How your business can use machine learning

Simply put, machine learning (ML) puts “intelligence” in artificial intelligence (AI). And two-thirds of respondents to a Deloitte survey say AI brings substantial value to businesses. Yet many companies, especially smaller ones, haven’t figured out how to use ML as a competitive advantage.

The reluctance to adopt ML most likely stems from a misunderstanding about it. Like most emerging technologies, ML adoption rates started slowly and in only a few industries. Today, however, ML-powered AI has become an option for organizations of all sizes and in nearly every industry.

The beauty of ML is that it is programmed to mimic the processing system of the human brain. The more data the ML receives, the easier it is for the program to “learn” from information and experience. For example, YouTube relies on ML to recommend videos based on the ones you’ve already watched or liked. The more videos you click, the more inputs the ML system collects to discover your preferences.

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Not sure how ML could benefit you, your team, or your customers? Below are some of the most popular applications of ML in business today.

1. Obtain deeper insights from existing data.

If you’re like most companies, your employees have access to mountains of data. In fact, you probably have more data than you could ever sift through. ML can not only help you examine your data, but also find interesting and important trends and insights.

MarketMuse provides a great example of how to better manage your content ROI using ML. By enabling an ML program to perform a content audit, you can analyze more than the ranking of a piece of content. You can explore its relationship to internal and external content and understand its importance more holistically. Instead of making rash content decisions, you can make thoughtful decisions based on comprehensive ML feedback.

2. Encourage customers to make more purchases.

Does your business have an e-commerce component? You can leverage ML algorithms to present prospects and customers with items they might like. The longer they shop with you, the more personalized this type of service can be.

Amazon has perfected this type of “ML fetch” experience. However, it is by no means the only player in the B2C virtual retail space using ML. Many other online stores and auction platforms are adopting ML. Etsy wrote about the evolution between 2017 and 2021 of its proprietary ML platform. His assessment of ML’s many use cases shows how far-reaching its benefits can be, especially in pushing people toward higher customer lifetime values.

3. Reduce the risk of fraud.

ML has an incredible ability to see patterns in places humans couldn’t. For this reason, many companies are incorporating ML into their fraud detection practices. An ML program designed to look for unusual login attempts or transactions can stop cybercriminal behavior in its tracks.

While financial institutions might seem like the most ideal places to enjoy this ML app, they’re not the only ones. Cybercrime Magazine predicts that cyber theft results in annual collective losses of approximately $6 trillion worldwide. Being able to stop a breach would more than pay off for any organization.

4. Improve the chatbot experience.

Chatbots are created with the help of AI. But the best ones also have an ML component. The ML aspect of the chatbot allows the chatbot to understand how to interact naturally. In poll after poll, consumers are surprised to learn that they chatted with a robot they thought was a person.

With ML behind your chatbot, you can reduce the stress on your support staff even further. As bots begin to communicate better, they can recognize and respond to questions faster. They can also automatically populate CRMs and other systems with information pulled from chats. This allows customer service representatives to spend more time with callers with complex issues.

5. Spot corrupt and duplicate data.

Nothing is worse than relying on incorrect data. ML can be a boon to you by cleaning up your data so it’s always clean. Remember, the grittier your data, the more likely you are to make good decisions based on that data.

Your ML system can also examine the validity of incoming data points in real time. For example, ML might detect a problem early like a cell that is not filled out correctly. The ML system could either rectify the error or alert a human employee to a potential problem. The sooner you know something might not make sense, the sooner you can fix it.

6. Find new pockets of potential customers.

ML looks at everything objectively and with an eye for trends. Therefore, if you are trying to enter untapped markets, you may want to commission ML.

The right ML program can explore everything about your current customer base, from geography to buying cycles. Using all the information, the program can advise you on possible prospects that you might not have considered. From there, your sales and marketing team could experiment with ways to reach those markets.

Above all, your goal is to stay competitive, satisfy your customers and build a solid reputation. ML can help you achieve these goals. Even if you don’t quite understand how ML works, you’ll have no trouble seeing how well it works for you after you set up ML.

Updated

Sherry J. Basler