Simply put, machine learning (ML) puts the “intelligence” in artificial intelligence (AI). And two-thirds of respondents in a Deloitte survey say that AI brings substantial value to companies. Yet many businesses, especially smaller ones, haven’t figured out how to use ML as a competitive advantage.
The hesitation to embrace ML most likely comes from a misunderstanding about it. Like most emerging technologies, ML’s adoption rates began slowly and within just a few industries. Now, though, ML-fueled AI has become an option for organizations of all sizes and within nearly every sector.
The beauty of ML is that it is programmed to mimic the human brain’s processing system. The more data ML gets, the easier it is for the program to “learn” from information and experiences. For example, YouTube relies on ML to recommend videos based on those you previously watched or liked. The more videos you click on, the more input the ML system gathers to discover your preferences.
Not sure how ML could be useful to you, your team, or your customers? Below are some of the most popular applications of ML in business today.
1. Gaining deeper insights from existing data.
If you’re like most companies, your employees have access to mounds 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 find intriguing, important trends and insights.
MarketMuse provides an excellent example of how to get a better handle on the ROI of your content by using ML. By allowing an ML program to perform a content audit, you can analyze more than just a content piece’s rank. 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 ones based on comprehensive ML feedback.
2. Encouraging customers to make more purchases.
Does your business have an e-commerce component? You can leverage ML algorithms to introduce prospects and customers to 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. It’s by no means the only player in the B2C virtual retailing space using ML, though. Many other e-stores and bidding platforms embrace ML. Etsy wrote about the evolution between 2017 and 2021 of its proprietary ML platform. Its assessment of the many use cases for ML shows how wide-ranging its benefits can be, particularly in nudging people toward higher customer lifetime values.
3. Reducing the likelihood of fraud.
ML has an uncanny ability to see patterns in places where humans might not. For this reason, plenty of companies are baking 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.
Although financial institutions may seem like the most ideal places to appreciate this ML application, they’re not alone. Cybercrime Magazine predicts cyber theft leads to annual collective losses of around $6 trillion globally. Being able to stop one breach would more than pay for itself for any organization.
4. Improving the chatbot experience.
Chatbots are created with the help of AI. But the best ones have an ML component, too. The ML aspect of the chatbot enables the chatbot to understand how to interact naturally. In survey after survey, consumers are surprised to learn that they’ve chatted with a robot they thought was a person.
With ML behind your chatbot, you can lessen the stress on your support people even more than before. As bots begin to get better at communicating, they can recognize and answer questions faster. They can also automatically populate CRMs and other systems with information culled from chats. This frees up customer service representatives to spend more time with callers who present complex concerns.
5. Spotting corrupted and duplicate data.
Nothing’s worse than relying on data that is incorrect. ML can be a boon to you by scrubbing your data so it’s always clean. Remember that the squeakier your data, the better chance you have of making good decisions based on that data.
Your ML system also can examine incoming data points for validity in real-time. For instance, ML could catch a problem early like a cell that’s not correctly populated. The ML system could either rectify the mistake or alert a human employee to a potential problem. The earlier you know that a piece of data might not make sense, the sooner you can fix it.
6. Finding new pockets of potential customers.
ML looks at everything objectively and with an eye for what’s trending. Consequently, if you’re trying to move into untapped markets, you may want to put ML into service.
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 you may not have considered. From that point, your sales and marketing team could experiment with ways to reach those markets.
Above all else, your goal is to stay competitive, keep your customers satisfied, and build a solid reputation. ML can help you achieve those objectives. Even if you can’t quite grasp how ML works, you’ll have no trouble seeing how well it works for you after putting ML in place.