Machine learning use cases
Machine learning is a vast field, composed of many model types, subsets, and use cases. In our forthcoming 2020 State of Enterpriser Machine Learning report, we dig into the use cases that are used most often by businesses today, but as there are new advances made in ML every day, there are also advances in number and complexity of ML use cases. This post will walk through some common machine learning use cases and how they enable businesses to leverage their data in novel ways.
What is machine learning?
Machine learning is the subset of artificial intelligence that involves the study and use of algorithms and statistical models for computer systems to perform specific tasks without human interaction. Machine learning models rely on patterns and inference instead of manual human instruction. Most any task that can be completed with a data-defined pattern or set of rules can be done with machine learning. This allows companies to automate processes that were previously only possible for humans to perform—think responding to customer service calls, bookkeeping, and reviewing resumes.
To extract machine learning value, a model must be trained to react to certain data in certain ways, which requires a lot of clean training data. Once the model successfully works through the training data and is able to understand the nuances of the patterns its learning, it will be able to perform the task on real data. We’ll walk through some use cases of machine learning to help you understand the value of this technology.
What is machine learning used for?
Machine learning has many potential uses, including external (client-facing) applications like customer service, product recommendation, and pricing forecasts, but it is also being used internally to help speed up processes or improve products that were previously manual and time-consuming. You’ll notice these two types throughout our list of machine learning use cases below.
This consumer-based use for machine learning applies mostly to smart phones and smart home devices. The voice assistants on these devices use machine learning to understand what you say and craft a response. The machine learning models behind voice assistants were trained on human languages and variations in the human voice, because it has to translate what it hears into words and then make an intelligent, on-topic response.
Millions of consumers use this technology, often without realizing the complexity behind the tool. The concept of training machine learning models to follow rules is fairly simple, but when you consider training a model to understand the human voice, interpret meaning, and craft a response, that is a heavy task.
This machine–based pricing strategy is most known in the travel industry. Flights, hotels, and other travel bookings usually have a dynamic pricing strategy behind them. Consumers know that the sooner they book their trip the better, but they may not realize that the actual price changes are made via machine learning.
Travel companies set rules for how much the price should increase as the travel date gets closer, how much it should increase as seat availability decreases, and how high it should be relative to competitors. Then, they let the machine learning model run with competitor prices, time, and availability data feeding into it.
This is a classic use of machine learning. Email inboxes also have a spam inbox, where your email provider automatically filters unwanted spam emails. But how do they know when an email is spam? They have trained a model to identify spam emails based on characteristics they have in common. This includes the content of the email itself, the subject, and the sender. If you’ve ever looked at your spam inbox, you know that it wouldn’t be very hard to pick out spam emails because they look very different from real emails.
Amazon and other online retailers often list “recommended products” for each consumer individually. These recommendations are based on past purchases, browsing history, and any other behavioral information they have about consumers. Often the recommendations are helpful in finding related items that you need to complement your purchase (think batteries for a new electronic gadget).
However, most consumers probably don’t realize that their recommended products are a machine learning model’s analysis of their behavioral data. This is a great way for online retailers to provide extra value or upsells to their customers using machine learning.
Marketing is becoming more personal as technologies like machine learning gain more ground in the enterprise. Now that much of marketing is online, marketers can use characteristic and behavioral data to segment the market. Digital ad platforms allow marketers to choose characteristics of the audience they want to market to, but many of these platforms take it a step further and continuously optimize the audience based on who clicks and/or converts on the ads. The marketer may have listed 4 attributes they want their audience to have, but the platform may find 5 other attributes that make users more likely to respond to the ads.
There are many processes in the enterprise that are much more efficient when done using machine learning. These include analyses such as risk assessments, demand forecasting, customer churn prediction, and others. These processes require a lot of time (possibly months) to do manually, but the insights gained are crucial for business intelligence. But if it takes months to get insights from the data, the insights may already be outdated by the time they are acted upon. Machine learning for process automation alleviates the timeliness issue for enterprises.
Industries are getting more and more competitive now that technology has sped up these processes. Companies can get up-to-date analyses on their competition in real time. This high level of competition makes customer loyalty even more crucial, and machine learning can even help with customer loyalty analyses like sentiment analysis. Companies like Weavr.ai provide a suite of ML tools to enable this type of analysis quickly and deliver results in a consumable format.
Banks use machine learning for fraud detection to keep their consumers safe, but this can also be valuable to companies that handle credit card transactions. Fraud detection can save money on disputes and chargebacks, and machine learning models can be trained to flag transactions that appear fraudulent based on certain characteristics.
Machine learning can provide value to consumers as well as to enterprises. An enterprise can gain insights into its competitive landscape and customer loyalty and forecast sales or demand in real time with machine learning.
If you’re already implementing machine learning in your enterprise or you’d like to start...