Welcome to the Location Data Blog!

 

Learn how companies all over the globe are utilising location data to enhance business operations and improve profitability. Keep up with industry updates, best practices, and key learnings from location intelligence projects we have executed.

Roger Sundararaj

Roger Sundararaj
Data Scientist | Solidity Engineer at Quadrant
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Recent Posts

How Quadrant prevents fraudulent records within location data supply chains

Good quality data is crucial for the operational efficiency of location-based businesses. Location-based analyses used in critical decision-making processes rely on the quality and accuracy of data powering them.

However, location data supply chains are characterized by a lack of transparency and authenticity, because data vendors prioritize volume over quality. To ensure that our customers make the most of their data purchase, we have made significant strides in identifying and erasing fraudulent data within our datasets. One way we achieve this is by examining our own data sources and constantly monitoring activity within our mobility and POI data feeds. We use data visualisation and proprietary AI models to spot instances of low-quality or fraudulent data and remove them before reaching customers. 

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How retail banks increase their competitive edge with location intelligence

Similar to other industries, financial services have been leveraging mobile location data to improve its operations and services.

Investment firms analyse mobility data in conjunction with POI data to forecast revenues for various retail outlets. This enables these companies to determine which businesses will deliver a good ROI.

Retail banks utilise location data in a number of ways. An important application includes limiting or preventing fraudalent transactions. Retail banks compare the IP address of a Point-of-Sale (POS) with the GPS coordinates provided by a customer's phone to prevent credit card fraud.

Banks rely on location intelligence to drastically improve the experiences they provide their customers. Here are three examples of how retail banks can use mobility data to enhance customer service and increase competitiveness. 

In each example, anonymized mobility data around Downtown Los Angeles between 1 October 2016 and 31 October 2016 was used.

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Combining mobile location & POI data to derive actionable intelligence

Location intelligence has revolutionised the way companies establish, operate and expand their business. From deciding an optimal location for a new store to performing footfall analysis to gauge competition in an area - location intelligence helps companies make faster, well-informed and profitable decisions.

One of the critical components of location intelligence is Point-of-Interest (POI) data. A POI indicates a specific location in an area of interest for a business. It could be a retail store, a hospital, a university, or a commercial building depending on what intel you need. POI data is vital to maximising returns on marketing and advertising, expanding a business to new areas, powering maps and navigation systems for logistics and supply chain and more.

To adequately leverage POI data, buyers must understand the type of data they need, the scale they need it at, and understand the best way to represent the acquired data to fit their use case. Here are the various ways to represent POI data, and how these methods can help answer questions to specific use cases and applications.

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Why businesses need to understand quality location data better

Data analysis plays a crucial role in detecting bad, misleading, and even fraudulent data. The data economy is full of such low-quality data, especially when dealing with location data.

Providing the highest-quality location data should be the goal of data providersand so this is an issue close to home for those of us playing this role in the industry - an issue to be taken seriously.

Let’s consider some of the various sources of location data, and what data feed characteristics tend to indicate about quality.

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The Uses of Location Data in the Financial Services Industry

Facing increased regulation and disruption from new technology, banks and financial institutions who want to maintain their competitive edge need to leverage on location data.

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