Gartner predicts that by 2020, consumers will manage 85% of their relationship with a brand without speaking to a single person. This presents both a challenge and an opportunity for businesses. 

After all, how do you provide a unique and personalized customer experience when you rarely interact with your customers? How do you make sure that your messaging resonates with them across a variety of platforms and devices? The answer is marketing automation.

Marketing automation gives a business maximum reach, regardless of the size of that company or its budget. After all, when the standard in retail is a company like Amazon, smaller companies need to find a way to utilize more resources with fewer costs.

And increasingly, companies are learning that to stay competitive, they also need to utilize machine learning.

What is Machine Learning?

Machine learning and AI are often spoken of interchangeably but they aren’t the same thing. Rather, machine learning is a type of AI that uses data to make inferences. 

Marketing technology platforms continue to become more efficient and allow companies to analyze large amounts of data. Machine learning combines this data with science and coding make to predictions based on identified patterns.

These patterns would be difficult for an individual to identify, which is what makes them so valuable. These patterns can predict things like when you’re about to lose a customer or what a customer is likely to purchase next. However, machine learning only works as well as the person managing the data. 

Machine learning may sound like a technology buzzword but it is quickly becoming one of the biggest trends in marketing automation. That’s because the two go hand-in-hand and one really compliments the other. Machine learning allows companies to increase their revenue and optimize the customer experience.

Problems Machine Learning Solves in Marketing Automation

Marketing is quickly becoming a data-driven field. Marketers rely on these insights and look to eliminate as much guesswork as possible. Listed below are a couple ways machine learning can take the guesswork out of your marketing efforts.

Customer Churn

Increasingly, marketers are using machine learning to understand and identify problems before they occur. And one of the biggest problems that machine learning can help solve is customer churn.

Customer churn refers to the number of customers that end their relationship with your company. This rate is calculated by the percentage of customers who leave during a specified period of time.

And it’s a big problem for businesses because if your customer churn rate is too high, your business can’t grow. Your customer churn rate is also a good indicator of how satisfied customers are with your product or service.

Fortunately, there are machine learning models which can actually predict customer churn before it happens. By analyzing a number of different factors, companies gain the insights they need to minimize customer churn.  

Lead Generation

How much confidence do you have in your lead scoring abilities? According to the survey referenced below, most marketers don’t have a high degree of confidence in this area.

Lead scoring is a notoriously difficult thing to evaluate but it’s also crucial. Improving your lead scoring abilities will improve your lead generation strategies. And of course, this will lead to new customers.

There are a number of factors that go into this calculation, including website visits, email open rates, social media interaction, and more. Machine learning can help you qualify prospects and create more accurate customer profiles.

3 Ways Machine Learning Improves Marketing Automation

Being able to anticipate customer behavior, stay innovative, and provide a unique experience is crucial in today’s market. Here are three ways machine learning can do just that and improve your marketing automation.

1. Dynamic Pricing Strategies

Machine learning can help businesses identify dynamic pricing strategies. Dynamic pricing strategies will disrupt the online retail industry in 2019. This is when businesses offer flexible prices on their products based on customer demand and market conditions.

And while it’s a newer concept to the retail industry, dynamic pricing has been around for a long time. It’s frequently used in both the travel and hospitality industries.

For instance, if you were to purchase a plane ticket, the price would depend on a number of different factors. It would depend on how far in advance you purchase the ticket, what day of the week you’ll be traveling on, the time of day when you’ll be flying, and more. 

However, you need a lot of information, including customer input, to make this work. Increasingly, businesses have the data they need and machine learning can analyze this data and make it easier for businesses to implement these pricing strategies.

2. Customer Service and Support

75% of companies that use machine learning enhance customer satisfaction by more than 10%. Thanks to machine learning, businesses can provide 24-hour customer support in the form of chatbots.

And machine learning can help you create a personalized shopping experience for customers. One of the best ways to do this is by offering personalized product recommendations.

For instance, Netflix saves $1 billion a year in lost revenue by using an algorithm to offer personalized movie recommendations to customers.

The company did this after learning that the average customer gives up after spending 90 seconds searching for a movie. According to the company, 70% of customer selections are based on recommendations.

3. Customer Insights

And finally, one of the biggest mistakes most businesses make is treating its customers like they are all the same. Segmenting your customers is crucial if you want to improve engagement and increase the lifetime value of that customer. 

Machine learning gives you valuable insights into your customers, their behavior, and the experience they are looking for. And you can use this data to segment marketing campaigns and deliver more specific results.

This eliminates the guesswork in customer segmentation and can also help you identify new opportunities in the market. This data may reveal new products you can develop to cater to a new group of customers.

This allows you to not only serve the customers you have but to continue growing and acting on new business opportunities.