Location Data: Best Location Datasets & Databases

The past two years have proven that spatial intelligence is crucial for business, government, and urban development. Reliance on location data for strategy and analytics is only set to grow in 2023. We’ve gathered the latest updates and trends on the future of location data - its sources, market size, and increasing power as a resource for good.

Key Points:

• The global market for location intelligence set to reach $29.90bn by 2026, with a predicted CAGR of 16.1% for 2022-2027
• Increased connection to 5G network driving up supply of real-time mobility insights as number of devices connected to 5G forecast to hit 1.34 billion in 2023
• Location intelligence is growing more versatile, driving success in diverse use cases: “We’re just an ingredient in any one solution. It’s like selling high-quality butter to pastry chefs. The end consumer of the croissant may not even know that there’s butter in the pastry. But the chef knows how important the ingredient is,” - Auren Hoffmann, CEO, Safegraph, leading geospatial DaaS company.

Why Location Data matters

Location data is one of the most important external data categories available. 80% of external data collected in today’s data ecosystem has a location component. This is largely because it’s so versatile - it has an endless number of use cases and market applications.

Understanding the physical world and how people are moving around it provides the core analytics for a plethora of industries and organizations. International corporations, emerging startups, as well as governments and NGOs - all are investing in location data to shape their strategies around movement. And by ‘movement’, we don’t just mean how the individual gets from A to B. Location datasets also cover the flow of transport and goods through the supply chain, across seas, airspaces and territories. These data points enable users to monitor mobility trends and map POIs (points of interest).

Here’s just a few examples of how location data is underpinning decision-making in commerce, urban planning, and government:

Location Data for commerce: from price-pointing to geo-marketing

Location data enables companies to shape effective strategies throughout the sales cycle. Retailers rely on location data to identify where to build brick-and-mortar stores, taking local demand, footfall, and competition into consideration. Analyzing average consumer spend and affluence metrics in a given area, businesses can optimize their price points from location to location.

Airbnb deploy pricing algorithms which determine rental property prices based on local amenities and transport connections. And they’re not alone: some of the most successful online and app-based retail and hospitality startups are utilizing location data to drive ROI.

US food delivery service DoorDash built its product around customer hotspots, restaurant availability and driver routes in a targeted location. It’s a strategy which has earnt DoorDash over 56% of the market share.

Location-based marketing is becoming increasingly innovative. With location data, digital marketers can build customer audiences and segments according to real-life visits, as opposed to digital footprints alone. Geofencing and geo-triggered advertising allow marketers to time the delivery of ads and pop-ups around the consumer’s physical location and POIs. Combining offline and online behavior, location data means marketers can deliver a more relevant and cohesive brand experience.

Location Data for urban planning: making cities smarter

The number of smart cities globally will increase in 2023. Last year, Germany invested €790 million in improving quality of urban life. Smart cities use real-time mobility insights to shape urban infrastructure around the movements of city-dwellers via connected device networks. This intelligence allows planners to produce digital landscapes which are updated according to mobility trends.

Aside from smart cities, developers are building more accurate population density models based on GIS insights. Census records only provide data every decade; economic reports tend to be published once a quarter. Mobile and satellite location data overcomes this information lag. As a result, companies in the urban development, civil engineering and real estate sectors can make informed geospatial decisions based on ultra-timely information.

Location Data for government: optimizing crisis response

COVID-19 demonstrating the importance of location intelligence. Understanding foot traffic patterns underpinned policy-making, with location data provider SafeGraph sharing their data with the CDC to improve the governmental response to the public health crisis. Location data is also helping fight the climate crisis. Scientists rely on ultra-precise satellite imagery to track the movements of polar and monitor changes in spatial conditions, from expanses of rainforest to individual crop fields. The same satellite data is also used to prevent humanitarian crises by predicting natural disasters.

So location data is a tool with many uses. It’s only going to become more versatile, as the information in a location dataset becomes more varied. Let’s have a look at the location data attributes of today, and the new attributes being added to datasets.

What Location Data looks like now, and the new attributes it’s acquiring

Of course, the basic attribute of any location dataset is geospatial information. This is given in various units: lat/long address, visual heat maps mapping foot traffic, or IDs, placekeys and geocodes specific to the provider. Other attributes include metadata about a location, such the building height, local amenities, and consumer footfall in the surrounding polygon

Across the external data industry, location data services are becoming increasingly tailored to each data client’s needs. Though unstructured, generalized mobility datasets are still available and valuable to many clients, most data providers now offer custom location intelligence, with attributes of the client’s choosing. For example, vendors will compile data points according to a specific geography, whether a country or ZIP code. This saves time in data cleaning and analysis.

To enable clients to unlock value from location data, providers enrich it with other data points. Connecting location intelligence to demographic data points, for example, enables marketers to build more detailed customer profiles. Similarly, location data providers are connecting sources of mobility intelligence and offering them as one unified service. Spatial intelligence businesses are enabling their clients to access insights into car movements, flight patterns, and consumer mobility via one integrated data platform.

On top of this, location data is becoming more accurate, granular, and faster to access. This is largely because 5G networks detect device movements spooner and to a greater degree of precision. The signals produced by 5G-enabled devices correlate to movement between individual POIs from locations as small as an ATM or bus stop.

Lastly, location data is becoming digestible to the everyman, not just the data scientist. It’s crucial that location data can be put to use by all organizations without the need for resource-intensive data analysis teams. To democratize the value of location data, providers are unifying attributes of mobility data sets, and ensuring that datasets are ready to be integrated into the client’s existing technologies. This way, startups can reap the rewards of location data just as readily as companies with big data budgets.

On this note, let’s have a look at trends in the location data market: who’s buying, who’s selling, and how much location data costs.

The market for Location Data in 2023: supply, demand, pricing

As ever, tech companies are leading the pack when it comes to driving demand for data and AI. The same is the case for location data. Nonetheless, others are catching up. CPG companies like Burger King are harnessing location data for location-based marketing, including a geofencing campaign against rival McDonald’s in 2018.

In terms of supply, startup provider companies are leveling the playing field. High-quality, privacy-assured location data is becoming available open source for the benefit of all. External location datasets are available to buy at more affordable prices. This is thanks to the rise of the data marketplace, which has seen companies join data commerce platforms to turn their proprietary location data into a viable revenue stream.

To understand location data supply, it’s worth taking a deeper look at today’s location data providers to find out the ones you need to know about.

The Location Data-as-a-Service Companies to have on your radar

There are several big players in the location analytics market, including Microsoft Corporation, Google LLC, IBM Corporation, SAP SE, Cisco Systems, and Hexagon AB. These companies have sophisticated data collection and storage technologies, so have a wealth of location intelligence.

However, most suppliers of location data are younger companies, found on data commerce platforms and data marketplaces, like Datarade’s Data Commerce Cloud. Here, you’ll find mobility data leaders like Lifesight, SafeGraph, and Geolytica. These data vendors specialize in delivering location intelligence at scale, on-demand, and tailored to a specific use case.

Many location datasets and APIs are instantly purchasable, with datasets varying in cost according to data coverage and recency. Providers with products available to buy instantly and effortlessly are disrupting the location data ecosphere, making location data commerce more transparent and accessible to all.

If you’re still with us, it’s time to get a bit more technical as we look at methods of location data sources and collection - the traditional, and the up-and-coming.

Location Data sources & collection - what’s changing in 2023?

Mobile location data is continuing to be collected as SDK, Bluetooth and cellular signals are emitted by devices and received by beacons, satellites, cell towers, and wifi routers. However, these traditional methods of data collection are getting more advanced. As 2023 welcomes the next generation of Wifi connectivity, AKA Wifi 6, each router’s capacity for connected devices will increase 4x. Wifi 6 will deliver faster, more stable connectivity and communication, and for location data, will enable device tracking and positioning to operate faster and at higher capacity.

Additionally, as more devices are added to the IoT, more location signals are collected. Bluetooth-enabled devices, from health monitors and smart speakers, are constantly producing location intelligence. This information forms the basis for real-time POI mapping and footfall attribution. The greater the quantity of devices, the clearer picture of mobility becomes.

The job of the data provider is to take this mass of location information and make it digestible for the non-data-scientist user. Which leads us to our final topic: how can you make sure that your future investments in location data are high-quality?

How to tell the good data from the bad

When you’re looking to buy location data or run a data sample through your quality checks, the following criteria are needed to assure you’re getting a quality product:

Relevance

For location data, obviously, relevance means getting the data covering the region you’re interested in. Scale and structure also come into it - how much data coverage do you need? Do you want the data in aggregated or raw, unstructured format? Always define your data requirements in detail and ahead of purchase so that you can ensure your prospect provider fulfils your needs.

Range

Here, we mean both spatial and temporal range. Certain providers only cover specific geographic regions, whereas others provide worldwide coverage. Some vendors specialize in given locations, buildings and landscapes, so can provide the most granular spatial insights in these areas. Temporality is important, particular for measuring footfall. Depending on your use case, you might require a real-time data feed, or historical records of movement. What’s most important is that, whether your temporal requirements, the data provider delivers the product needed.

Responsibility

Location data is a force for good. As we’ve seen, it can tackle health and humanitarian crises, and improve our natural and urban environments. Location data collection must be ethically-sound so as to not overshadow its amazing applications. Quality data providers will make privacy a priority. Sensitive data should be anonymized and all data collected from mobile devices must be opt-in. Before buying location data, always check that a provider’s collection methodology, privacy procedures and company mission statement are in line with relevant legislation and ethical guidelines.

Roundup - mapping the future of Location Data

Datarade is partnered with hundreds of leading geospatial DaaS companies who are driving the innovation of location intelligence. Using our data industry intelligence, we’ll keep this guide updated with market insights and emerging use cases. Going forward, it’s clear that location data is set to become more important, more valuable, and for more people in 2023 than ever before.

Your Map to Geospatial Success: The Ultimate Guide to Location Data in 2023

Whether you’re looking to purchase location data, enrich the data you already use, or discover why geo intelligence is creating such a buzz among businesses across the world, you can rely on our Ultimate Guide to Location Data in 2023 to tell you everything you need to know.

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To help you conceptualize technical terms and scenarios, we’ve included use cases that are interesting and contemporary examples of what a location data company can bring to the table, and how truly versatile location data is.

Having access to the internet virtually wherever we go has revolutionized modern commerce. Smartphones have become an extension of ourselves – and it’s estimated that 3.8 billion people in the world now have one. That’s almost 48.46% of the world’s population. As such, smartphones can reveal a lot about our habits and behavior – both online, and when we’re out and about in the physical world.

It’s never been simpler to search for restaurants, shops, utilities and services around us. To make sure that these services are findable when you need them, businesses are turning to location data to better understand the demands of consumers in a given area. Location-based marketing and advertising are rapidly becoming the primary means of planning campaigns and targeting consumers, with both traditional and digital marketing strategies benefitting from positioning technology and footfall traffic analysis.

But let’s start with the basics: what actually is location data?

What is Location Data?

Location or geolocation data is a sub-category of geospatial data . It is information relating to the specific geographical location of an object, whether that be an electronic device or a building. Just a few examples of these:

Devices - mobile phones, tablets, laptops

Buildings - restaurants, retail stores, shopping centres, business HQs

Other structures - historical monuments, landmarks, train stations, airports.

What does location data look like?

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The most common attributes of location datasets are latitude and longitude (lat/long), usually expressed in coordinates which correlate to a geographical position. Other attributes include:

• Altitude/Elevation – The height of the object or structure above sea level. Usually expressed in metres above mean sea level (MAMSL).
• Mobile Ad ID (MAID) – A term for strings of hexadecimal digits assigned to smartphones by Apple or Android. They work like cookies, in that they identify a user to ad networks, and can detect the user’s location, as well as behavior and demographic profile.
• Internet Protocol (IP) Address – A numeric label assigned to every device linked to a computer network that uses Internet Protocol. It’s used to identify the location of the device – though not always entirely accurately, as we’ll see later.
• Timestamps – These are used to understand the context of the movement of a particular device, and can report either single occurrences or a sequence of events. Context-aware timestamps are commonly recorded in Unix time (a.k.a Unix Epoch time, or just Epoch).
• Place ID - These are numerical location indicators, which correlate to specific buildings, road junctions, or monuments, among others. They are given as a series of numbers that can then be put into Google or other mapping services to indicate a location.
• Place Name - There’s not much more to it than that! One attribute to a location is simply its name, whether that’s the name of a building or piece of land such as a park.
• Brand ID - This is the registered identity number of a brand or business at the point of interest.
• Brand Name - This is the name of the brand or business at the point of interest. As shown in the table below, KFC is the brand name at the point of interest in this example.
• Category ID - A numeric label assigned to every mapped location, which indicates the category to which the location belongs.
• Category Name - This is the name of the category to which a location belongs. For example, school is a category name and so is theatre.

This information can be displayed as a data dictionary like this:

How does Location Data collection work?

There are many different ways to collect location data. Its sources are widespread: locations can be calculated with the help of objects like vehicle fleets, wearable devices, shipping cargo, among many others. However, in this article, we’ll be looking primarily at mobile or cell phone location data. For marketers aiming to target customers’ locations more precisely, in 2023 they are most likely to rely on cell phone location data, due to the ever-growing usage of smartphones. So we first need to understand the device which is responsible for generating most location data today – your smartphone.

What role does my smartphone play?

In the most basic terms, your smartphone can generate data about where you are and what you’re doing there. This is mobile location data. In this sense, the device acts as a proxy for its user, providing an insight into the user’s behavior, habits and intent. The data from one smartphone can be used to represent one customer and their behaviour. We’re creatures of habit and, for many of us, our weeks follow a routine, with weekends offering more variety to our movements. This weekly routine helps to build up a profile of our behaviours and teaches companies when and where we are most likely to open up our wallets.

“Although we often think of smartphones as transmitting signals, in this context they’re actually receiving them”

Smartphones function according to their operating system (OS), which is unique to their manufacturer, and is responsible for ‘telling’ them how to work. Each mobile OS, like iOS or Android, is associated with an identifier: for Android devices, this identifier is the Android Advertising ID (AAID), and in iOS devices, it’s the Identifier for Advertising (IDFA). Identifiers help us understand the movement of a device over time. How do they do this? Well, as the name suggests, identifiers identify what is receiving the signals which are produced from other technology. These signals are transmitted by external sources and are then received by smartphones.

What are these signals and where do they come from?

Collecting mobile phone location data requires two components: a signal and a receiver. Although we often think of smartphones as transmitting signals, in this context this isn’t the case. They’re actually receiving them, and as such, are often called receivers. The signals can be transmitted by the following:

• GPS Satellites – GPS (global positioning system) works using the 31 GPS satellites which orbit the Earth. Satellite data is another branch of geospatial data. Each satellite transmits signals which are received by a device, and the device’s location is determined by calculating how long the satellite’s signal took to reach it. GPS can calculate outdoor locations accurately to within a 4.9 m radius when the sky is clear. Location accuracy is reduced indoors, underground, and in places with tall buildings and trees. However, for the most part, GPS location suppliers provide a reliable understanding of device location that is broadly the same for all mobile users.

• WiFi Routers – WiFi performs significantly more accurately indoors than GPS. It determines the location of a device, like a cell phone or laptop, by calculating the distance between the device and an ‘access point’, which is what allows devices to connect to a local WiFi network. The data generated is granular, accurate between 10-100 metres, and is especially useful when GPS and cell tower signals aren’t available. However, it’s common for users to register to public WiFi with fake email accounts, so the device may not always represent the user.

• Beacons – A beacon is a piece of hardware which transmits information via a Bluetooth signal, which is picked up in the device by a software development kit (or SDK – a piece of software built into an app. We’ll look at SDKs in more detail shortly). Beacons can calculate location data to a very granular level, and can even place a consumer in a certain shopping aisle of a store, but the scale they offer is limited: they can only emit Bluetooth signals in the locations they’re installed. However, as demand for Bluetooth location services is expected to grow 10x by 2022, and with more Bluetooth devices (think Apple Airpods, wireless headphones and the like) to function as proxies, problems with scale could soon become a thing of the past.

Each signal transmitter comes with its own pros and cons, and their usefulness depends on the requirements of the user. Generally speaking, the location accuracy they generate increases when more than one transmitter is used, or when the mobile location data is supplemented with point of interest (POI) data, which tells you about a specific location (we’ll look at POI location data in more detail later), or GIS (geographic information system) data.

How to collect location data?

Location can be calculated using the longitude/latitude of a mobile device. Most devices are GPS-enabled, which, when used in conjunction with Wi-Fi, IP address, and cell triangulation, can produce locational coordinates which we call ‘location data’. With this method, it’s possible to determine both historical location data as well as real-time location data of an individual or an object. It is estimated that there are over 770 million GPS-enabled smartphones worldwide, meaning it is now possible to collect location data through GPS on a large scale.

Still with us? Okay, now that we understand what location data is and the methods used to collect it, let’s look at the different sources of mobile location data.

What are the sources of location data?

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Software development kit (SDK) - What is a location SDK?

Remember the SDK we mentioned above? It’s a set of software tools built into an app by its publishers. SDKs can monitor user behavior, transactions, app performance – and, importantly for us, location. SDK location data is offered by the majority of location analytics companies.

Some SDKs use the OS alone to provide a precise location for the device being used, but others optimize this with additional analytics. A location SDK requires the user to give permission for the app to access their location, which is done by the app’s API (application programming interface). This access can be constantly on in the background, like with weather apps or Apple’s ‘Find My Friends’, or just when the app is open, like a food delivery or taxi app.

SDKs can perform a wide range of functions, but this means that the data reliability varies from one SDK to the next. The most accurate SDKs can listen to multiple signals, for example GPS and Bluetooth from beacons, and combine these signals to generate the device’s location throughout the day within a metre of data accuracy.

Because SDKs are codes built into apps, the scale of the SDK location data they provide depends on how many devices have that app. Constant tracking of location requires a lot of the device’s battery power, which prompts many users to turn off the location capability. Only when a considerable number of devices have the app downloaded and location-tracking enabled can the geolocation SDK data be analysed for patterns and insights, otherwise the data can’t represent a sizeable enough chunk of the smartphone population.

Bidstream/ad open

To understand this source of location data, let’s have a quick look at how ads are traded online:

Digital ad inventory: Explained

Ads are traded online in three ways:

• Direct deals with the creator of the app, website or social network.
• Ad networks gather ad inventory according to certain categories, then sell the inventory to advertisers.
• Ad exchanges allow ad inventories to be traded in real-time as advertisers bid for them. This is the most common way of trading ads today, and is known as the ‘bidstream’.

The first two ad trading options don’t generate any location data. However, the ad exchange, or bidstream, does. When deciding whether to serve an ad on a device, advertisers consider the following device attributes (amongst others) before making a bid:

• URL
• Device type
• IP address
• Connectivity (WiFi, 3G, 4G)
• OS
• Timestamp
• and, fortunately for us, location!

All of this information is parcelled up in the ‘bid request’. So, device location data could be seen as a ‘by-product’ of real-time bidding (RTB), which we call bidstream data. Bidstream data is accessible to any company or organisation to a demand-side platform (DSP). This is both a blessing and a curse, because the data is out there waiting to be mined, but, of course, your competitors will likely have the same idea.

The scale of bidstream data is vast, and can be accessed and implemented immediately, because it’s collected programmatically by the IAB’s protocol ‘OpenRTB’ – millions of bid requests per second, in fact. OpenRTB is also responsible for policing how recent the data is. By looking at the data fetch timestamp, you can decide if it’s recent enough.

The main downside to bidstream? How unreliable it is.

Approximately 60% of ad requests contain some form of location information. Of these requests, less than 1/3 are accurate within a 50-100 meters of the specified location– Foursquare, Thinknear, Ubimo, xAD, Inc.

The problem is how the location in the bid request is derived. It could be GPS (a ‘Type One’ source, meaning it’s more accurate), but equally, the location could be based off user-input data or an IP address, which aren’t always accurate.

For example, you might be in London and search for restaurants in Paris on your smartphone, which could cause the bidstream data to incorrectly give your location as Paris based on your manual activity. This is where user behavior and intent don’t always correspond to their location. Similarly, IP addresses rotate every few months, making it difficult to use them to accurately track location over a longer period of time. So with bidstream collection, there’s the risk that your location intelligence data is inaccurate. There are also concerns that bidstream data could be fraudulent, or that publishers could give incorrect source information.

Telcos

We’ve looked at how cell towers can generate location data. Well, this data can be obtained directly from telecommunication companies (telcos). The same pros and cons apply to this source as to the cell tower triangulation method of collecting it – great for scale, but not always precise. The data available from telcos depends on the size of the telco in question. Some telcos are responsible for supplying signals to most of a country’s population - this allows you to access location intelligence at scale. In fact, the scale of the data offered by telcos makes them a hugely attractive location data source.

Publisher datasets

Location data can be collected by app publishers themselves. If the app has inbuilt location services (like searching for a taxi from one city to another, or the option to click ‘stores near me’) then this information can be converted into a device location. As with all user-input data, the data generated won’t be as accurate as data collected using signals, unless you can verify the method used by the publisher.

So let’s recap. The method? Mobile location data is collected by various signals which are received by your smartphone. The source? SDKs, bidstream, telcos, and publisher datasets.

We’ve looked at some of the pros and cons of each method and source – but how can we use this to ensure that the location data we use is of a high quality? Just like with the transmitters we discussed in part 1, the best way of ruling out error and getting the clearest picture possible is to consult multiple data sources, and compare data providers, products and datasets. Don’t put all of your eggs in one basket!

But before you invest in any source at all, it’s crucial to determine the quality of the data you’ll be provided with. So, our next step: data quality control.

How can I assess the quality of location data?

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Low quality data limits how useful it is – don’t let bad location data send you in the wrong direction. It is important to invest in high quality location data. Let’s examine the components which determine quality.

Before we begin, it’s important that we understand the difference between three terms which we’re using to judge location data quality: accuracy, precision, and scale. It’s tempting to use them interchangeably, but they’ve got specific meanings which are worth considering. Fortunately, these don’t take as long to explain as some of the technical jargon which location data tends to bring with it!

• Accuracy – refers to how close the measured location is to the true location. The closer the measured location, the more accurate the data.

• Precision – refers to the level of detail (in terms of how many decimal places the lat/long coordinate has) the measured location provides. For example, coordinates of two decimal degrees can place someone in a town or village, whereas coordinates of six decimals degrees can pinpoint individual people. The more decimal places, the more precise the data is. Explained another way, you could say a retail store is located on Oxford Street, or in the borough of Westminster, or in the city of London. All three statements are accurate, but indicating the street offers the greatest precision. The number of decimal points to which a piece of location data is given indicates how precise the data point is. Data precise to 5 decimal places (0.00001 or 0°00’0.036”) offers precision to the degree of identifying a specific door entrance on a street or an individual tree.

• Scale – how much location data a source offers you to analyze. The greater the volume of data, the greater the scale. Scale of location data varies between location data companies.

As with all types of data, location data assessment necessitates a balancing act between the three to ensure your source provides high quality insights and a deep understanding of consumer behavior. Sometimes accuracy is most important, other times it’s all about precision (we’ll go into different scenarios soon).

It’s also worth considering how fresh the data is (recency), and whether it distinguishes frequent visitors from new ones (frequency):

• Recency – how long has it been since this data was retrieved? Will this affect whether the device is in the same location or not?
• Frequency – how often is the individual visiting a specific location? Are they a regular customer, or a first-time visitor?

Another thing to consider: consumer location data can be susceptible to fraud and fakery. Bid requests can be fraudulent, and as we discussed in part 1, fake email addresses are a problem associated when WiFi is used as a signal transmitter.

The steps to ensuring location data is of the best quality possible can be bewildering. Don’t be deterred – we’ve created a checklist to ensure you’ve covered all the bases of data quality assessment:

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In which scenarios would you value one aspect over another?

The quality factor you deem most important depends entirely on your needs. Effective location-based marketing takes into account the demands of your business and campaign. For example, if you’re interested in the locations of people across a network of towns, you’d probably sacrifice precision in favour of scale. Conversely, the vast scale offered by bidstream data is unhelpful if you’re trying to construct a buyer persona within a precise demographic, like a specific neighbourhood.

Or, if you’re installing beacons in a shopping mall, you’re probably doing so because they’re more accurate than GPS, even though the scale offered by GPS is greater. Sometimes recency isn’t an issue – if you’re analyzing historical location data to spot movement trends, then real-time locations aren’t needed in the way that real-time is essential for delivering in-app ads to a device which has travelled from one city to another. But if you’re using location data for retail site selection, you need the most up-to-date data to ensure you have the latest catalogue of the stores in an area.

It probably goes without saying, however, that fraudulent and fake data are always good to avoid, where possible! Before you buy location data, you should expect your provider to take all the right precautions to remove bad data from their sets and sources.

All of these scenarios draw on data from a location intelligence platform to enhance a business’ understanding of where their prospective customers are and how they behave there. Let’s turn to some examples of how location data is used in the business world – and beyond.

Who is using location data?

Location data collection isn’t a new phenomenon – it’s the same basic information that’s collected whenever you enter where you live or work, so that marketing material can be sent to you. The insights provided by location intelligence is relied upon for a variety of use cases, from public planning and infrastructure, to fraud detection and location verification. What’s really getting people talking, however, is how mobile location data has transformed digital marketing and advertising.

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Marketers - Location-based Marketing

As we know, how you market a product depends entirely on context. Knowing where your prospective customers come from and travel to increases the detail of your buyer persona dramatically. If your task is to market an athletic clothing brand, a lot of the market research you’d do is linked to location. Where does your buyer persona live – in the city centre or the suburbs? Where do they exercise – do they travel to a gym, or stay near their home? How much time do they spend there? Which brand competition would you face in this area? Would the average income of that location affect how you price the product? What are the opportunities for opening stores or running ad campaigns there? The answer to all of these questions about behavior, interests, intent, brand affinity lies in location intelligence, which is why so many marketers are choosing to buy store locations’ data. With a location dataset, you can roll out location-based targeting initiatives. Location-based mobile marketing allows marketers to drive up conversions and ROI, whilst gaining a clear insight into consumer behavior.

For the digital marketer specifically, understanding how prospective customers use their smartphones in an area is vital. If the gym-goer uses a fitness app, then in-app ads for athletic clothing are a good idea. If they go and buy a coffee on the journey home, you might spot an opportunity to collaborate with the coffee shop or brand, like offering a discounted coffee if they purchase yoga pants. With location-based marketing, the possibilities are endless – context-aware location data allows marketers to be as creative and innovative as they want to be, safe in the knowledge that their decisions are backed up by facts.

Advertisers - Location-based Advertising

The global location-based advertising market had a value of USD 62.35 billion in 2019 and this is only expected to keep growing. It is forecast to register a compound annual growth rate (CAGR) of 17.4% between the years 2020-2027. Location data has opened up countless new ways for advertisers to communicate with prospective customers. It allows them to create campaigns based on wherever the customers go and what they do there, not just where they live.

Knowing a customer’s visitation habits, you can determine which media platform is best suited to your campaign. If you can see that your buyer persona takes the bus to work and uses Facebook during the journey, it’s probably more effective to create in-app ads for your brand rather than physical billboards, which your customer won’t be paying attention to.

Location-based marketing companies also fine-tune their brand message based on location data. If you notice that your buyer persona frequently visits the cinema, not only could you place adverts there, but you could shape your campaign so that it’s in line with the latest blockbusters.

These location-based examples may sound far-fetched, but marketers and advertisers are having to create increasingly inspired solutions as both our physical and online world becomes more packed with content. All sorts of social and cultural factors come into play as a prospective customer makes their way down the sales funnel.

We’re used to hearing about data in a marketing and advertising context. But is location data only useful for businesses? Not at all – it can transform how supply chain managers control their stock, how finance analysts invest their money, even how governments are run. That’s the beauty of such a versatile data type:

Retailers

Retailers of all sizes are using store location data for retail analytics, including customer behavior, store visits and point of sale locations. Stores location data also allows brick and mortar store to benchmark their performance against competitor stores in the area. Visit data helps increase revenue by adjusting opening times, stock orders and staff numbers according to footfall and peak times, and by knowing which storefronts and layouts create interest and result in more purchases. What is being used here is known as data-driven forecasting. This is a technique being used more and more by businesses as a way of optimising their efficiency.

For example, location data can indicate to a retailer that footfall is highest in the lead up to Christmas. Retailers can then use this knowledge to increase their staff numbers and anticipate a surge in sales. Just as a business may use this location data to increase their staff numbers at busier times, they may also cut staff numbers at times they know are less busy in order to ensure that they are not overstaffed and, therefore, less efficient. Retailers can make huge savings just by applying the knowledge gained from the purchase of location data to their operations.

Hotels and Malls

With larger buildings, you need to know where visitors spend the most time and how they get there. If you oversee a hotel’s operations and notice that guests spend a long time waiting in the lobby, this raises concerns about how likely they are to then visit your restaurant, so you would make changes to how the reception is managed. For a mall, you could rearrange the layout of the building to subtly encourage customers and guests to venture further inside. If your most popular attraction according to foot traffic measurement is the food court, you’d consider putting this at the centre of the mall, or improve signage to other stores and attractions.

Finance Analysts and Financiers

Location data is a vital tool for finance analysts. It helps them detect criminal activity and guard users by adding a robust security layer. Financers can use location data to predict earnings in line with an area’s inflation level, KPIs, and the number of customers based on previous footfall. All this helps them make the best investment decisions.

Real Estate Investors

Real estate investors also use location data to understand how busy specific regions are, how well local businesses are performing, and what the general demographic in that region is. Increasingly, real estate analytics use POI data in property valuation to determine how desirable the property’s location is.

Government

Governments use location data to understand how different cities and towns function independently and are connected to one another. It helps them craft better public infrastructure, based on POIs and traffic analysis. For example, location data can tell them which roads cyclists use most often, so that they can plan new cycle lanes, or where traffic is most congested, so that bigger cities can introduce low emission zones.

As more people are making the switch to electric vehicles local councils are turning to location data to inform the placement of charging points across cities. From September 2019 to February 2020, Oxford City Council relied heavily on location data collected across the city to select a road to trial the instalment of six prototype charging points. The data allowed the council to take the city’s narrow streets and pavements into account. Smart motorways offer another relevant example of governments’ use of location data. As of 2020, smart motorways covered more than 400 miles of England. Smart motorways use data collected from cameras to detect how congested a stretch of motorway is, allowing the hard shoulder to be used as an extra lane or variable speed limits to be implemented in order to limit congestion.

Shipping and Haulage

GPS location data is vital for asset tracking - in other words, ensuring that goods and services are transported safely from A to B. Similarly, satellite location data and real-time traffic reports can help logistics and distribution teams plan the most efficient routes. Not only is it becoming more commonplace for customers to track their online orders to ensure their parcels arrive safely, but many companies are now offering customers the option to decline a delivery time in order to ensure they are at home when the package arrives.

For example, DPD now informs their customers of the time slot in which a parcel is scheduled to be delivered and gives them the option to redirect their parcel or request for a delivery on the following day. This allows DPD to increase their delivery service’s efficiency, reducing the number of undelivered parcels. Similarly, takeaway services such as JustEat utilise location data for route optimisation. By collecting the addresses of customers and the restaurants from which they are ordering, takeaway services can allocate the delivery driver in the most opportune location to ensure a fast delivery to the customer.

Travel, Tourism and Hospitality

Travel companies and hospitality services such as hotels rely on location data to gain an insight into customers’ movements and travel preferences. By engaging with this data, it is possible to target advertisements in a way that is likely to attract regular custom. One thing the Coronavirus pandemic has made clear is the necessity for companies to attract loyal customers in order to ensure not only their success, but also their survival. By asking themselves where customers travelled before and during the pandemic and how cautious they feel in regards to travel, travel companies are able to tailor advertisements to their customers. Not only are they able to gauge what sort of trips are the most desired but they are also able to reinforce their marketing with the assurance that health guidelines are being adhered to, with strict sanitation measures in place.

So location data isn’t just an asset for marketers. It has the potential to improve where we live, and how. This brings us to part 5: how online data is used in the offline world.

How does online location data translate to offline scenarios?

Although e-commerce is booming, over 90% of transactions in the US still take place in a physical store. How can we ensure that the fantastic online capabilities of mobile geo location data generate in-store results? If we’re too caught up with catering to an online audience, we risk alienating customers in the physical world. Remember the POI (point of interest) data we mentioned way back in part 1? Well, this data holds part of the answer:

• POI Data: Explained – Point of interest data is used to identify places by their use and function, as well as by their postal address or location. They make it easier to mark important places and landmarks on a map. POI locations are places that are useful or interesting to consumers, like hotels, restaurants, retailers, campsites, fuel stations, a heritage site, or a corporate office.

Advertisers and marketers use POI data to understand where campaign-relevant consumer and device activities occur. The data represents locations in the real world where mobile activity can be measured. When used with location data, POI analytics offer additional context, allowing advertisers and marketers to best assess why consumers go to certain locations.

So, a POI dataset works in symbiosis to your location dataset. Let’s say you’re launching an app which helps students revise. Your instinct might be to put billboards and street promoters around the city and university libraries. But what if your POI and location data shows you that actually there’s a greater concentration of students who work in coffee shops. The smart thing to do would be to roll out an ad campaign which focuses around coffee shops and cafes – your audience’s POIs.

What about converting online interest to in-store purchases? When a smartphone user searches for something on Google, or follows an in-app ad link, this indicates their interest and intent. If marketers can spot a pattern between what interests them online and where they spend the most time in the physical world, there’s a gap to be filled. If you’re a car retailer, then Google location data can detect when people search for second-hand cars online and use their location to serve ads and pop-ups for local car showrooms.

It’s often in the user’s best interest to allow an app or website access to their location. Their user experience is optimized as a result. In a recent survey, over 40% of smartphone users claimed that they are more likely to use apps that personalize in-app content by location. Google in particular uses the location data from Google account holders to give personalized, relevant recommendations based on places they’ve visited, real-time information about when the user should leave home to avoid traffic, and create albums in Google Photos according to where they’ve been.

This is how we can connect online data to business operations on the ground. But what are the exact strategies that enable location data to yield results?

What is Location Data used for?

There are countless marketing and advertising strategies involving geographic targeting. They all boil down to tracking mobility, analyzing patterns, and targeting the areas which will boost your ROI and improve your understanding of your customers for segmentation.

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You can buy cell phone location data for the following use cases:

Footfall Traffic Analysis – This could be seen as the ‘first step’ towards utilizing your location data effectively. The data uses a footfall tracker to show you trends in footfall, such as which locations are popular, and whether this varies over time. From this, you can create location visualization tools, like ‘heat maps’ which estimate where the best locations for ad campaigns are. You can create additional heat maps based on the peak day or time of day for footfall, and maximise the exposure of your campaign. Good location data will distinguish between employees and passers-by by considering the ‘dwell time’ of the device, so your understanding of footfall isn’t skewed.

Attribution Analysis – This comes after footfall tracking and analysis. By knowing how many people visit a location and what the peak visitation times are, you can direct your campaigns on this basis. If you see that footfall increases after you’ve trialled an in-app ad or promotion, then you can roll this campaign out further because the footfall indicates a good ROI. Attributing footfall is increasingly relevant for businesses which offer both in-store and online purchases. If an ad campaign is generating interest, then why are you selling more of the same products online than in person? If you can track the customer’s physical journey as well as their online interactions, you can see what’s preventing them from making in-store purchases, and alter your storefront, billboards and store layout accordingly.

Original Destination Study (O-D) – An O-D Study can show you long-term patterns about how people travel. It relies on GPS, and is also used in road and infrastructure planning. Knowing where people travel to and from, you can place billboards and visuals along popular commuter routes and where the flow of traffic is most heavy.

Out-of-Home (OOH) Analysis – OOH refers to any media you’ll see in the public sphere, including malls, stores, train stations and airports. The media can be billboards, signs, TVs and posters. Being able to analyse how devices move in relation to the OOH media they’re exposed to can allow you to see which media forms are most successful and where the optimum location for them is. This can be done by looking at which devices that saw the media ended up being used to make an online purchase. The analysis might show you that customers respond best to digital billboards over traditional, paper billboards. From there, you could decide that Digital OOH (DOOH) is more cost-effective, because the movement on-screen attracts attention, even though the cost of installation is higher.

POI Mapping - Location data can be used alongside a POI database to create POI maps. POI mapping is the process of plotting important or popular locations on a map to see their geographical proximity to one another. POI and location intelligence mapping is used in ad placement and retail site planning to see where ads and stores will attract footfall, or face competition from other POIs.

In-flight Campaigns – Here, location-based marketing uses footfall analysis heatmaps and DOOH analytics in conjunction. A huge benefit of using digital ads over traditional ones is that you can adjust them as you go along, or ‘in-flight’. These adjustments can be made based on the results of A/B testing to see which approach generates more lift. Although making these tweaks can require more time and money, from a ROI perspective, it’s a better alternative then having to go and physically replace billboards and print new ones!

Geofencing – Geofencing allows you to create a virtual ‘fence’ around a geographic area. Normally, you’d pick this area because your heatmaps are showing you that there’s a high concentration of foot traffic there, and the people who visit there match your target demographic. You can use lat/long coordinates or beacons to create a boundary which can take any shape you need. When a device crosses the geofence, a response is triggered programmatically in real-time. Then, you can deliver location-specific ads, push notifications or even photo geo-filters (on apps like Snapchat) to the device.

Geotargeting – More sophisticated geofences can tailor the ads they send so that they’re only sent to devices whose users match predetermined criteria. This is called geotargeting, and it works by combining real-time location data with other data about customer attributes – their behaviour, demographic, interests etc. – making the campaign you create better suited to your audience.

Geo-conquests – Businesses can also use their store location database to set up geofences around zones where they face competition and push ads and promotions in that area. In 2020, Whole Foods turned to geo-conquesting to increase their sales. Their aim was to improve post-click conversion rates on their mobile ads. Post-click conversion rates indicate the number of people who click on a paid-for advertisement and proceed to engage in some way, whether that be a sign up or a purchase. Whole Foods placed geofences around its stores, whilst also placing geofences around competitors’ stores. When a customer entered these areas they were targeted with ads and discounts for Whole Foods, aiming to deter them from the competitors’ stores and attract them to Whole Foods. It’s safe to say this was a successful example of geoconquesting, with the campaign yielding Whole Foods a post-click conversion rate of 4.69%, more than three times the national average of 1.43%.

All of these strategies become more effective when supplemented with audience and POI data , so you can understand which locations are popular with which types of people and create appropriate segments. Let’s say you notice that an inner-city gym is a POI. This information alone isn’t helpful – what if you’re a desserts company? Just because a location itself is attractive in terms of footfall, this doesn’t necessarily mean it’s a good location to centre an ad campaign around. Supplementing your geolocation dataset with other data types can give you the strongest chance of creating a successful campaign. A good cell location data supplier offers context-aware location datasets, which take into account consumer data alongside the raw location databases and APIs.

What is the definition of Location Data analytics?

Location data analytics describes the process of aggregating layers of geographical data and business data to achieve more detailed insights. Location analytics data can be purchased online through direct purchase from location data vendors. Location data analytics enables users to create visualizations of consumer movement, the flow of stock, sales, and supplies in greater depth. A business that adds location analytics to its existing intelligence is better placed to spot meaningful patterns in raw geographic data and improve their strategies.

How does traditional analytics make use of location-based data?

Traditional analytics amalgamates a business’ internal data with external location data in order to get maximum business and consumer intelligence. Location data has become easier to use alongside traditional analytics thanks for data management software, which can create visualizations and produce pre-analyzed reports. Business intelligence (BI) software uses external location data to enable analysis of the data to provide wider context via these visualizations and reports.

If you’re still wondering…

• What is location marketing
• Who uses location based marketing
• How does location based marketing work
• What are location based marketing examples
• What is location based advertising
• How does location based advertising work
• How to geofence a location

…then visit our location-based marketing and location-based advertsing use case pages to learn more about location data services and discover location based advertising companies.

How can Location Data can help with branding?

Location data is valuable at all points of a company’s life-cycle. Once your brand is out there, make sure it stays out there. If you’re looking to make your brand’s operations more efficient and able to withstand competition, you may be surprised at the solutions offered by location-based analytics:

• Prediction/Forecasting – Manage your inventory and personnel more wisely by using store location data, footfall rates and post-visit reports to predict when customers will increase and decrease.

Historical Retargeting – Retargeting is a key part of location-based advertising. Location data can identify devices which have visited a location previously. Knowing this, marketers can adjust the timing of their ad delivery strategically. If you’ve seen from your location database API that a device has visited a competitor’s store that day, you can queue an ad to be sent to the device later that evening and win business from your competitor.

• Programmatic Media – Reaching customers who don’t use Facebook, Google or LinkedIn can present issues. Luckily, ‘programmatic’ ad inventory can be purchased, which gives marketers display impressions over millions of websites and apps. Devices can then be targeted based on real-time location data coming from the bidstream.

• Cross-Device Reach – We’ve talked a lot about smartphones, so you’re perhaps wondering if they’re the only device that location data can work with. That’s not the case! As of 2017, the average digital consumer owned 3.2 connected devices (smart watches, streaming sticks and the like). The problem with desktop computers is that they rarely leave their primary location. So, we can use cross-device or identity graphs to link the static device to the owner’s other devices, and analyze the relationship between location and online activity that way. This also gives you a wider time window to target customers when they’re working on a computer and not using their smartphone. You might also alter your ads so they’re compatible with different screen sizes based on the information from your location API.

• Site-planning – this is, unsurprisingly, about planning where to open a new store or roll out an ad campaign. Looking for POIs increases the likelihood of brand exposure and store visits. Retail location data allows brick and mortar store owners to monitor traffic to their store and identify competitors in the area.

What is the best Location Data for me?

Aside from quality, finding the best database for location data depends on the nature of the data you need for your specific use case. For example, the best location data for marketers won’t be as useful for publishers. Geolocation database suppliers offer different types of location data in different formats. Some location intelligence services can offer a real-time location tracking API, others offer AI location intelligence. Let’s look at the pros and cons of each type of location dataset:

Real-time location data

Real time location data gives you access to live information about mobile users’ location and movement. Real time location intelligence is collected on a second-by-second basis. Real time location data gives you the most up-to-date understanding of customer movement and POI intelligence for real-time geolocation analysis. It’s used by advertisers for programmatic advertising, and by marketers for in-flight adjustments to their location-based marketing campaigns. The downside to real time location data? Live location data streams and feeds are typically more expensive than historical geolocation datasets, because location data companies need access to sophisticated software to deliver a live service.

Deterministic location data

Deterministic location data relies on data from actual human behaviour. Rather than using collected data to predict a customer’s future habits, data is used to personalise companies’ actions or campaigns to their customers. For example, if a company knows that you live in a given postcode they could offer a discount tailored to what is in your area rather than just predicting that you might be interested in a brand based off your habits. Customer habits can change quickly and that is one reason why deterministic location data may offer a better option for companies than probabilistic data.

Visits Data

Visits data is location data that indicates where a customer has visited and at what time or frequency. Visits data can refer to website visits as well as physical visits to locations. This allows a company to track factors such as popular visitor times, frequency of visits and locations from which a website is being visited or from where a visitor has travelled.

Raw location data

If you want to carry out your own location data analytics, raw geolocation data is best. A raw data download will allow you to gather the insights you need for a range of use cases, from location-based advertising to geolocation mobile marketing.

AI location data

Artificial intelligence location intelligence - sounds intelligent, right? AI location data is exactly that: extremely accurate location information gathered using AI and ML. Geolocation data providers with AI data collection tools can offer location data at scale, using algorithms to process huge amounts of data in short amounts of time. If you’re looking for the most detailed, factual location data, then AI location data is a good fit for you. However, like real time location data, location intelligence companies offering AI data usually charge a lot for their location-based intelligence, because the data quality is so high.

Historical location data

To track location trends and location based marketing trends, you need access to a historical location dataset. With historical data, you can perform location data analysis and make predictions about future POIs and consumer visits. Retailers can buy historical location data from a location analytics company for a variety of solutions, from retail site planning to supply chain management. Historical location databases vary in price, depending on how far back into the past you want their location data offerings to stretch. Historical location intelligence providers can’t supply a smart location database of the same standard as a real time or AI data provider, but historical data serves very different use cases and often isn’t as expensive.

Like everything, buying data comes with some challenges, which are worth considering.

What are the challenges with Location Data?

Quality

Ironically, a lot of the problems with location data are caused by the fact that it can be such a powerful tool. As the demand for location data grows, more location intelligence companies emerge, but, also, more poor-quality location datasets are released. We’ve talked about why data needs to be accurate and precise to perform at its best, and the only way to be sure your data is of quality is to buy from a first-party provider with a verified method of data collection.

First, Second and Third Party Data: Explained

Make sure you know the source of the data you are buying:

• First-party data is data you collect yourself, directly from your customers.
• Second party data is someone else’s first-party data.
• Third-party data is purchased from external sources – so the vendor didn’t collect it directly.

First and second-party data is almost always more reliable and transparent than third.

How accurate is cell phone location data?

The accuracy of cell phone location data is up to 30 meters. Relying on cell phone location data alone can sometimes be misleading given the distance involved, it is therefore important that providers verify and supplement cell phone location data with GPS location triangulation in order to get more accurate data on location. So, when you shop for location data it is important to ask your provider about their accuracy assurance measures!

Privacy

In recent years, countries have brought in data protection regulations in order to ensure users are aware of when their data is being used and what for. For all consumer data, there needs to be a clear opt in process which is enforced by the following legislation:

European Union

In 2018 the European Union introduced a new regulation in EU law called General Data Protection Regulation (GDPR). It aims to make users more aware of the data being collected from them when they access websites, asking the user to accept cookies for the website or accept the terms and conditions associated with use of the website. This regulation not only applies to companies working in the EU but also to companies outside of the EU with customers located in the EU. If a company from the USA wants to collect data from an EU citizen they must also ensure that they comply with GDPR. Companies found to be in breach of GDPR can expect to be fined up to 4% of their annual global revenue.

Mobile Location Data and DSGVO/GDPR

Data protection is vital. Companies and organisations must be increasingly careful when it comes to how they manage and use their clients’ personal data.

Businesses operating in the EU must abide by several different sets of laws for data protection. The DSGVO/GDPR is perhaps the most important data protection regulation. It stands for ‘Datenschutzgrundverordnung’ (whereas GDPR stands for ‘General data protection regulation’).

Companies selling DaaS in the EU must conform to DSGVO/GDPR regulations. For this reason, privacy-assured mobile location data with EU coverage is harder to come by, whilst demand for location data has increased. Buying location data from a data provider who is certified to operate in the EU is a way of overcoming this challenge.

USA

The California Consumer Privacy Act (CCPA) functions in a similar way to GDPR. It was enforced in January 2020 and is a state-wide regulation. If a company fulfils any of the following three requirements they are obliged to abide by this legislation: any for-profit organisation that sells the personal information of more than 50,000 California residents annually; has a annual gross revenue exceeding $25million, or derives more than 50% of its annual revenue from selling the personal information of California residents. Under this act, any resident of California is also entitled to opt out of having their data collected, request insight into what data of theirs has already been collected and request any data of theirs be deleted. In a similar way to GDPR, businesses are liable to significant fines if they fail to comply: either $7500 per violation or $750 per affected user.

Other data protection regulations around the world

In April 2020, Canada implemented PIPEDA (Personal Information Protection and Electronic Documents Act. Canada not only implemented this regulation in order to reassure Canada’s own citizens of their data’s security but also as a response to the EU’s GDPR, aiming to offer reassurance that Canada was committed to ensuring the protection of EU citizens’ data. Data protection regulations are being implemented across the world, from Brazil’s LGPD (Lei Geral Proteção de Dados Pessoais) to China’s Cyber Security Law of 2017.

Is Location Data personal data?

Location data is classed as personal data when it doesn’t refer to the positions of buildings or objects, but people. So, before buying and selling such information, data providers must aggregate PII and follow privacy-compliance regulations. Location data vendors have to follow GDPR and CCPA laws before disseminating it to any third party.

To mitigate the challenges associated with buying location data, most DaaS companies will let you sample location data before you buy it. It’s always good to ask any location data provider for a sample, because then you can test whether the data provides the insights you need before you make a purchase of location data.

Which delivery formats can Location Data be delivered in?

• S3 Bucket - This is a public cloud storage resource which is part of Amazon Web Services’ (AWS) Simple Storage Service (S3). Essentially, buckets are places to store files (or ‘objects’, as Amazon calls them). They can be accessed using URLs so must have globally unique names.
• SFTP - Secure File Transfer Protocol is a file protocol that allows you to transfer large files online. This ensures the security of files when being transferred. When certain data protection standards need to be met (such as GDPR mentioned previously), businesses can rely on SFTP to ensure these standards are satisfied.
• REST API - REST stands for Representational State Transfer. REST can be seen as a set of rules for communication between programs. The information abstracted using REST is called a resource. This could be a document or image, for example. REST APIs function by receiving requests for a resource and returning all relevant information about the resource in a format easily understood by the client.

That’s a lot of things to ask your data vendor. We’ve put together a handy list of the most important things to ask to ensure you’re buying the most robust dataset:

• What is the source of the data – first, second or third-party?
• How do you verify your data?
• Do you filter out poor-quality or irrelevant data?
• How do you collect the data (GPS, WiFi, beacons etc)?
• What is the scale of your dataset?
• Can you prove your data is GDPR and CCPA compliant?
• Do you offer a location data sample?

Considering location-based marketing or advertising? Here’s a quick overview on how location data is typically priced, and where you can buy location databases.

How are Location Data products priced?

There are different pricing models attached to cell phone location data for sale. The pricing of location data offered by location data providers varies depending on the quality of the data provided. As a general rule of thumb, highly accurate data can get costly.

If you’re looking to find location data, two pricing models are most popular:

• One-off – Businesses buying historical business location data for analysing patterns in foot traffic, for example, buy their data per given batch in the form of one-off purchases.
• Real-time APIs – This is a subscription service, preferred by those who need more recent and timely updates from their data. Real-time APIs are fed by continuous sources of daily, even hourly updates. For such services, a monthly or yearly subscription fee is the most common form of pricing. A location data API is the best way to access live updates about consumer movement.
• Usage-based - Buyers also have the option of paying for location data depending on how much they need and how regularly they require it. It’s a flexible payment model offered by many mobile location providers.

Does Google sell location data?

Google shares location data on an opt-in basis. Data Marketplaces like Datarade’s offer alternative location data sources via commercial location datasets from various provider companies. Because Google’s location data can be expensive, data marketplaces enable businesses to shop for location data online and find location data from a provider at a price that works for them.

What are the common data visualization types that use Location Data?

DaaS companies are offering more ways of presenting location data in a way that is both accessible and attractive. Here are a few data visualization possibilities:

Dot distribution maps:

Each dot on a map has the value of a certain number. The distribution of these dots offers an overall view of areas of high or low density of the subject in question.

Choropleth maps:

Predetermined areas on a map are assigned a shade of a colour that represents the data collected for this area. For example, areas with a high density of supermarkets could be attributed dark blue, with areas with few supermarkets are represented with a pale blue.

Proportional symbol maps:

The clue is in the name with this one! Symbols are placed on a map, varying in size depending on the variable they represent.

Flow maps: Lines on a map represent the movement or people, goods or services. Thicker lines are used to indicate a greater amount of movement between two areas.

How has the Covid-19 pandemic impacted the collection of Location Data?

Although the Covid-19 pandemic has reduced the amount we are all moving about, mobility data has played a key role in both adjusting to a new way of life and overcoming the virus. While many companies were left bankrupt as a result of the pandemic, many of those with a firm foot in e-commerce and those able to adapt and diversify their businesses actually grew.

Governments worldwide put location data to use in contact tracing systems and in mapping coronavirus hot spots. This allowed countries insights into current infection levels.

Similarly, many retailers used footfall data to map the times at which customers are leaving the house to shop or exercise as well as which shops they are frequently returning to. Overall, retail websites generated almost 22 billion visits in June 2020, up from 16.07 billion global visits in January 2020. By collecting location data from these visits, companies have been able to target specific areas and establish trends in customer behaviours. Perhaps one of the greatest e-commerce winners of 2020 were supermarkets, with the number of online food orders soaring across the USA and Europe. The UK online supermarket Ocado Retail experienced a tenfold increase in demand in April 2020 and its web traffic was up to 100 times higher than pre-pandemic levels. Supermarket retailers such as Ocado greatly benefitted from their access to customers’ location data, which allowed them to concentrate delivery drivers and supplies in specific areas and offer more delivery slots in the areas experiencing the highest demand levels.

Likewise, the Covid-19 pandemic meant that social distancing became an everyday norm. Access to public spaces was restricted worldwide, especially in heavily-populated cities.

In these cities, which fortunately are already well-connected in terms of transport and logistics, autonomous delivery systems could mean that food, goods and supplies could be delivered without contact between people. With the help of location data, local authorities and manufacturers could use drones and driverless vehicles to optimize delivery routes and programme delivery robots. Location data is becoming a more important asset in smart city planning, and the pandemic only confirmed the power of mobile location data as a force for good.

One thing the coronavirus pandemic has made clear is the increasing relevance of location data in business’ survival. Access to customers’ movements may prove essential in an ever-changing and unpredictable world!

Conclusion

We’ve now covered everything you need to know about location data: what it is, how to source the best quality. It has potential to enhance your organisation’s performance in ways you may never have considered. Location data has already transformed the campaigns and infrastructure of businesses worldwide - and it’s showing no sign of stopping.

Wondering how to buy location data? Take a look at some of the leading data providers.

Where can I buy Location Data?

Data providers and vendors listed on Datarade sell Location Data products and samples. Popular Location Data products and datasets available on our platform are Factori Mobility/ Raw Location Data | Global mobile location data (1 year history) by Factori, Irys Real Time & Historical - Worldwide Mobile & Connected Device Location Data by Irys, and Area Visitors – analyze global foot traffic trends for geographic areas via API or batch data delivery by Gravy Analytics.

How can I get Location Data?

You can get Location Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Location Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Location Data APIs, feeds and streams to download the most up-to-date intelligence.

What are similar data types to Location Data?

Location Data is similar to Map Data, Satellite Data, Point of Interest (POI) Data, GIS Data, and Cell Tower Data. These data categories are commonly used for Location-based Marketing and Location-based Marketing.

What are the most common use cases for Location Data?

The top use cases for Location Data are Location-based Marketing, Location-based Marketing, and Location-based Marketing.