The Ultimate Guide to Location Data 2020
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Your Map to Location Data Success: The Ultimate Guide to Location Data in 2020
Whether you’re planning to enrich the location data you already use, or are looking to discover why location data is creating such a buzz among businesses across the world, you can rely on our Ultimate Guide to Location Data in 2020 to tell you everything you need to know.
To help you conceptualise technical terms and scenarios, we’ve included use cases that are interesting and contemporary examples of how truly versatile location data is.
Having access to the internet virtually wherever we go has revolutionised modern commerce. Smartphones have become an extension of ourselves – and it’s estimated that 3.5 billion people in the world now have one. As such, smartphones can reveal a lot about our habits and behaviour – both online and when we’re out and about.
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.
What is location data?
Location data is information relating to the position of mobile phones, tablets, and laptops, or structures like historical monuments, buildings, and attractions.
What does location data look like?
The most common attributes of location data 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 behaviour 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, 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. Timestamps are commonly recorded in Unix time (a.k.a Unix Epoch time, or just Epoch).
How is location data collected?
In many different ways! Locations can be calculated with the help of objects like vehicle fleets, wearable devices, shipping cargo, and the like. However, in this article, we’ll be looking primarily at mobile location data. This is the type of location data which marketers in 2020 predominantly rely on. Before we can understand mobile location data, we first need to understand the device which is responsible for generating most of it today – your smartphone.
What role does my smartphone play in collecting location data?
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 insight into where they are and what they’re doing there.
“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 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. 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. The accuracy is reduced indoors, underground, and in places with tall buildings and trees. However, in all, GPS data gives 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 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.
Cell Towers – Cell towers supply the signals which allow mobile phones to place calls and send messages. The location of the device can be calculated using triangulation as the signal strength varies between towers as the device moves around. The precision of the data can vary, because the location generated is based on the radius of the nearest tower, and so can be up to 10km away from the user’s true location. For this reason, cell tower data is useful over broader areas, like neighbourhoods or postal codes, where precision is less important
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, but the scale they offer is limited to 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) 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 accuracy of the data they generate increases when more than one transmitter is used, or when the data is supplemented with point of interest (POI) data, which tells you about a specific location (we’ll look at POI data in more detail later).
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 this data.
What are the sources of location data?
Software development kit (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 behaviour, payments, app performance – and, importantly for us, location.
Some SDKs use the OS alone to provide a precise location for the device being used, but others optimise this with additional analytics. SDKs require 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 app.
SDKs can perform a wide range of functions, but this means that the reliability of the location data they collect varies from one SDK to the next. However, the most accurate location-based 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 accuracy.
Because SDKs are codes built into apps, the scale of the 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 SDK data be analysed for patterns and insights, otherwise the data can’t represent a sizable 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:
- Device type
- IP address
- Connectivity (WiFi, 3G, 4G)
- 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”
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. Similarly, IP addresses rotate every few months, making it difficult to use them to accurately track location over a longer period of time. Thus, there’s the risk that the data is misleading. 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, making the scale of the data they offer hugely attractive.
Publisher datasets – Location data can be collected by app publishers themselves. If the app has inbuilt location services (like searching for a taxi from 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? 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. 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?
Low quality data limits how useful it is – don’t let bad location data send you in the wrong direction. 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 store is located on Oxford Street, or in the borough of Westminster, or in the city of London. All three statements are accurate, but the first is most precise.
Scale – how much location data a source offers you to analyse. The greater the volume of data, the greater the scale.
As with all types of data, location data assessment necessitates a balancing act between the three to ensure your source provides high quality insights. 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 ago the data was retrieved. Will this affect whether the device is in the same location or not?
Frequency – how often someone visits a location. Are they a regular customer, or a first-time visitor?
If that wasn’t enough to consider, location data is 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:
In which scenarios would you value one aspect over another?
The quality factor you deem most important depends entirely on 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 analysing 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.
It probably goes without saying, however, that fraudulent and fake data are always good to avoid, where possible! Ensure your vendor takes all the right precautions to remove bad data from their sets and sources.
All of these scenarios draw on location data 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. What is getting people talking, however, is how mobile location data has transformed digital marketing and advertising:
Marketers – 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 behaviour, interests, intent, brand affinity lies in location data.
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. The possibilities are endless – 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 – In 2017, US companies spent over $17 billion on location-based mobile advertising, and this spending is forecasted to reach nearly $40 billion in 2022. 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.
You can also fine-tune your 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 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 considers a purchase. Location data can be the key to launching your brand to success.
We’re used to hearing about location 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 location data to understand customer behaviour and store visits. It 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.
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 footfall is the food court, try and put 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.
Retail 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.
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. 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.
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 turn the fantastic online capabilities of location data to generating in-store results? If we’re too caught up with catering to an online audience, we risk alienating customers in the real-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. A POI is a place that’s 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. 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 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 optimised 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 personalised, 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 which use location data, but they all boil down to tracking the movements of devices, analysing where they go and when, and targeting the areas which will give you the best results, whether this be ROI or better understanding of your customers for segmentation.
Location data can help you make informed decisions, based on the following strategies:
Footfall Traffic Analysis – This could be seen as the ‘first step’ towards utilising your location data effectively. The data can show you trends in footfall, such as which locations are popular, and whether this varies over time. From this, you can create ‘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.
Footfall Traffic Attribution – This comes after footfall 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 to a specific campaign lets you decide which are most successful. This 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, 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.
In-flight Campaigns – Here, you’ll use your 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 footfall 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 set up geofences around zones where they face competition and push ads and promotions in that area. Burger King took on rival McDonald’s in an epic geo-conquest in 2018, offering anyone who opened the Burger King app within 600 feet of a McDonald’s store the chance to buy a Whopper burger for a penny.
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 location data with other data types can give you the strongest chance of creating a successful campaign.
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 location data offers:
Prediction/Forecasting – Manage your inventory and personnel more wisely by using footfall rates and post-visit reports to predict when customers will increase and decrease.
Historical Retargeting – 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 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.
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 analyse 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.
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.
Like everything, location data comes with some challenges, which are important to consider before investing in it.
What are the challenges of location data?
Quality – Ironically, a lot of the problems with location data are caused by the fact it’s such a powerful tool. As the demand for location data grows, more poor-quality 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 vendor 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.
Privacy – After the EU implemented the GDPR in 2018, businesses had to make it clearer to customers when their data was being used. The California Consumer Privacy Act (CCPA) is the US equivalent, introduced into operation in California in January 2020. GDPR infringements can result in a penalty worth 4% of the company’s annual turnover, so it’s vital that your location data is GDPR compliant. Again, buying from a trusted vendor who can prove that the data was collected consensually can ensure this.
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 before buying location data:
- 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 compliant?
Considering making location data a part of your strategies? Here’s a quick overview on how it’s typically priced.
What is the cost of location data?
The pricing of location data varies depending on the quality of the data provided. As a general rule of thumb, highly accurate data can get costly.
Two pricing models are most popular:
Pay-per-batch – Businesses buying historical location data for analysing patterns in foot traffic, for example, pay for their data per given batch.
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.
To sum up…
We’ve now covered everything you need to know about location data: what it is, how to get your hands on 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.
Take a look at some of the leading data providers, and get access to the 2020 marketer’s secret weapon.