Machine Learning Use Cases

We’ve talked about what machine learning is, but how is it being used today? Current applications and those in late-stage development are astonishing in their breadth and capabilities, and inspiring to those looking to drive competitive advantage with these emerging technologies. Following is just a taste of what’s going on in the field.

Industry Verticals

Most industries collecting and working with large amounts of data have begun to invest in machine learning technologies with an eye toward gleaning real-time insights to work more efficiently, maximize ROI, and gain competitive advantage. For example:

  • Sales and marketing. Leading websites use machine learning to personalize the shopping experience, recommending products based on both previous purchases and trend analysis of broader customer data.
  • Financial services. Fraud prevention is a big area for machine learning for banks, credit card companies, and others. Machine learning is also becoming integral in identifying investment opportunities and steering financial planning and risk management.
  • Machine learning is good at predicting traffic bottlenecks in order to increase throughput and reduce costs. The technologies are also used for logistics by the likes of UPS, FedEx, and the United States Postal Service.
  • Law enforcement. Police and other enforcement groups have multiple, independent sources of data that can be analyzed collectively to produce insights. Crime data can allow police forces to re-route patrol patterns to higher risk areas.
  • This industry is looking to machine learning to discover new energy sources still in the ground, predicting equipment failures, driving efficiencies, and streamlining distribution.
  • Wearable devices are accelerating machine learning applications in healthcare, as the sensors collect various data that can be used to monitor and forecast a patients’ health. Medical professionals have more information at their disposal to improve diagnoses and to suggest preventative measures and treatments.
  • Retailers are turning to machine learning for many reasons, including inventory management; picking, packing, and shipping; supply chain management; and “shrink” reduction.

Other Use Cases

Various other use cases transcend industry and are being applied by various companies:

  • Detecting fraud, not only purchases made with stolen credit cards, but even identification of inauthentic product reviews.
  • Personalizing any website experience, whether for content consumption or product recommendations.
  • Targeting marketing campaigns to attract new customers and/or upsell, cross-sell and expand relationships.
  • Predicting customers at risk of attrition and implementing retention programs.
  • Classifying documents or any unstructured text and taking actions based on content; for example, rating Tweets about a company as positive, negative, or neutral.
  • Automating customer support, in some cases offering better scripted responses; in more complex applications, using customer comments available on email, service calls, or social media to identify those with an issue and get them to appropriate customer care.
  • Offering computer vision systems, such as to replace barcode scanning, and performing character recognition for various purposes.
  • Creating chat bots for anything from information lines and health monitoring systems to facilitating basic bank transactions.
  • Improving search results that adapt to constantly changing scenarios, such as flight searches that need to weigh new schedules, prices, features (e.g., onboard internet), and so on.
  • Enabling predictive maintenance of machinery, such as in manufacturing or energy, sometimes engaging drones and image processing for inspections in the field.
  • Recognizing unhealthy or problematic behaviors, such as identifying problem gambling on casino websites.
  • Using facial image recognition for login and security or for law enforcement applications.
  • Optimizing supply chains through data-driven analytics.

Did these examples spark ideas for using existing data within your organization to jumpstart some machine learning? Next up, we have some information from the experts on how to get started.




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