The Outlook of Local in 2012
Location-based services became a new type of service with the emergence of the smartphone. Maps and navigation software are all about location. Foursqure has become the most popular “check-in” location-based service with 15 million users in December 2011 and over billion check-ins (https://foursquare.com/about/). Other services are Forcast, which allows you to tell your friends where you will be at some time, and Facebook has Places (after buying Gowalla, the Foursquare rival). But location is not the only thing about local. It‘s also about the person itself and the context.
Location apps seem to work. According to JiWire Mobile Insights Report, 78% of smartphone users use location-based apps and 29% use them multiple times a day. While this sounds great there are those who believe that the novelty will run out. Checking in is not enough, there has to be value.
As location service mature, they will give more value to the users. Eric Schmidt of Google said in his James MacTaggart lecture 2011: „If content is king, context is its crown.“ It turns out that with multiple of devices, people are in a different situation when using web sites. According to the JiWire report, 17% have made a purchase in response to a location-based advertisement.
This is however not limited to a specific place. Mobile apps can also factor in the time of day, what type of day (workday, weekend, or holiday), your surrounding (are you in a mall, or at a football game, or in a casino). It can also be based on data about you – your digital profile. This profile determines who you are, it is based on data you leave behind, your digital trail, your searches, the pages you visit, the pages you share, your recommendations, comments, your tweets, your Facebook information, and so on. With services based on your profile, apps can utilize a context that is local to you.
This is what we call context, and the technical term is Context Aware Computing or CAC. When one user is sitting at home with their laptops, taking time to find some content on the Internet, another one is looking for answers relative to the place he or she is in. As an example consider a sport betting solution. When accessing the solution on a laptop the player can be given multiple of choices. The player could be looking for content to watch or statistics to look up. If the same player accesses the solution via mobile we might want to give him or her fewer but more focused choices. It is likely that the player just wants to bet on the favourite team or look up the latest scores. The context of the player determines the content and to do so we have to use machine learning.
Machine learning is a method that uses algorithms to analyse empirical data to discover patterns and make decisions based on the data. Popular example uses of this are Amazon‘s recommendation engine and NetFlix movie recommendations. Machine learning is the next step after search. When searching gives you too many outcomes and becomes irrelevant, using data based on the user’s behaviour is the next logical step.
Location is just beginning. Software like Apple’s Siri are emerging and are in the early stages. In 2012 and beyond we will see more applications that use data analysis to find out more about us as users, track our behaviors, our views, comments, ratings and so on. Like it or not, your personal assistant is coming.
The picture above is on the remains of the Berlin wall, part of the East Side Gallery. Taken in summer 2011.