Building The Future of Freelance Software / slashdev.io
Machine Learning in Action: Real-world Applications by Top Companies/
Machine learning has taken the business world by storm over the past year, with many executives believing it will be as significant a paradigm shift as the Internet and personal computers. Machine learning is a subfield of computer science that uses large data sets and training algorithms to teach computers how to learn without being explicitly programmed. This technology has wide-ranging applications that can benefit businesses of all sizes and stages. Here are five ways that businesses are already using machine learning to create value, reduce costs, and drive ROI.
Machine learning is used by businesses across the entire customer acquisition funnel. For example, Amazon uses machine learning to power product and deal recommendations based on user preferences. Many retailers use machine learning to segment users and show them relevant products. This technology is also used to adjust branding, copy, and promotional pricing on the fly to maximize the likelihood of a sale. On the enterprise front, Salesforce’s Einstein product examines CRM data to provide tailored recommendations to increase the chance that a prospect will convert.
To retain users and limit churn, businesses are using machine learning to improve the customer support experience. For example, Brazilian supermarket Ocado used Google machine learning APIs to build a custom system that measures the sentiment of customer support inquiries and moves negative responses to the top of the support queue. This results in Ocado responding to urgent messages four times faster, creating a valuable opportunity to win back customers at high risk of becoming detractors. Chatbots are now triaging support requests without help from a human operator, using machine-powered natural language to deliver a first response that can fulfill routine requests like issuing return labels.
Many organizations are starting to use machine learning to build more robust, granular, and accurate forecasting models. For example, Walmart ran a competition on the data science recruiting platform Kaggle, asking applicants to use historical data from 45 stores to build a model that forecasted sales by the department for each store. Insurance giant AIG has assembled a 125-person data science team to build machine learning models, with the goal of improving the company’s ability to anticipate claims and predict outcomes. Even the global eyewear conglomerate Luxottica puts machine learning to work forecasting demand, adding 2000 new styles to its collection every year.
Security and Fraud Detection
Machine learning is starting to bear out its potential as a powerful tool to intelligently monitor millions of transactions in real time, reducing waste from fraud. PayPal is a leader in this arena: they have used open-source tools and their vast trove of transaction data to build an artificial intelligence engine from scratch, with the key goal of reducing the number of false alarms produced by their older fraud models. Startups like Sift Science can consume a business’s data and apply fraud signals from their entire network of enterprise customers, ensuring that the latest techniques of fraudsters are swiftly caught.
Machine learning can help businesses filter through hundreds or thousands of resumes to assemble a shortlist for interviews. This problem is being addressed by startups like Restless Bandit, which makes a candidate management system used by companies like Adidas and Macy’s to filter resumes based on decisions that hiring managers have made in the past. These algorithms can be trained to ignore unconscious human biases and even flag biased language in job descriptions. Machine learning can also augment the mentorship of great managers and help employees perform better by generating specific and unbiased career advice, based on past employees with similar profiles.
In conclusion, machine learning has made significant strides in the business world and has the potential to help companies in a variety of ways. With more data and cheaper storage, widely available machine learning libraries, and greater horsepower, the barrier to entry for organizations wanting to apply machine learning has been significantly reduced.