While machine learning may seem like a futuristic fantasy where computers teach themselves, in reality it is a here-and-now application that analyzes huge amounts of data in order to allow businesses to identify profit opportunities and risks.
More and more businesses are leveraging machine learning, according to a Forbes article. Citing a study from O’Reilly Media, the article says 49% of organizations are exploring machine learning, while 51% claim to be early adopters (36%) or sophisticated users (15%). The study also lists factors that make machine learning adoption a huge challenge for businesses, including a shortage of qualified professionals and the lack of access to real-time data.
What Is Machine Learning?
Here’s what MIT Technology Review says about machine learning:
- It utilizes algorithms to establish patterns and trends in huge amounts of data which may include anything from numbers and words to images and clicks.
- It is the process behind many services we use today such as recommendation systems in Netflix and Amazon Prime, search engines like Google and Bing, social media services like Facebook and Twitter, and voice assistants like Siri and Alexa.
- Those services use the data they collect and machine learning to make an educated guess on what you want or might expect next.
The process is quite basic, says an article in MIT Technology Review: “… find the pattern, apply the pattern. But it pretty much runs the world.”
What Businesses Need to Know
Many of today’s most successful companies are using machine learning to better understand their customers. Oracle AI and Data Science Blog lists some of the more common applications:
- Lifetime value modeling is used to identify, profile and retain a company’s most valuable customers and, at the same time, market to new customers who match that profile.
- Churn modeling identifies customers who are likely to quit doing business with a company and why. The results can be used in a retention program for high-value customers by developing discount offers and email campaigns, for example.
- Dynamic pricing allows a business to price products based on factors such as level of interest of targeted customers and demand. It also considers if a customer has participated in a marketing campaign.
- Customer segmentation groups customers according to demographics, browsing behavior and affinity, connecting these traits to purchase behavior to develop highly personalized marketing campaigns.
- Recommendation engines sift through data to determine how likely a customer is to purchase an item or service and then suggest those items.
The ability of machine learning algorithms to discover insights into customer behavior leads to other benefits for businesses as well, says an article from Flatworld Solutions.
For example, manufacturing firms can use machine learning to discover meaningful patterns in their factory data and practice what is called predictive maintenance, which can lead to more efficient maintenance practices and risk reduction.
Machine learning can also be used to detect spam and phishing messages, as well as improve cybersecurity.
In addition, machine learning can improve customer satisfaction by matching customers to the service representative most suited to their needs. This decreases the time and cost of managing customer relationships and enables a business to use a predictive algorithm to suggest the right products to the right customers.
Finally, healthcare organizations can use machine learning to improve patient care and decrease healthcare costs. Using patient records and datasets, doctors can make better diagnoses, predict re-admissions, recommend the correct medicine and identify high-risk patients.
Businesses will increasingly use machine learning for marketing analysis and planning, strategic financial planning, and other functions. It will require a new generation of business leaders who are not only well-versed in traditional business skills but are also open to a landscape facing rapid change from machine learning and big data.