With Mobile, we consider where people are and what they are doing by focusing in the presence and usage of mobile devices. Whether they be mobile phone, tablets, laptops or other connected devices that can move with a user. We can actually expand this a little to include objects that are connected to mobile networks. Whether they be mobile like a car or truck or not like the water meter at your house.
Many of the same ideas that apply to that web also fly to mobile. Users can access web sites on mobile devices. And we like to understand that experience and potentially how that experience differs across different types of devices. We can often use web analytics techniques to get the answers. Mobile also adds another dimension to our advertising use case. Companies are increasingly using available information about a user’s location to provide highly localized targeted ads to the right people at the right place, at the right time. However one of the richest areas of insight provided by mobile data is around how people and objects as a whole are distributed geographically and how they move around. It’s possible for many companies to get access to this information.
A retailer’s mobile application asks for permission to access GPS information on a device. A wireless carrier detects which cell towers connect to a device. A fast food restaurant chain sees when a mobile device connects to a Wi-Fi hotspot in the store. A trucking company installs wireless devices in all its vehicles. There are a wide variety of marketing, sales, and operations problems that can be by understanding location data. In fact, there are entire businesses that are built with location data at the core. Think of your favorite navigation or traffic app on your phone. There are also businesses that are using precisely positioned connection points within their physical locations to track how customers move through a building or store.
Broadly speaking the techniques we use when working with location data fall into a class of analytics called spatial analytics. Normally this involves plotting location points on a map of some sort. And using visualization or algorithms to identify patterns and draw inferences on behaviors. We would also use related techniques like boundary triggering, cluster pattern analysis, or hot spot analysis to drive insights around specific problems.