10 common uses for machine learning applications in business
Machine learning has moved from the stuff of science fiction to a staple of modern business, as organizations across nearly every industry vertical implement ML technologies.
Doctors are using machine learning to more accurately diagnosis and treat their patients, retailers are using ML to get the right merchandise to the right stores at the right time, and researchers are utilizing the technology to develop effective new medicines.
That is just a sliver of the use cases emerging, as all sectors — from energy and utilities, to travel and hospitality, to manufacturing to logistics — and the various functions within any given organization increasingly put machine learning to work.
Machine learning is a subset of artificial intelligence, where computers use algorithms to learn from data, allowing the machines to identify patterns — a capability that organizations can put to use in multiple ways.
Experts said machine learning enables organizations to perform tasks on a scale and scope previously impossible to achieve. As a result, it speeds up the pace of work, reduces errors and improves accuracy, thereby aiding employees and customers alike. Moreover, innovation-oriented organizations are finding ways to harness machine learning to not just drive efficiencies and improvements but to fuel new business opportunities that can differentiate their companies in the marketplace.
“Machine learning is improving almost any function and process automation by enabling operational adaptation based on changing conditions,” said Bruce Guptill, chief strategist at Addressable Markets, and a member of The Analyst Syndicate community of independent analysts.
Here are 10 applications of machine learning that are being used to solve business problems and deliver tangible business benefits:
1. Real-time chatbot agents
One of the earliest forms of automation are chatbots, which have bridged the communication gap between people and technology by allowing people to essentially converse with machines that can then take actions based on the requests or requirements voiced by humans. Early generations of chatbots followed scripted rules that told the bots what actions to take based on keywords.
However, machine learning and natural language processing, or NLP, another member of the AI technology family, enable chatbots to be more interactive and more productive. These newer chatbots better respond to user’s needs and converse increasingly more like real humans. “Digital assistants such as Siri, Google Assistant and Alexa, are based on machine learning algorithms, and this technology may find its ways in new customer service and engagement platforms that replace traditional chatbots,” said Lian Jye Su, a principal analyst at ABI Research.
Chatbots are the among the most widely used machine learning applications in business. A few examples of company chatbots that have won kudos include the following:
- Watson Assistant, touted by its IBM for providing “fast, straightforward answers,” is programmed to know when it needs to ask for clarity and when to triage the request to a human being.
- The music streaming platform’s bot for Facebook Messenger lets users listen, search and share music and get recommendations.
- Riders request service via chat platforms or by voice and are sent images of the driver’s license plate and car model to spot their ride.
2. Decision support
Decision support is another area where machine learning can help businesses turn the plethora of data they have into actionable insights that deliver value. Here, algorithms trained on historical data and any other relevant data sets can analyze information and run through multiple possible scenarios at a scale and speed impossible for humans to make recommendations on the best course of action to take.
“It doesn’t replace people, but rather it helps people do things better; it can make people much more effective,” said Dan Miklovic, founder and principal analyst at Lean Manufacturing Research LLC, and a member of The Analyst Syndicate.
Here are some examples of how decision support systems are used in various industry sectors:
- In the healthcare industry, clinical decision support tools that incorporate machine learning guide clinicians on diagnoses and treatment options, improving caregiver efficiency and patient outcomes.
- In agriculture, machine learning-enabled decision support tools incorporate data on climate, energy, water, resources and other factors to help farmers make decisions on crop management.
- In business, decision support systems help management anticipate trends, identify problems and speed up decisions. Information is presented via executive dashboards in the form of charts and other graphics.
3. Customer recommendation engines
Machine learning powers the customer recommendation engines designed to enhance the customer experience and provide personalized experiences. In this use case, algorithms process data points about an individual customer, such as the customer’s past purchases, as well as other data sets such as a company’s current inventory, demographic trends and other customers’ buying histories to determine what products and services to recommend to each individual customer.
Here are a few examples of companies whose business models rely on recommendation engines:
- Big e-commerce companies like Amazon and Walmart use recommendation engines to personalize and expedite the shopping experience.
- Another well-known deployer of this machine learning application is Netflix, the streaming entertainment service, which uses a customer’s viewing history, the viewing history of customers with similar entertainment interests, information about individual shows and other data points to deliver personalized recommendations to its customers.
- Online video platform YouTube uses recommendation engine technology to help users quickly find videos that fit their tastes.
4. Customer churn modeling
Another way enterprises use AI and machine learning is to anticipate when a customer relationship is beginning to sour and to find ways to fix it. In this way, the new ML capabilities help companies deal with one of the oldest historical business problems: customer churn.
Here, algorithms pinpoint patterns in huge volumes of historical, demographic and sales data to identify and understand why a company loses customers. The company can then utilize machine learning capabilities to analyze behaviors among existing customers to alert it to which customers are at risk of taking their business elsewhere, identify the reasons why those customers are leaving and then determine what steps the company should take to retain them.
Churn rate is a key performance indicator for any business, but it is especially significant for subscription-based and services companies. Examples of companies that use customer churn modeling include the following:
- media companies such as The New York Times, Bloomberg News and The Wall Street Journal;
- music and movie streaming companies such as Netflix, Amazon, HBO and Spotify;
- software-as-a-service companies like Salesforce (CRM software), Adobe (multimedia, marketing software) and
- major telecom companies.
5. Dynamic pricing tactics
Companies can mine their historical pricing data along with data sets on a host of other variables to understand how certain dynamics — from time of day to weather to the seasons — impact demand for goods and services. Machine learning algorithms can learn from that information and combine that insight with additional market and consumer data to help companies dynamically price their goods based on those vast and numerous variables — a strategy that ultimately helps companies maximize revenue.
The most visible example of dynamic pricing (which is sometimes called demand pricing) happens in the transportation industry:
think surge pricing at Uber when conditions push up the number of people seeking rides all at once or sky-high prices for airline tickets during school vacation weeks.
6. Market research and customer segmentation
Machine learning doesn’t just help companies set prices; it also helps companies deliver the right products and services to the right areas at the right time through predictive inventory planning and customer segmentation. Retailers, for example, use machine learning to predict what inventory will sell best in which of its stores based on the seasonal factors impacting a particular store, the demographics of that region and other data points — such as what’s trending on social media, said Adnan Masood who as chief architect at UST Global specializes in AI and machine learning.
“Think of it as a recommendation engine built for retail,” he added.
Similarly, companies can use machine learning to better understand specific segments within their overall customer base; retailers, for instance, use the technology to gain insights into the buying patterns of specific groups of shoppers — whether a group based on similar ages or incomes or education levels, etc. — so they can better target their needs, such as stocking stores with the merchandise that the identified segment is most likely to want.
Who uses it? Everyone from Starbucks to the insurance giants.
7. Fraud detection
Machine learning’s capacity to understand patterns — and to instantly see anomalies that fall outside those patterns — makes it a valuable tool for detecting fraudulent activity. In fact, financial institutions have been using machine learning in this area successfully for years.
This is how it works: Data scientists use machine learning to understand an individual customer’s typical behavior, such as when and where the customer uses a credit card. Machine learning can take that information, as well as other data sets, to accurately determine in mere milliseconds which transactions fall within the normal range and are therefore legitimate versus which transactions are outside expected norms and therefore are likely fraudulent.
Applications of machine learning to detect fraud across industries include the following:
- financial services
8. Image classification and image recognition
Organizations are also turning to machine learning, deep learning and neural networks (sets of algorithms designed to recognize patterns) to help them make sense out of images. This machine learning technology has wide application, from Facebook’s desire to tag photos posted on its site, to security teams’ drive to identify criminal behavior in real time, to automated cars’ need to see the road.
Retailers also have a number of applications for image classification and image recognition, including the following:
- equipping robots with computer vision and machine learning to scan shelves to determine what items are low or out of stock or misplaced;
- using image recognition to ensure all items are removed from shopping carts and scanned for purchase, thereby limiting unintentional loss of sales; and
- combating unsafe conditions by analyzing visuals to identify suspicious activities, such as shoplifting, and to detect workplace safety violations, such as unauthorized use of dangerous equipment.
9. Operational efficiencies
Although some machine learning use cases are highly specialized, many companies are implementing the technology to help handle routine businesses processes, such as financial transactions and software development.
“The most widely seen use cases in my experience (so far) are in enterprise finance organizations, manufacturing systems and processes, and, most impactfully, software development and testing. And almost every case occurs within grunt work,” Guptill said.
Machine learning is used to drive efficiency in many business departments, including:
- finance departments and firms that use machine learning to speed up work and reduce human error;
- operations teams that use machine learning-based solutions to monitor equipment and identify in advance when maintenance and repairs will be needed, thereby reducing unexpected problems and unplanned work disruptions; and
- information technology departments that can use machine learning as part of its automation of software testing to dramatically speed up and improve that process, resulting in better software developed faster and at lower costs.
10. Information extraction
Machine learning with NLP can automatically identify key pieces of structured data from documents even if the needed information is held in unstructured or semistructured formats.
“Using machine learning to understand documents is a massive opportunity across industries,” said Scott Likens, leader of advisory firm PwC’s new services and emerging tech practice.
Organizations can use it to process everything from tax forms to invoices to legal contracts, bringing increased efficiency and improved accuracy to such processes and freeing human talent from mundane, repetitive work. IT’s a use case, he said, that’s “not sexy, but it’s a real value for any business.”