Oil and gas companies rely heavily on geographic data to determine where to drill, contending with the location and positioning of rock formations before extraction, etc.
Likewise, retailers are heavily dependent on locational data to ensure that they’re placing stores close to the target customers they’re trying to reach. Meanwhile, retailers also regularly use locational data to strategically position inventory in store aisles.
The use of locational data across various vertical sectors can provide decision makers with meaningful insights they can use to optimize operations or strengthen business outcomes.
However, the geographic information systems (GIS) used by companies in different industries to collect information doesn’t really help with the analysis of the data.
There are a number of ways that business leaders and front-line managers can use locational data and analytics to generate insights that can lead to faster and more effective decision making.
In the energy industry, executives for electric utilities that are deploying smart meters can use predictive analytics and real-time data streams to obtain granular insights as to how customers are consuming energy.
The use of data discovery techniques can further help utilities’ leaders become more informed about how to set pricing and identify cross-sell/upsell opportunities for programmable thermostats and other products and services.
Insurance leaders can harness the use of locational data generated through in-vehicle telematics with analytics to gain deeper insights into driver behavior.
Insurers who are able to determine whether and to what extent customers are driving safely (e.g., driving within required speed limits, braking properly in advance of stop lights or intersections) can build a much more detailed picture of risk both on an individual and trend-led basis.
Meanwhile, product marketing leaders for consumer packaged goods (CPGs) companies can analyze location-based data in a number of different ways.
For instance, decision makers for CPG companies can analyze product placement data gathered in supermarkets and other retail outlets to optimize product assortment, pricing and promotions.
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