Table of Contents

    What is location data?

    Location data presents geographic information about precise locations expressed in coordinates for devices such as mobile devices, mobile phones, smartphones, tablets, smartwatches, or IoT devices.

    This data is valuable for many reasons, such as emergencies, including missing persons, individuals in danger or affected by natural disasters. Businesses use this data for real-time, real-world navigation, transportation tracking, and targeted mobile ads.  

    Where does the data come from?

    Location information comes from devices such as smartphones or tablets, wearables like smartwatches, and IoT devices. These devices receive signals from global positioning system (GPS) satellites, Wi-Fi hubs, cell towers, and beacons emitting low-energy Bluetooth signals several times per second. Locations of the devices receiving the signals recorded by these technologies are available as mobile location data.

    Ad servers also record the advertising id of the devices receiving their ads and provide information about which advertisement they receive, which websites they visit, and the time of the visit. These ads are geofenced, delivered in a virtual boundary around a specific field. The ad server data helps identify the area of the device, though not the accurate coordinates.

    What types of attributes should I expect?

    The main attributes are longitudinal and latitudinal coordinates, device IDs, and timestamps. The coordinates indicate the precision or granularity of the data. For example, coordinates of one decimal place cover a large city, while coordinates up to five decimal places can pinpoint a house or a store.

    As the data points are collected from devices, attributes can also include  IP address and country code. Data provided by ad servers can additionally offer the app and publisher IDs.

    The typical attributes include:

    • Longitudinal and latitudinal coordinates
    • Altitude/Elevation
    • Place name
    • Place ID such as road and building numbers
    • Category name such as school, store, or house
    • Category ID
    • Device ID such as mobile ID MAID
    • IP Address
    • Timestamp
    • Brand name
    • Brand ID

    How should I test the quality of the data?

    The quality of the different types of location data depends on the specific requirements. The low location accuracy of coordinates indicating a city can be sufficient for a city-wide advertising campaign. Updating the location of a vehicle in real-time, on the other hand, requires coordinates up to five decimal places.

    Besides ensuring that the data attributes and their level of detail match your requirements, you need to test data for the required timeliness. Location information of vehicles can change every second, while the location of a house does not change over a period of time. For targeted advertising, location datasets must be updated regularly.

    For data in real-time, choose a data feed that updates automatically over a defined period, such as every minute or every 20 seconds.

    For geofenced advertisements, a field test can confirm if the data used is of good quality. Visiting the area can validate that you received the targeted ad.

    To test the quality of the data:

    • Ensure that the data accuracy matches your requirements, and test the accuracy by comparing it with other data sources or data providers.
    • Verify that the data update frequency is appropriate for your use case, and that it gets continuously updated as defined.
    • Validate the data your geofenced ads use by field testing.

    Who uses location data?

    Emergency services use location services to find individuals in medical emergencies and people caught in natural disasters. They also use this data to search for missing persons.

    Transport companies use GIS data for navigation and tracking their fleet of vehicles. Marketers use location information primarily for geofencing and geo-targeted advertising. Business analysts use it for supply chain, demographic, foot traffic, POI, and location analytics. Financial service providers use this data for location verification and fraud detection. 

    Real estate agents and investors perform property intelligence initiatives before deciding the property price and leverage location data for property valuation.

    What are the common challenges when buying location data?

    The accuracy of the data depends on the requirement. Testing accuracy is easy by comparing with other data companies or verifying the working in the field. Data timeliness is critical, as the data can change rapidly and affect data-driven decisions. 

    • Data timeliness: The data gets rapidly updated when devices are on the move. For fleet management, as an example, a data update every 10-20 seconds is essential. For geo-targeted marketing, real-time updates are necessary to maximize the campaign results. It can be challenging to ensure that the data timeliness and update frequency match your requirements.
    • Data accuracy and completeness: When your use case needs IP address and other attributes for marketing purposes, ensuring location data completeness is difficult. The accuracy of these attributes will drive the effectiveness of the marketing strategy.  Any inaccurate or incomplete data can affect the ROI of the campaigns. When leveraging more than one dataset, it can also be hard to match the formats. 
    • Data precision and scale: Precision of data indicates the level of granularity, which in this case is the number of decimal places for the longitudinal and latitudinal coordinates. The coordinates of one decimal place indicate a city, while two decimal places can be a town or a village. The requirement of precision depends on the use case. For example, geo-targeted advertising needs 4 or 5 decimal places. The necessary scale of data also varies according to the intended use. Usually, larger volumes of data can power more accurate visualization and analysis, though a trade-off may be needed to avoid managing too much data. Verifying the precision and scale match the use case requirements can deliver trusted results.
    • Privacy compliance: Location data is classified as personal data when referring to device locations coming from mobile apps. It also contains sensitive and personally identifiable information (PII). Protecting personal data and ensuring privacy compliance for applicable industries and regions is a challenge for location data. 

    What are similar data types?

    Location data is similar to map data, satellite data, GPS data, points of interest data, and other categories of geospatial data.

    You can find a variety of examples of third-party data including geospatial data in the Explorium Data Gallery.

    Sign up for Explorium’s free trial to access the data available on the platform.     


    What are the most common use cases?

    Some common use cases for location data include location intelligence, geo-targeted advertising, and fleet management.  

    • Location Intelligence: It delivers insights derived from location data, and enables linking them to real-life cases.  For example, analysis of the locations a customer visits helps build their profile with habits and preferences. Marketers use location intelligence to trigger their advertising campaigns. Businesses use location intelligence to optimize store or restaurant locations. Insurers leverage location intelligence to verify insurance claims, and banks use it to detect fraudulent transactions.
    • Geo-targeted advertising: A strategy that leverages the location of visitors to deliver targeted messaging. Geo-targeted advertising identifies an audience in a specific area as most likely to buy specific products and services. For example, when a visitor is close to a store, geo-targeted advertising can deliver promotional offers to encourage them for a visit. A restaurant in a shopping mall can detect visitors and leverage the opportunity for geo-targeting them with menu and discount offers.  As a strategy, geo-targeted advertising focuses on a smaller group and generates higher conversion with a better ROI.
    • Fleet Management: Transport companies need to optimize their fleet of carriers for better ROI. They use IoT (Internet of Things) devices to track their vehicles and leverage the information for multiple cases. Location data helps them schedule and reschedule shipments, estimate the delivery time, confirm the routes taken, and provide emergency assistance. In addition to transport companies, schools, military, police, and emergency services also use fleet management to provide timely help and ensure safety.  

    Which industries commonly use this type of data?

    Industries that need to track devices use location data. They include transport, logistics, and shipping companies. Other industries using location data for marketing include retail, CPG, entertainment, tourism, travel, hospitality, sports, leisure, CPG, healthcare, financial service providers, insurance providers, and banking.   

    How can you judge the quality of your vendors?

    Vendor quality drives the success of the projects, including timely data collection, delivery, quality, ability to match your requirement, ease of integration, and committed engagement. You can judge the vendor quality using several methods, such as information available on the website and direct interaction with the vendor reps.

    • Customer reviews and ratings: Most of the vendors present customer reviews and ratings on their websites. The reviews describe customer experience with the vendors, indicative of the quality of the vendors. They sometimes also provide information on the industry vertical and the type of the project. The ratings signify the overall experience and are a good measure of vendor competency. You can leverage these details to assess if your projects can benefit from the vendor capabilities.
    • Customer testimonials: Besides customer reviews, the customer testimonials highlight the strengths of the vendors. You can use this information to evaluate if these strengths will be valuable for your projects.
    • Case studies: With a more detailed discussion of customer projects, case studies help you analyze how the vendor manages the dataset requirements and quality. Particulars of the industry vertical, project size and duration, challenges, solutions, datasets used, and integration assist in evaluating the vendor suitability to your projects.
    • Demo: The best way to understand how vendor data powers a successful project is to watch demos. Vendor websites often provide demos or a request form for demos. Sometimes vendors can also arrange a live demo for you. In all cases, watching data in action clarifies quickly how the datasets are used to derive actionable insights. Demos also demonstrate the vendor’s ability to customize datasets and assure quality. 
    • Interacting with vendor reps: Once you have shortlisted vendors using the information available on the websites, interacting with vendor reps can finalize the selection. You can use the opportunity to discuss your project, timelines, quality requirements, and the required range of attributes. During the discussion, you can also judge if the vendor can deliver the expected engagement level.