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Location Intelligence Part II: Real World Examples from Businesses that Use Geospatial Data

ABSTRACT: This article, the second in a series, shares cutting-edge examples of location intelligence applications from the real world.

In my last blog, I laid out an abstract framework for the kinds of questions you can ask of geospatial data. This blog provides concrete examples of companies that implement location intelligence across a range of industries. Although I don’t name specific organizations, all of these examples come from real case studies.

Intelligent Farming

Agriculture may conjure pastoral images of farmers standing out in their fields with a plow, but, in reality, modern farming is a highly data-driven industry. It sits at the cutting edge of Internet of Things (IoT) applications thanks to smart tractors and soil and crop sensors. At the same time, it’s an industry inherently tied to physical space. After all, a farm is a large plot of land.

In traditional industrial farming, entire plots were treated in the same way with mass watering and fertilization. This allowed farms to operate at an unprecedented scale, but it neglected the reality that the earth is inconsistent. The mineral and moisture composition of the ground can vary even within a single field. But advances in the precision of smart sensors have allowed farmers to detect and handle these discrepancies. 

Agriculture equipment manufacturers embed analytics services into their machines. Using the spatially linked data their sensors gather about field conditions, they can make real-time suggestions about which parts of the field require what interventions. They even can direct machines to apply different treatments to different sections of the farm. Not only does this result in better yields, but it also increases the efficiency of resource usage. At a time when persistent droughts in major agricultural regions like California have placed a premium on water conservation, this geospatial data-driven approach allows farms to avoid needless overwatering.

Management of Telecom Networks

In the telecom industry, companies need to route millions of calls in a split second. Careful analysis of geospatial data helps support use cases such as network traffic optimization and new equipment installations.

Network traffic optimization. Any loss of signal or connection creates irate cellular customers who might jump ship to carriers with more reliable service.  Cellular carriers use geospatial analytics to choose the optimal routing paths for calls when volume runs high or when towers go down. In a split second, algorithms can process data to determine the most efficient path, keeping calls coming through and customers happy. This outcome is only possible thanks to the ability to process high volumes of data about the location of devices and towers.

New equipment installations. Carriers around the globe are rolling out 5G infrastructure and need to pick the optimal locations to place new equipment. They analyze geospatial data to identify the locations responsible for the most traffic on their networks. Then, they can build more robust coverage in the areas that require it. They can also use trends to predict which regions are most likely to see growth in the future, so they can build out infrastructure ahead of demand.


There’s no point in advertising your downtown Los Angeles coffee shop to folks in Des Moines, Iowa. This is why we have geomarketing. In a world where online ad purchases account for an ever-growing portion of marketing budgets, it’s important to know where the customers viewing your products reside or travel—especially for companies with a physical presence or a limited geographic range.

In the old days of TV and billboard marketing, setting geographic boundaries was relatively easy. You’d just buy space in the geographic markets relevant to you. Traditional geomarketing uses location data generated by computers or mobile devices to broadcast their messages to customers in their target regions. 

Today, however, retail companies experiment with a new, hyper-precise approach facilitated by advances in indoor mapping. This new technique consists of marketing to potential customers who are nearby or even already in a store. Rather than blanketing a whole region with ads, the retailer might push coupons to visitors in a mall who stand within 500 feet of their location. 

Territory Planning

For companies with field staff that make in-person sales calls, territory planning can impact the efficacy of their teams. While many companies allocate leads manually, location intelligence can help create statistically optimized territories. This helps reduce the time salespeople spend on the road, increase the visits they can make per outing, and free them up to spend more time selling. 

In these instances, companies pull location data from customer relationship management (CRM) tools. They analyze that data to determine drive times between locations and the expected profitability of each locale. With that information, they can calculate the optimal boundaries for each territory. Because that analysis is based on data, as underlying factors change, territories can adjust at the same time. That flexibility allows businesses to respond to the signals coming from their market before sales productivity declines.

Fraud Detection

Geospatial analytics forms a significant part of the modern approach to fraud detection. Every time someone makes a credit card purchase, the bank receives a record of the location where the transaction took place. With that information, the bank can create a geographic profile of the common spending patterns for each of its credit card customers. When there’s a significant deviation—for example, a five-figure purchase thousands of miles away from the last purchase a customer made—it can raise a fraud alert.

With modern tools, companies can even use geospatial data to get ahead of fraud. By analyzing where fraud happens the most, businesses can identify areas where they should review transactions more closely. This means they optimize staff productivity—for example, human fraud analysts—by deploying them on analyses where they’re most likely to have an impact.

Location intelligence supports a wide variety of use cases. Obviously, this wasn’t a comprehensive overview, but it should give you a taste of the power of modern geospatial applications. As you consider your own industry, look for parallels to see if there are use cases that would benefit from geospatial analysis. The third and final blog in this series will examine the technologies that enable all of these capabilities.

Joe Hilleary

Joe Hilleary is a writer, researcher, and data enthusiast. He believes that we are living through a pivotal moment in the evolution of data technology and is dedicated to...

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