Information is not processed by the human brain in rows and columns. Information is organized by our brains in terms of time and place. However, today, organizations still have information confined to rows and columns. It is true that this makes reporting fast and easy – but not necessarily more insightful. Today, organizations have the luxury to add the context of location and timing to traditional data, thereby creating maps that show changes that have taken place over time and exactly where these changes have taken place. Maps make it easier for us to recognize patterns that used to be buried in spreadsheets – Patterns in relation to distance, proximity, affiliation and contiguity.
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Technology today – mobile devices social media, location sensors and more – allow organizations to collect data with respect to time and place practically about any event. The big question arises – “What can be done with all this information?” Geospatial analysis builds maps, graphs, statistics and cartograms using this data to make complex relationships understandable. These representations can make us aware of historical shifts, shifts that are underway today and can even point to those that may occur in the future.
In order to remain competitive in today’s world, companies are looking to a variety of different data types and new forms of analysis. Companies with a futuristic mindset are developing analytics ecosystems that make use of disparate kinds of data. These include – text data, social media data, machine data and more. In traditional and big data analytics, geospatial data is emerging as an important source of data. Geospatial data and geographic information systems (GIS) software are being integrated with other analytics products to enable analytics that utilize location and geographic information. Such analytics are leaving mapping in the past and are moving on to more sophisticated use cases like – advanced visualization and predictive analytics.
Geospatial technology is turning heads as organizations want to harness the power of location information to aid with the toughest business challenges. Our geospatial analytics practice helps you use information in new ways and integrate the latest technology for robust and affordable geospatial solutions.
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Today location information is being collected everywhere due to the increase of location aware mobile devices and socially enabled platforms. Our team of Geospatial analytics experts can help develop data, application and enterprise solutions that will give you new insights and integrate smoothly with existing processes and technology.
Market segmentation in marketing is a popular use case for geospatial analytics. The goal of market segmentation is to divide the customers into groups with common characteristics. These customers may have demographic and/or lifestyle data and even behavioral features in common. These segments will help improve retention, promotions and to acquire new customers. Let’s take the case of a sporting goods that uses demographic data along with purchase history to develop its segments. With proper analysis, the company identified a segment – “High end soccer mom”. This segment consists of customers who are female and aged 30-45 and buy high end sporting goods for their children and themselves.
The addition of geospatial data to this mix can help the company maximize its promotional activity and target new customers. Making use of geocoded data from ZIP codes associated with its current customers will help visualize where the customers in this segment live. When maps combine this data together with store locations, it helps visualize the distribution of the target segment around its stores to learn where the women are clustered geographically. In this case business analysts may create a new derived variable called distance. This would be the distance between various store locations and ZIP codes. What this does is, it helps the company look for other women who fit the segment’s profile and reside in clusters within a 10 mile radius of various stores. Using this data the retailer can decide as to which stores would benefit from a targeted promotion to both existing customers and prospects. Going one step further, the company could use location data from the GPS on the target segments smartphones to send offers via push notifications, on weekend’s maybe, or when the customers are in the store.
Transportation charges are often a large chunk of logistics costs. Driver time, fuel and maintenance add to these costs. With the objective of maximizing fleet utilization in order to achieve optimum efficiency, transportation companies usually build complex models making use of linear programming techniques. Some of the factors that are taken into consideration may be shift times, delivery data, trucks used, orders loaded on each truck, routes taken and more. Currently the combination of this software with GPS tracking and data is used to monitor the planned versus real time of delivery.
Geospatial analytics can be used in different scenarios. A service company which is required to plan daily routes for its technicians who need to arrive on time for specific customer appointments also benefit from geospatial data. With the idea of achieving maximum efficiency in mind, accurate street data and customer locations can be used to ascertain driving times. Along with this, installation of GPS devices in the trucks helps infield technicians plan routes in case of an issue. These devices will transmit data back to homebase. This will help in tracking service activity and help optimize operation and meet response time targets.