Machine Learning Improves UX. Just Ask Google.
- Posted by Brittany Coombs
Google has officially caught the machine learning bug. Recently, the company announced it will use machine learning, commonly called “ML” by engineers, to give Android a competitive edge over Apple in the mobile device space. Google’s not stopping there, either: It also intends to improve user experience on Google Now and Google Photo with machine learning techniques.
What, exactly, is machine learning? And what does it do? These questions linger for many businesses (and we’ve previously attempted to answer them in our webinar, “Machine Learning 101.”) A subfield of computer science that uses data science techniques, ML is what it sounds like: a methodology for enabling machines—or applications, programs, and systems—to understand (learn) data and act on it. These actions don’t have to be explicitly programmed into machines. Rather, developers use ML to give systems the ability to independently or automatically react to data in flexible, smart, and proactive ways in real time.
Most consumers don’t realize it, but we use programs powered by machine learning all the time. When you type, “Hows it goinf” and your phone automatically corrects—autocorrects—to “How’s it going,” complete with apostrophe and proper spelling, that’s machine learning at work. Your phone is not only able to identify misspelled words but is also able to surmise the words you were probably going for based on common typing errors (for everyone) and your personal most used words.
Autocomplete is another form of machine learning. When you type “how to” into Google and the search engine suggests endings for this phrase like “tie a tie,” “get a passport” or “write a sonnet,” ML is doing the dirty work. The search engine’s algorithms are helping Google guess what you want to look up based on several factors, including hot topics locally, domestically, and globally, the Internet’s search history, and your individual search history.
Now, autocorrect and autocomplete are simple examples of machine learning. Android, Google Photo, and Google Now are complex products that will require advanced implementations. But Google is up to the challenge and is poised to use machine learning to great advantage in the mobile space. With ML, Google products will be able to pull and organize data from a wide range of data sets, create insights, and use these insights to reach customers in new and engaging ways—with reduced, minimal, or no intervention on the part of Google developers in the process.
Machine learning will also enable Google to build more dynamic and tailored user experiences with customer feedback and demographic data. ML algorithms use both positive and negative user opinions to help products adapt to the interests of individual customers. In addition, these algorithms detect patterns in usage among groups of customers, enabling Google and its products to better predict the interests of users with certain profiles and the actions they’re likely to take.
For enterprises, technology products are becoming more and more a part of the overall offering to customers. This is true even for companies that don’t live in the tech sector. Machine learning is integral to augmenting and expanding the customer experience. With its predictive, proactive algorithms, companies that are customer-obsessed can give users more customized interactions. Moreover, companies that are seeking to learn more about customers and reach out more effectively can take a huge leap forward with data-driven insights and automatic action by their programs.
If Google believes in machine learning, there’s a strong chance we all should.
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