percent is acceptable. Anything beyond that should be a cause for alarm.

3. Sending unprofessional emails

This is simply to tell you that quality is essential. What does that mean? Typos, low-quality images, and other mistakes should be avoided. Remember that you are operating in a competitive market. The customers are receiving emails from other companies, and you should, therefore, put your best foot forward.

You should, therefore, take time to do well-written emails, source for quality images, and use relevant content. It is the only way that you will be able to stand out in the crowd.

4. Sending emails at the wrong time

Understanding your customer demographics is essential for running any successful digital marketing campaign. You may have well-written emails, but if you have no clue of what the customers want, you have no chance. For that reason, you need to keep a track on the time that your emails are opened. That should be the best time to send your emails. Observe the open email trend, and you will be able to establish the perfect time.

If you avoid the mistakes above, nothing will get in the way of you having a successful email marketing campaign.

Incorporate Machine Learning into Email Marketing for Best Results

Machine learning plays a very valuable role in email marketing these days. New big data algorithms can help you avoid some of the biggest email marketing mistakes. You just need to utilize it properly.