5 Revenue Opportunities Machine Learning Is Unlocking for 2022

In 1986, Tom M. Mitchell published this definition in his ground-breaking book Machine Learning:

“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”

Since then, the idea of machine learning (ML) has gone from a wordy—and rather mathematical —definition to a widespread practice, with companies increasingly putting the technology to work to tap new revenue streams.

ML is the application of applying algorithms to parse and organize data, learn, and then make predictions about possible results. This can mean anything from speech recognition to aid in customer services, to more complicated tasks like image recognition in video or predictive analytics to help businesses make forward-looking decisions.

As ML has matured, so have the opportunities for businesses to increase revenue. Here are five of the areas we see ML creating opportunities in 2022 and beyond.

1. Increased customer acquisition

Through ML, companies can gain instant access to crucial information about their customers. Information like interests, general location, and behaviors that can be used for better targeting and more successful campaigns.

Example: A retailer is launching a campaign around a specific holiday. With ML, that retailer can tailor every aspect of the campaign to target only those customers identified as, say, major holiday gift-givers or past visitors to their store around that particular holiday.

Not only does this reduce the overall costs of the campaign, but it also narrows the scope of potential customers so the campaign can be better personalized for those likely to spend money around the specific holiday.

2. Better product recommendations

In e-commerce, data is king. With ML, companies can assess what features or functionalities will best retain users and increase revenue. They can also utilize smart data analytics to optimize inventory forecasting and test the waters on pricing strategies.

Example: A couple years ago, Forbes published an article on how jewelry.com was able to use ML to increase revenue by 39% through homepage recommendation personalization.

The secret, as noted in the article, was for the company to employ ML to automatically pinpoint the most effective promotional strategy for every user, whether they already had a history of interacting with the site or were new arrivals. The end result was a homepage that would look completely different for those already familiar with jewelry.com, with recommendations that were likely to pique their interest.

3. Business process automation

File this opportunity under cost savings and efficiency. By using ML, businesses of all sizes are able to automate rote tasks in order to free up highly skilled employees to focus on product innovation and service improvement. 

Example: RPA tools can increase worker performance, reduce human error, and accelerate response times. 

This increases efficiencies in the company and it frees up valuable time and resources to focus on providing new and innovative products for customers.

4. Predictive maintenance

Even the most sophisticated machines require maintenance in order to function properly. With ML forecasting models, companies are better able to predict when components of machines are likely to wear down and potentially fail, then schedule preemptive maintenance in order to avoid major costs from breakdowns.

Example: Agricultural manufacturer John Deere has a program around predictive maintenance, with its tractors and other large farming equipment providing ongoing data throughout their lifetimes in order to highlight when hardware may need some attention.

5. Manufacturing automation

The increasing reliance on robotics to handle assembly at major factories requires sophisticated AI training. This has led some companies to create virtual factories where ML models can be trained before being applied to the robots in real world locations.

Example: BMW has teamed up with NVIDIA to create a factory of the future that blends reality and virtual reality in order to assist in the design and reconfiguration of various automobile models. 

This setup allows BMW’s global teams to collaborate in real-time, as well preview changes in design without the need for expensive modeling and testing.

Looking to learn more about ML? Check out our AI and ML solutions. 

Note: This post was originally published in 2019 and has been updated with new content.