Business executives are increasingly interested in machine learning applications for their enterprises as ML tools and available expertise demonstrate real-world business results.
For the uninitiated, Machine Learning was defined by Stanford University as “the science of getting computers to act without being explicitly programmed.” (You can see a deeper discussion of how machine learning works here.)
Machine Learning is now responsible for some of the most significant advancements in business technology. The nascent discipline that helped train computers to understand voice recognition only a couple of decades ago has grown to become the underpinnings of a variety of commercialized solutions today.
ML has now gone mainstream as a business solutions strategy. Experts, like the machine learning developers at Bitstrapped, are readily available to deploy a business idea you may have into real-world machine learning applications that solve business problems and, in many cases, can directly contribute to a business’s bottom line.
To that end, here is a list of 10 machine learning applications that will help you understand what real-world machine learning applications can do for your business today. The list will also help you to see what is possible for your business with machine learning technology. Below the list is an explanation of each application and how it is used in a business production environment.
Chatbots are perhaps the most widely used machine learning applications in business. They allow people to talk with machines, and in turn, the machines can notify humans or provide relevant information to respond to human requests. The first chatbots used scripted rules that formulated responses based on keywords. Later generations used machine learning and natural language processing, or NLP, to improve chatbots and give them the ability to be more interactive and occur like human customer service representatives. At home, digital assistants like Amazon Alexa, Apple Siri, and Google Assistant all use machine learning algorithms to communicate with humans.
Decision support applications powered by machine learning can help businesses parse the big data they have collected from operational activities into valuable insights. Business leaders can use this information to make strategic decisions. Machine learning applications in this category do not make final decisions; they help humans become more efficient and effective at their jobs.
ML decision support systems can help managers identify and act on trends in business. They can detect problems and recommend actionable solutions. In healthcare, ML clinical decision support tools can help doctors make diagnoses and recommend courses of treatment. This can help medical workers be more efficient and improve patient outcomes. In agriculture, ML decision support tools can use data about farm resources, weather, energy, and water consumption to provide farmers with insights to help them with crop management.
Machine learning can power customer recommendation engines that make customer experience better. They can help guide choices and personalize customer interactions on a platform. ML uses data gathered from customer activity, like previous purchases, and parses against inventory levels, pricing, customer trends, and behaviors such as buying habits to recommend purchases. Walmart and Amazon already use this technology to enhance the customer shopping experience. Netflix uses similar technology to make content recommendations to subscribers. And YouTube helps its users find relevant videos among the hundreds of millions of options on the platform.
Some companies implement machine learning to run daily repetitive business activities that a minimally skilled human would otherwise handle. You might see this in manufacturing, finance, and even software development and testing. It is used to detect and reduce errors and monitor efficiency and predict repair and maintenance needs for production equipment.
Machine learning can be used to monitor customer relationships and levels of customer satisfaction. It can recognize if a customer is exhibiting behaviors that may lead to the cancellation of services. It can also make recommendations to mitigate dissatisfaction and prevent customer churn. Machine learning applications can also analyze customer behavior data to surface why customers cancel business relationships. This has high utility at service-based companies like Internet providers, cable operators, software-as-a-service providers, music streaming platforms, and any other business that relies on subscription revenue.
ML applications can ingest historical pricing data against business variables (seasonality, time of day trends, etc.) to predict demand, set prices dynamically, and maximize revenue. Think of surge pricing in rideshare services like Lyft and Uber in North America and Grab in Asia. The technology can also lower prices when demand is reduced to help move products that have not sold. Airlines can also use the technology to maximize ticket prices and profitability as demand fluctuates for seats on its aircraft.
Machine learning applications can be used to predict inventory needs and to perform customer segmentation. Retailers can use ML applications to determine what inventory is needed based on demand at each location. It can use datasets such as local demographics, seasonal trends, customer behavior, and other location-specific data to predict demand. For example, in August, it can be used to determine what school supply-related inventory needs to be available locally at each of its stores based on the dates that students are returning to school in a given postal code. It would be able to use demographic information to evaluate student population levels.
Machine learning can be a valuable tool for detecting unusual activities or behaviour outside set tolerances. This is highly useful in detecting fraudulent activity at credit card companies. It is also used in the gambling industry and for subscription access (logging into accounts). The technology has been in use in the finance sector for years. It is why if you travel outside your usual geographic area and use your credit card, that you might get a blocked transaction requiring a call to customer service to let them know you are on a trip.In this case, a machine learning algorithm has detected the anomalous behavior and has flagged and often blocked the transaction automatically.
Businesses also use machine learning and associated technologies to manage images acquired by artificial vision systems. This application has great utility for business when a massive volume of images needs to be processed in real-time and categorized or segmented for instant action or further analysis by humans. Applications include security or automated navigation applications (autonomous and semi-autonomous cars) and even workplace safety monitoring.
Machine learning can be used in an automated toolset that automatically extracts defined structured data from forms or documents or other structured or semi-structured data. This technology can be applied to forms, contracts, invoices, and the like. It eliminates the need for a human to manually review large volumes of documents and manually extract data for further usage in other applications. See our case study on how we used ML in an application for a tax preparation firm
To discuss how machine learning applications can be used in your business, contact us at Bitstrapped for a free discovery call: Click here.
Machine learning is an exciting subspecialty of computer science and as with any technology topic, it has a lot of terms that need to be defined to better understand the field. Here are some of the topic terms you may encounter.
Learn the basics of machine learning. What is it? How does it work? And what is it used for?