Nathan Sykes
When we interact with digital devices and services, they almost always generate data about our location. Smartphone users rely on location services to look up driving directions and weather reports and set up geo-fenced alerts. For service providers, this information is equally useful: It can reveal a lot about customers, competitors, and opportunities to expand or improve their services.
Applying machine learning will make location-based services even better attuned to our needs and preferences. Let’s take a look at what this might mean in practice.
Proactive Navigation
The greatest triumph of the smartphone was the democratization of the Global Positioning System. Once just a tool for governments and militaries, GPS now empowers people all over the globe with insights into where they’re going and how to get there. We can complain all we like that nobody knows how to read a paper map anymore, but does anybody really want to go back to those days?
Thanks to machine learning, our smartphones, and mobile apps are going to get better at delivering uncannily accurate predictions about where we need to go, when we need to leave and how to get there, all based on pattern recognition and historical user data. We can already add a “time to leave” modifier to the entries we make in our calendars, but machine learning will take this to the next level.
Next time you’re getting ready for work, imagine receiving an unprompted notification on your phone screen indicating a traffic snarl-up along your usual route. You know how to get to the office — you haven’t needed directions to get there in a long time. However, thanks to machine learning, your phone is helpfully pinging you with an alternate route, because it knows your usual commute is going to slow you down and it doesn’t want you to be late.
From Apple and Google to Nokia and lots of startups you haven’t even heard of yet, there’s a lot of money being poured into intelligent navigation systems. The future, according to Uber, Lyft, Tesla and others, is autonomous cars with smart navigation that can change in response to real-time events. Getting there means our technology needs to get a lot better at studying and visualizing user patterns for thousands of customers at one time. It also needs to take into account congestion, weather, time of day, planned and unplanned events, and much more.
Smarter Apps
Many of us rely on smartphones to remind us about upcoming items on our to-do lists, to keep track of which groceries we’re running low on, and whether our next dental cleaning is coming up. If reminders and calendar items are the bread and butter of the mobile operating system experience, machine learning is the secret sauce that could take stock smartphone apps and deliver performance that makes it feel like we’re living in the future.
Suppose we’ve been adding items to our grocery lists like toilet paper, milk, and eggs. Before too long, our favorite apps will be smart enough to plan our shopping trips and even entire days with uncanny accuracy and help us make the most of our limited time. They’ll know what’s on our shopping or to-do lists and where we’ve been in the past when we checked those items off. The next time we’re driving by the store, they’ll let us know about it — all without being asked — or maybe even suggest an alternative that’s running a sale or promotion.
We tell ourselves that smartphones are like digital personal assistants, but we have to perform a lot of the logic for them. Thanks to a combination of machine learning and location services, we can expect far more intuitive and automated performance soon.
Destination Predictions and Recommendations
Everybody loves to travel, but finding lodgings, activities, and places to eat can be overwhelming. Wouldn’t it be nice to have some helpful guidance to iron out the kinks in your itinerary and ensure a smooth experience? As Google points out, about 85% of people who travel for pleasure don’t plan their activities until they’ve arrived at their destination.
To get a sense of how traveling and planning destinations stands to change and improve with machine learning at the helm, consider these use cases:
You’ve arrived at your destination and you’re looking for an afternoon activity the whole family can enjoy. Instead of poring over the hotel’s stacks of pamphlets, you can ask your phone or your favorite travel app for recommendations instead. Based on keywords in user reviews, driving distance, and your travel history, it can deliver accurate and personalized results and rank them based on what you’re most likely to enjoy.
You’re hungry and you need to find a place to eat. With just a quick voice search, your phone and navigation app can pull up a list of restaurants in the area, prioritized according to the time of day, curated based on past preferences, and organized into restaurants that have a delivery option. With 70% of restaurant traffic expected to be off-premise by 2020, machine learning will continue to play a part in the new age of takeout.
You’re in an unfamiliar airport. You know your gate number and the boarding pass is on your phone already. As you unlock it for a bit of help, you’re surprised to already find walking directions, courtesy of location beacons.
The same goes for finding a hotel — or even choosing a city to stay in, for that matter. Thanks to machine learning, your whole trip can come together faster and easier than ever. Based on web history, historical location data, keywords in your messages, photos you’ve taken and even your step counts, machine learning is about to make our phones and apps smarter than any travel agent ever was.
There are many exciting ways to combine machine learning with location data. We’ve all had to call off the dogs once or twice when our bank mistook our impromptu weekend out-of-town for a stolen credit card. Combining these two technologies should deliver far more intelligent forms of fraud prevention for those on the go. In these ways and many more, this is a technology pairing that’s sure to continue delivering dividends well into the future.
