When you think about the future of the business world, what comes to mind? Chances are, you may think of artificial intelligence running much of the busy work that companies have. It’s a concept that’s not too far off, but it may be machine learning which dominates the business landscape instead of AI. While similar, machine learning and artificial intelligence are still quite different with each other, with machine learning involving machines learning specific functions over time. The algorithms used are designed to improve with each successive iteration, in a sense getting smarter as more time passes. We don’t have to stretch our minds too far to see the future in action. In fact, real world applications of machine learning solutions are already prevalent in business.
Why is Machine Learning Important?
Businesses have always sought ways to make their operations more efficient and effective, thereby cutting costs and increasing profits. Machine learning is simply the latest technological tool to be used to those ends. This is compounded by how important collecting and analyzing data has become in today’s business world. To stay competitive, companies have needed to use the best machine learning software to properly utilize that data. With the many different types of machine learning out there, nearly every type of industry can benefit from this technology.
Machine Learning Examples in Real Life
It’s easy to say that companies should adopt machine learning solutions for their businesses, but some might not understand what machine learning can actually do. So it’s important to look at each machine learning application for the real world in order to see how best to apply it for your own operations. Many of these solutions have been in place at companies for years now.
One of the most common examples is the ability for machine learning algorithms to identify market trends and perform market analysis. This has been especially helpful for companies in the financial industry, but since every business needs to be on top of market news, it can be used almost anywhere. Machine learning works in this category by analyzing vast amounts of data to identify specific patterns that can indicate what markets will do in the near future. Some of these signals can be slight, possibly so small that a human observer would miss them. With enough data points, however, machine learning can pinpoint them and give companies valuable information on what the market will do in the days, weeks, months, and even years ahead. Best of all, this type of analysis can be done in real time. With this information at hand, companies will always be prepared for what’s to come.
Of course, one of the keys to company success stems from providing the customer with a high quality experience. Machine learning can help in this regard as well. One area in particular comes from giving customers accurate product recommendations. You’ve likely come across this feature when shopping on Amazon or watching movies on Netflix. Machine learning helps in this process by taking a customer’s past actions and behaviors and determining what kind of products they would be most interested in based on this data. This is especially helpful for ecommerce companies, who rely on repeat business and customers purchasing multiple items. If a customer knows that a company has a good idea of what they like, the more likely they are to stick around.
Improving the customer experience with machine learning can go even further than that. With machine learning algorithms, businesses can improve customer retention. Companies are collecting a great deal of data on their customers, and it’s through analyzing customer behavior that machine learning can pick up on certain “warning signs” that a customer might soon leave. If companies know a customer is on the verge of leaving, they can take further steps to keep them around. Even in this approach can machine learning make a difference by basically identifying what the next best course of action is in retaining the customer.
While much of the focus on machine learning is on improving customer relations, there are aspects and work processes within the organization that can be improved as well. Take hiring practices, for example. A typical job opening at a large organization could get dozens, even hundreds of applicants, and sifting through them all can consume a lot of time and resources. That’s where machine learning can step in and direct companies to the best applicants. Machine learning algorithms, based upon past and current successful employee data, can determine which of the applicants would have the best chances at being successful at the job. This can narrow down the field to something much more manageable. Machine learning can also eliminate potential biases that can come up in hiring practices, which would lead to a much fairer process on the whole.
Machine learning can also play a role in helping organizations improve their overall logistics. The supply chain can get quite complicated, especially when a business doesn’t have control over every step of the process. If something goes wrong, it can cripple a company temporarily. Machine learning algorithms in this case can be used to predict when things might go wrong, such as an accident at a factory. If something unfortunate does happen, machine learning can also be applied to finding solutions to mitigate the damage and keep businesses up and running despite the setbacks.
Medical and insurance companies are seeing the benefits of utilizing machine learning in their practices. This can also benefit the health of patients and customers through the use of health monitoring. Through various wearable devices, organizations can now collect data on a person’s health, anything from their heart rate to how many steps they walk each day. Based off of this information, the health industry can get a better understanding of how healthy someone is. Machine learning algorithms can even detect if future health problems may arise or predict if something bad might happen soon.
Another machine learning application in the financial world includes risk assessment, an especially important area when it comes to investments. By analyzing market data, machine learning algorithms can get a good idea of what moves will be the most likely to see a return on investment. At the same time, they can help companies steer clear of investments that carry too much risk, even if on the surface they seem safe and secure.
The Future of Machine Learning
As impressive as the capabilities of machine learning are today, the possibilities of what it can do in the future are making many of the top machine learning companies excited. Security is one area that holds a lot of promise. While strides have been made in things such as spam reduction, realm time machine learning advances make improvements to cyber security likely. In this way, potential malware attacks can be sniffed out and taken care of before they have the chance to do widespread damage.
Expect further advances in image and voice recognition, which is already seeing some impressive leaps thanks to the proliferation of virtual assistants. It could be that one day soon, nearly every device we have will respond with impressive accuracy to voice prompts.
Machine learning in general will also get better over time, as is its nature. Many of the examples given above will only become more efficient as machine learning algorithms become more ingrained in business practices. While some machine learning problems still need work, these real life examples show that the promise of machine learning isn’t all hype — it stands to completely transform businesses.