4 Ways to Use Machine Learning in Marketing Automation
Applying machine learning in marketing automation is not rocket science. It has one and only one purpose: make marketers’ life easier. It helps you focus only the most important data, performs the must-have, but repetitive todos. Therefore your scope of duties will move to a more strategic position as a marketer. I’m about to I show you 4 examples when machine learning in marketing automation could truly help you.
What is machine learning?
Machine learning is a discipline that uses science, data and computer code and automatically predicts certain outcomes based on discovered patterns.
As a marketer, you probably use conditions such as “IF” and “AND” to “preprogram” and automate your emails and different actions like tagging or modifying custom values.
Machine learning helps you with repetitive processes and there will be a time when you won’t have to connect “different boxes” on your workflow editor to draw the buyer journeys of your prospects but ML will do it for you.
Machine learning in marketing helps marketers increase their productivity and the ROI of their campaigns.
Why should you use machine learning in marketing automation?
As we mentioned, you can use rules in order to make your MA system work. “The biggest problem is that these rules are hard-coded to specific user actions based on an aspirational understanding of what constitutes a good lead, versus based on what the data says.” — says Vik Singh at TechCrunch.
It means that you set up your workflows without a real understanding of the different patterns and real buyer journeys. This way you will never know whether you could build better-performing workflows or not.
Machine learning is a solution to this challenge and will change the way we use our MA systems.
Now take a look at the 4 applications of machine learning in marketing automation!
Next generation split testing
One of the most important things marketers do is creating assumptions, testing, evaluating and starting it over and over again.
But what are the problems with it?
- You need to perform a high volume of experiments at a time. (For example: testing email subject lines, CTAs or copies for every newsletter or drip campaign you send out on a daily or weekly basis.)
- You need a high volume of traffic to make sure your result is significant.
The solution for that difficulty is implementing multi-armed bandit algorithms that will continuously play around with the weights of your email variations as more and more leads go through your drip campaigns.
This methodology will reduce the required volume by third and the machine learning algorithms will do everything automatically for you.
Below here you can see a short explainer video how it works in reality (this solution combines A/B testing and sending frequency optimization that is described in the next section).
Sending frequency optimization
Lead nurturing is one of the most important things you can do to enhance the ROI of your campaigns. As you know, it’s important to send the right message, to the right person at the right time.
Therefore you need to find out somehow how frequently you should send your follow-up emails in your drip campaigns. Unfortunately, you can’t do that manually because you should play with the sending frequency, learn from it and do your tests continuously.
Machine learning will handle it automatically for you. It experiments in a given time-period.
For example, you say that you want to send out 3 follow-up emails after form submission in 10 days. As your leads go through your drip campaign, the algorithms will learn how often it should send your emails for the best performance.
Predictive analytics are important because it gives you the chance to see the future. This way you will not just react to the actual happenings but will be proactive by preventing possible negative outcomes.
You can see the churn rate in the near future and see which client is at risk of leaving you. This way you have the chance to contact them or offer something that will make them stay.
You can see the trend of your growth: for example by this time next year how much your profit will be.
You can distribute your marketing budget not just based on historical data across channels but with predictive budget planning. Where should you allocate more money for higher ROI.
Segmenting your database is very efficient when it comes to personalization and powerful messaging. Manual segmentation processes are working although today’s marketing is more about micro-segments and unique buyers’ journeys than simple classifications of demographics.
Machine learning algorithms identify similar user behaviors and recognize patterns that can separate different customer groups. So you just need to describe your service and it will automatically find the best segments for you with good descriptions for it.
Do you know any other ways to use machine learning in marketing automation? Share it in a comment.