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4 Ways Predictive Data Analytics Changes How Consumers Behave

Smartphones have made it possible for businesses to monitor you at all times. Take a company like Google for example. You may look up the name of a restaurant over Google Search, turn on your navigation to the destination using Google Maps and perhaps also check for weather and traffic updates along the way. The amount of information you provide to Google here is pretty exhaustive and is a treasure trove in the hands of a data analyst.

Privacy advocates may have their reasons to be concerned about users providing commercial entities like Google access to so much information. This article is however all about the different ways these tech businesses are transforming these billions of data points into something extremely useful and possibly revolutionary in the area of predictive data analytics.

Google Play Music

Music apps have been making use of historical data to recommend new playlists for a long time now. But Google is doing something extraordinary with their new Play Music. Recently, the company launched a revamped Play Music that will make use of dozens of data sources to recommend music more accurately than any other product out there. The main source of information is of course the music you have listened to before. But that is not all. Google now uses a host of other factors influencing your music preferences. For instance, you could pick classical music at work, peppy songs during your gym session and perhaps romantic songs while you travel. Google’s machine learning algorithm now intrapolates your music preference with other factors like location(at work or at gym, for example), weather (raining or sunny) and even other details pulled from your email or calendar to find the perfect playlist recommendation for you.

Uber Restaurant Guide

As a service that, among other things, transports people to and from restaurants, Uber has pretty valuable data points that can tell a user what restaurants its customers prefer to visit in any given location. Uber is now coming up with a restaurant guide that uses this data along with other real-time information about the number of drop-offs, the type of vehicle used and trending locations to prepare its restaurant guide. The number of drop-offs could perhaps tell you about the popularity as well as waiting times, type of vehicle could be an indicator of how upscale the restaurant is, and trending locations could be used to recommend restaurants to users who do not have any specific destination in mind. As of now, the Uber restaurant guide is only operational in twelve cities across the US although this is likely to go up in future.

Apple’s Siri Experiment

If there is one product that has brought machine learning to the mainstream, it is perhaps Siri. The voice assistant on the iPhone makes use of deep learning (which is a tad different from traditional machine learning) for speech recognition, natural language understanding, execution and voice response. Ever since it was incorporated into the iPhone, the software has undergone a sea change and uses machine learning incorporated through deep neural networks, convolutional neural networks, long short-term memory units, gated recurrent units and n-grams to cut down its error rate by a factor of two. Besides Siri itself, Apple also has ingrained machine learning into all of its products right from showing reminders for appointments you never got around to entering on your calendar, showing map locations of hotels even before you type it in and also detecting fraud on the Apple Store.

Facebook FBLearner Flow

The amount of data stored and processed on Facebook is humongous. The earliest users of Facebook today have over ten years of photos and videos stored on their timeline which needs to be pulled up anytime it is requested. Now take into account the over billion monthly active users and the sheer scale of the challenge becomes apparent. Last year, the company made its AI backbone called the FBLearner Flow available company wide. This platform is what controls every minute aspect of machine learning and AI within Facebook’s many products. Aside from plainly obvious features like deciding the right kind of content and friends to show on the timeline, FBLearner Flow also includes models for many intricate machine learning programs. For instance, one model helps Facebook provide auto-captioning of videos to its advertisers. Studies have shown that captioned videos bring about higher engagement levels than regular videos and can boost viewing time by as much as 40%. Quite evidently, such machine learning scripts are critical in bringing more advertising revenue. Such internal machine learning models have also helped Facebook reduce its reliance on third party tools to translate the nearly two billion news feed items each day (for which Facebook used Microsoft Bing’s Translation tools earlier).

Most of these machine learning innovations are not immediately evident to a layman user and pass off as a small addition towards better user experience. But in each of these instances, the companies have to deal with millions, if not billions, of data points to analyze, execute, test and relearn concepts. It will be interesting to see where these various experiments lead us to over the next decade.

 

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Image: kyknoord, CC 2.0

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