The global adoption of smartphones has grown at incredible speed in the last decade.
Mobile devices are a powerful tool for understanding the aggregated behavior of consumers.
Understanding device location opens doors to a wide range of use cases that are unique in many different ways.
Mobile location data provides a granular solution for consumer understanding. Combining this understanding with other datasets are helping to solve business problems and achieve goals across many different industries.
For these reasons, location data has quickly become the holy grail of mobile. It’s applications are broad and run across a number of different industries and verticals.
But before we get onto that, what exactly is location data?
The mobile device or smartphone has been revolutionary. Its growth has been incredible – many predict that there are now more of these devices in the world than there are people.
Smartphones have transformed everything about our everyday lives -we rarely leave home without it, and it’s always on our person, ready to provide us with instant information or guidance.
These devices have enabled the location data industry to understand how audiences move and behave in the real-world. This information is location data. It comes in many different forms and from various sources.
What is location data?
Location data is geographical information about a specific device’s whereabouts associated to a time identifier. This device data is assumed to correlate to a person – a device identifier then acts as a pseudonym to separate the person’s identify from the insights generated from the data.
Location data is often aggregated to provide significant scale insights into audience movement.
How is location data generated?
Companies are collecting location data in many different ways. There are several different techniques to collect location data. These techniques differ in reliability (but more on that later).
For now, the primary process of collecting location data requires the following ingredients.
A location source/signal
The first ingredient is a location signal. This signal is not a product of the device itself – it comes from another piece of technology that produces signals. The device listens to these external signals and uses it for positioning. These signals are as follows:
GPS is shorthand for the global positioning system and was first developed in the 1970s. The system is made up of over 30 satellites which are in orbit around the earth. This technology works in your device by receiving signals from the satellites.
It can calculate where it is by measuring the time it takes for the signal to arrive.
GPS location data can be very accurate and precise under certain conditions, mostly in outdoor locations. In the best instances, the signal can be reliable down to within a 4.9 metre radius under open sky (source) .
Wi-fi networks are another source of location signals that are great at providing accuracy and precision indoors. Devices can use this infrastructure for more accurate placement when GPS and cell towers aren’t available, or when these signals are obstructed.
Beacons are small devices that are usually found in a single, static location. Beacons transmit low energy signals which smartphones can pick up.
Similarly to Wifi, the device uses the strength of the signal to understand how far away from the beacon it is.
These devices are incredibly accurate and can be used to place a location within half a meter with optimal signal strength.
Carrier data/cell towers
Mobile devices are usually connected to cell towers so that they can send and receive phone calls and messages. A device can often identify multiple cell towers and by triangulation, based on signal strength, can be used to place a device location.
Each smartphone needs to be associated with an identifier to understand movement over time. This identifier is called a device ID. For iOS, this is called an Identifier for Advertising (IDFA), and for Android, it’s called an Android Advertising ID (AAID).
Meta data or additional dataset (optional)
A location signal combined with an identifier will allow you to see the movement of a device over time. However, for more detailed insights and to get more value from location data, you’ll need some metadata or an addition dataset.
The most common dataset to do this is a POI dataset. This dataset includes points of interest that are important when comparing how audiences move and behave in the context of the real world..
For example, a series of data points showing how Londoners move between 7-10am could be useful. Tying this to a dataset that included tube stations and key travel routes would allow you to do much more with the initial data.
So, we have already looked at the ingredients that combine to make location data, including the different types of location signals. However, what are the sources of location data? If you are looking to use location data in your organization, then you need to know the differences between every potential source.
The source can have a significant effect on accuracy, scale and the precision of devices. So, from where does location data come? There are three primary sources:
A sizeable proportion of location data comes from something called the bidstream (also referred to as the exchange). The bidstream is a part of the advertising ecosystem. Don’t worry if you’ve never heard of this – we’ll explain everything.
Explainer: The ad buying ecosystem
The ad buying ecosystem
Before we talk about bidstream data, it’s helpful to understand how ads are bought and sold.
- Direct deals with publishers such as an app, site, or network.
- Ad networks which group ad inventory to sell it to advertisers
- Ad exchanges provide a solution for publishers to offer up their inventory programmatically, allowing advertisers to buy it in real-time. Purchasing advertising inventory in this way produces a bid request.
Why is this relevant for location data I hear you ask? In every bid request information is passed on – this data contains several attributes used to determine whether to serve the ad on the device.
Included in this dataset is a form of device location. A company will package up this location data, and the result is the bidstream location data that is available today.
Bidstream location data is appealing because of the sheer amount of it – it can very quickly provide a large amount of scale. However, bidstream data also comes with specific issues – it can be inaccurate, inconsistent, and even fraudulent. Because it’s captured programmatically ,then bidstream location data also has the benefit of being immediately actionable.
“Up to 60% of ad requests contain some form of location data. Of these requests, less than a third are accurate within 50-100 meters of the stated location”
Remember, in the last section, when we identified location signals? Cell tower location is one of these and is the process of triangulating the strength of mobile cell tower signals to place the device in a specific location.
This kind of location comes directly from a telecommunications company (telco). Usually, they have some demographic data associated with the location data.
Similarly to bidstream data, the scale that telcos can offer (they have an extensive reach as in many countries few companies serve the entire population) is appealing.
However, in the same way, this scale is masking many issues with the accuracy of the data. Some studies have found that as little as 15% of data sampled was incorrect.
A software development kit (SDK) is a toolkit that app publishers can add to their app to provide third party functionality. Developers add location-based SDKs to their apps to access the most precise and accurate location data signals from the user’s device.
Location SDKs come in many shapes and forms – some make use of the core location functionality present in the OS, others do a degree of data processing on top, to boost accuracy.
Some SDKs only operate in the integrated app when the app is open. Others can run in the background to gain broader insights into the movement and behaviors of the device.
Location-based SDKs collect data with the user’s consent – the apps native permissions often collect this consent, but some SDK providers offer consent tools to ensure that the apps are collecting data in accordance with relevant regulations.
The difference between SDK generated data, and other sources of data can be seen in the accuracy and precision of datasets. Data collected by location SDKs are more accurate because they can listen for multiple location signals.
For example, SDKs can use the device’s built-in GPS to place the device and then, using Bluetooth signal strength from beacons, verify and fine-tune the location of the device down to within a meter of accuracy.
Location SDKs usually have a more sophisticated way of understanding how the device is behaving. For example, the Tamoco SDK uses motion behavior and other entry/exit events to know when a device visits a venue or location.
Why isn’t all data collected using SDKs?
If location SDKs are the most accurate and highly precise, then why don’t we use them to collect all location data?
The issue with many location SDKs is that they require integration into a publisher’s app. This app then needs to cover an adequate number of devices before the data is representative enough to gain any valuable insight or relevant patterns.
However, some SDKs have been built with functionality that benefits the publisher and limits battery usage to a minimal level. These SDKs are the ones that have achieved significant scale.
For example, the Tamoco SDK is optimised to send data in batches to minimise the number of requests. We also modify how data is collected depending on the current battery level.
All of these factors are a direct result of a close working relationship with our developer partners and allows the Tamoco SDK to scale along with our partners.
It’s possible to obtain location data directly from app publishers. Some publishers have developed methods of obtaining location by using the devices inbuilt location services.
These will usually coincide with a location-based process within the app – such as looking up a nearby restaurant.
These are often not as accurate as the location SDKs that have been carefully built to collect verified location signals. However, they can be a good source of location data as long as you can validate and understand the process of data collection put in place by the publisher.
We’ve already said that good location data is accurate and precise. However, let’s take a step back and ask ourselves a question – what do we actually mean by accurate and precise location data?
Location data collected by smart devices usually come in the form of a latitude and a longitude coordinate, or a lat/long. This reading refers to the perceived location of the device at the time.
However, how can we make sense of this number and understand if it’s accurate?
Location accuracy v location precision
You might think that accuracy and precision can be used interchangeably. However, in the world of location data, they have different meanings.
Accuracy is used to mean how close the measured location is to the actual location of the device. Precision refers to how close two or more measurements are to each other. The illustrations below helps to visualize the difference:
Not accurate, not precise
Accurate, not precise
Precise, not accurate
Accurate and precise
Accuracy is a measurement that helps us to understand how close the device’s geographical reading is to the actual location of the device.
So how do we measure accuracy? The location accuracy of the device changes depending on the type of signal and the device. Accuracy is measured by looking at the signal type (GPS, wifi, cell tower). The device provides us with a reading of the location and then an accuracy rating. This unit is usually a measure of distance and is the margin of error associated with the measurement.
Precision is the level of detail associated with the location measurement. The more this is is similar to the other measurements in the dataset, the more precise the data is.
In location terms, we use lat/long to measure this. Firstly we check to see if the data points are realistically within the same area.
The number of decimal points in the lat/long is essential in measuring the precision of location data. The more digits there are after the point, the more precise the data is.
The following table helps to explain precision when looking at lat/long:
|Decimal Places||Decimal Degrees||DMS||Qualitative Scale|
|0||1.0||1° 00′ 0″||Country or large region|
|1||0.1||0° 06′ 0″||Large city or district|
|2||0.01||0° 00′ 36″||Town or village|
|3||0.001||0° 00′ 3.6″||Neighborhood, street|
|4||0.0001||0° 00′ 0.36″||Individual street, land parcel|
|5||0.00001||0° 00′ 0.036″||Individual trees, door entrance|
|6||0.000001||0° 00′ 0.0036″||Individual humans|
Not all mobile location data is equal
As many in the industry have stated: the type of location data and methodology is of significant importance. The relevancy of different kinds in different scenarios is often contented.
Mobile location data requires some fundamentals to provide granular insights that we discussed earlier.
So what’s the best way to accurately and precisely collect location data and what happens when signals such as GPS aren’t working?
We think this is another argument for SDK generated data. For example, the Tamoco location SDK can listen for multiple signal types simultaneously. Processing these signals allow the SDK to measure accuracy and then determine which signal to use.
Our SDK, therefore, uses Bluetooth and Wifi to help position the device in areas where GPS signals are weak. This sensor agnostic approach means that the SDK can place the device with better accuracy and more precision by using multiple signals.
Remember, when we talked about the three main ingredients that combine to produce location data. We’ve covered the device and its identifier. We’ve also covered the signals that the device used to position itself.
However, we are yet to cover the additional data that is needed to make use of the dataset. As we have discussed location data is usually a lat/long associated with a device and a timestamp.
We need to understand what this location is to make any use of the data. Knowing a device location is half of the challenge. To do this, we use database that allow us to connect this online data to the offline world. We call this a POI dataset.
What is POI
A point of interest (POI) dataset is a data representation of the physical world. A single POI is a geographic boundary and is usually associated with a physical location (think a store or building).
As with location data POI datasets come with a series of challenges including accuracy. Business regularly move, and as changes happen in the real world, the datasets evolve accordingly.
At Tamoco, we set up our own Place database to explore-in-depth how devices move and behave in the offline world. This database is slightly different from a POI dataset.
Explainer: Tamoco places
- Contains metadata associated with the place – opening hours, floor level, polygon footprint and other essential information that can help to verify if a device entered the POI.
- Combines with an associated geographical boundary (geofence) that can be used to understand the device activity inside and how long it stays inside.
- Combines with any known sensors (beacons, Wifi, or other signal based tech) to help understand when a device is visiting the POI and not in fact staying in a place nearby.
What’s the importance of POI?
Perhaps the best way to understand the importance of a useful POI dataset is by using a real-world example.
In the above example, we don’t have a POI dataset. We have multiple lat/long, which might be accurate and precise, but we get no value from this as we have no connection to the real-world.
Here we have a POI dataset which connects the lat/long to a physical location. However, the POI is slightly in the wrong place, which means we think the device has visited the coffee shop, but they are waiting outside, or elsewhere. The implications of this will become more evident in the next section.
Here we have a place with opening hours and altitude. We have a geofence which allows us to see when the device enters and exits. We also have a Wifi and beacon sensor that we know is inside the coffee shop. Using this, we can verify with accuracy that the device was inside the place.
Connecting location to POI
At Tamoco, we do this through a process called visits. This methodology is a powerful data science technique that allows us to validate whether the device is inside a place and to say with a level of accuracy how long a device was inside.
Where other data providers will claim a device is inside a store if a single lat/long shows up inside a POI, we go much further.
What happens if this single data point is an outlier from a car driving past. What if the POI is in the wrong place?
Tamoco uses essential device information (yes, this is possible only by using a location SDK) such as motion type to verify visits to a place and filter out any false visits.
Hopefully, by this point, you will have understood more about how location data is collected and how device location is used to understand the connection between online and offline.
However, what are the uses for accurate and precise datasets? How can your business benefit from adding location data to your business?
Segmentation and targeting
Marketers are always looking for ways to identify relevant audiences for their advertising campaigns. They want to segment their audiences as much as possible to maximize campaign relevancy and convert more users into paying customers.
Location data is an effective and unique method to achieve those goals. The reason for this is that location is a significant indicator of behavior, interests, and intent.
For marketers, the patterns that you exhibit can be used to create a very detailed image of what you look like as a consumer. Location data helps to create an accurate representation of your interests, and this can be used to bring more targeted and relevant ads to potential customers.
When using location data to target audiences, there are a few things to consider. Depending on the business and the campaign marketers may use a different combination of each of these in a single campaign.
Real-time v historical
Marketers might want to run a different campaign depending on the kind of data available to them. One way that they do this is based on time.
Realtime location-based targeting involves identifying when a device is in the desired location and usually involves a mobile targeting. The process is simple – when the user is in the desired location, deliver an advert instantly on that users device through programmatic advertising.
Historical location targeting
This form of targeting is usually called retargeting, and it is similar to real-time that we discussed above. The difference is that over time, the devices that appear in a predefined location are used to build an audience. The advertiser will then retarget this audience at a later date.
Visits vs interests
Targeting based on visits is a clear way of building an audience that has visited real-world locations such as a specific coffee shop.
Depending on the value of the POI database this can be extended to include devices that have visited all of the stores across a brand (for example every Starbucks) or every visit to a type of venue (example – visits to coffeeshops in Austin).
Using location to target people based on interests is another way of reaching a highly specific audience. This method is similar to visits but usually consists of several repeat visits to a location or combined visits that fit particular criteria.
For example, an interest-based target audience, such as big coffee drinkers could contain devices that have visited any coffee shop at least three times in a weekly period.
Another example could be active consumers – these could visit both a gym and a health shop within a month.
Interest-based location targeting is interesting because you can create very specific segments. However, as with other aspects of location-based targeting, the more specific you get, the less scale you can achieve with your campaigns.
Channels for location-based targeting + examples
By combining these, you can create highly targeted audiences using location data. But how do you then reach them?
Using device identifiers marketers can feed relevant devices into their programmatic stack to automatically buy ad impressions and target the desired devices in near real-time.
The same data can be used to retarget at a later date in a social feed or via another programmatic channel.
The benefits of this strategy are that you can automate a lot of the marketing process. By using location-based audiences, you can ensure that you are reaching the right audience with the right message.
Tamoco offers these as pre-built segments (both visit and interest based) that can be activated directly in your DSP for targeting, or in your Data Management Platform (DMP) for combining with other data sources.. This process can be used to reach consumers across several programmatic channels and on different devices.
Drinks brand targeting consumers in real-time when they visit a venue.
In this situation, we would identify several venues that stocked the relevant products. By feeding visit data into the programmatic stack, it is possible to deliver mobile ads to the device while that visit is going on, or after the visit has occurred. This ad could appear in-app inventory or while browsing the web on the device.
Retargeting through social visitors to gyms with a health drink
Here visits to the category of gyms would be used to build an audience. Next, we would feed the audience into the social targeting platform (facebook ads or similar). The campaign would deliver the retargeting ads to the consumer in their social feed.
Targeting a competitor’s bank customers with a better offer
In this example, devices seen inside a competitor bank are targeted with advertising intended to initiate a switch to a new bank. The data would be historical and might include multiple visits to verify the person is a customer. This data could be used as part of a campaign across several different channels, depending on the marketing stack.
What about location-based segmentation?
The examples we have given include building a new location-based audience to feed into targeting solutions. However, the same principles can be applied to an existing audience.
For example, you can use location data to segment your audience into more specific segments and tailor each targeted ad to be more relevant to each segment.
Personlization & engagement
Today’s consumers demand a high level of personalized communication. Location data can help to bridge the gap between communication and personalization.
Location data can help to personalize ads and messaging to new customers. It can also help to personalize the customer experience.
Consumers want personalization, and everyone from marketers to product designers wants to deliver it.
Location-based marketing personalization
In marketing, location data can help to personalize ads, changing the creative for segments of the audience. This personalization is done by segmenting the ad audience based on location data behavior. These segments are then used to deliver creatives that are relevant to their behaviour – think ‘enjoyed your coffee today’?
Tailoring the ad message boosts personalization and boost the key metrics that marketers are always looking to improve
Location based engagement
Location technology can also be useful for personalizing the customer experience. Integrating a location SDK into your consumer-facing app can support location-based personalization, boosting engagement and retention in the long term.
For example, you can deliver contextual notifications when a user is in a relevant location. Remind users of items left in their app basket when they are nearby to a physical store, for example.
Using location to predict what your customers want
The data that marketers now have at their disposal has enabled them to do more than just personalize based on past consumer behavior.
Location datasets can take personalization to the next level. Marketing personalization is becoming predictive. Brands and advertisers can now combine multiple data sources to understand how consumers behave on both a micro and macro level.
Using this information, it’s possible for marketers to become predictive with their personalization.
Marketers can continuously update their perceived customer profiles with data that explains a consumers profile clearly. This process helps the business to personalize the consumer journey and remove potential barriers to purchase.
Measurement and attribution
As we have seen, the world of marketing and advertising can benefit from using location data in their targeting, segmentation, and personalization strategies. However, location data is valuable in another area where marketers have struggled – attribution.
Advertising is usually quite easy to measure in the online world. If a consumer clicks an ad and makes a purchase, this can be measured and attributed pretty accurately to the ad.
However, what happens if the goal is a store visit instead? Marketers have been scratching their heads for years trying to solve this conundrum. Location data is the missing link that can connect the two.
Location data can act as the link between the online and offline, linking a digital programmatic ad to a store or venue visit.
This link allows marketers the ability to measure and quantify the return on investment from their campaigns. The same capability is useful for out of home (OOH) providers who are looking for a way to link their real-world ads to digital or physical conversions.
It always goes back to accuracy and precision
Location-based measurement and attribution are useful, but it requires data that accurately represents a consumer’s real-world behavior. This data needs to be more than just a single data point – marketers need to know with certainty that a store visit is attributed to an ad to measure ROI effectively.
This requirement is another argument for a place visits methodology that we have already discussed. Device characteristics such as motion and dwell time are essential in providing an online-offline attribution solution that accurately links digital ads to store conversions.
Digital campaign attribution
An agency is running a campaign for a clothing brand. The campaign is delivered to audiences programmatically. The campaign aims to drive footfall to stores stocking a new range.
The impressions and clicks can be measured by the agency, but the brands want to know if the campaign is driving customers to their stores.
Using location data and matching against the IDFA/AAID’s targeted during the campaign, an exposed audience is created. A control audience is also built to compare the exposed group against users who weren’t targeted during the campaign. By having an exposed and control group who were equally likely to visit the clothing brands stores before the campaign, it is possible to isolate the impact the advertising had on store visits by seeing how store visits between the groups move during, and for a period after, the advertising period.
A brand runs an OOH campaign across multiple OOH sites and wants to understand which of these was the most effective in driving online purchases, or whether the OOH advertising was driving online purchases in the first place.
Through an accurate understanding of where the OOH sites are located, and by accurately and precisely understanding how a device moves in relation to the site (an accurate view of this needs to factor in how much time a device spends close the site, how fast they move past the site and a number of other factors the Tamoco SDK factors in) it is possible to build a group of devices that were likely to have been exposed to the OOH advertising.
These devices can be compared to similar devices that weren’t exposed to the advertising, and their device identifiers can be matched to customers in the companies CRM or DMP to measure the impact the OOH advertising had on store purchases as well as which of the OOH locations was the most effective in driving purchases.
Analytics and insights
Location data is a useful tool to analyze how large numbers of people move and behave to identify large scale trends and patterns.
These kind of insights are usually difficult to attain at scale in the offline world. Location data works as an indicator of where people go and how they behave – and how these change over time.
In the realm of advertising and marketing, location-based analytics can deliver valuable insights, such as:
- Comparisons between brand, category, or another group of physical locations over time. Such models will look at the footfall changes over time.
- A brand can use location data to understand more about its customer demographics – where they live and work, where else they shop
- Insights into their store performance – average unique visits per month, number of repeat visits, average visit length.
These insights can be used for a variety of adjustments. Including changing campaigns to suit the real-world behaviour better, to fundamentally changing market strategies to match the data of how a customer is behaving in the real world.
These same insights can be applied outside of the marketing and advertising vertical. Using footfall can be useful across a range of industries including retail, finance, real estate, healthcare, and government.
Location data can be useful for both smaller and large retailers. Understanding store visits, as well as customer behavior through mobile device data, is having many positive effects on the retail sector. These insights can help inform business decisions such as store layout, opening times, staffing, and more.
Location data is an essential tool for finance. Device location can help to identify fraudulent activities and protect users with an added layer of security.
Understanding footfall through big data sets is valuable for the financial sector. Mobile device data can help to forecast earnings, number of customers and other KPIs before they are formally reported. These insights help to inform investment decisions.
Anyone looking to invest in real estate, or open up a new store branch can use location data to understand how busy certain areas are, what type of people you’ll see in certain areas and how well similar businesses in that area perform.
The rise in mobile location data has provided better opportunities to understand how cities work. It’s helping to create systems and infrastructure that reflects this.
Combined with the increasing number of connected devices in cities, central planning authorities now have a set of tools that can inform decision making in many different areas.
Mobile location data is contributing to a better understanding of where demand for public infrastructure is most significant. For example, we could examine mobile device location data to understand the most cycled roads within a city. This information is precise and invaluable when planning where to implement new cycling routes.
The same is true of traffic and congestion. In increasingly crowded and polluted megacities, it’s crucial to understand how traffic issues can be alleviated. Understanding traffic flow and where to build new road structures or introduce new low emission zones is vital to making the kind of smart city that can sustain current levels of population growth.
Location data can have a substantial positive effect on this kind of planning. Thanks to the accuracy and uniqueness of mobile device data and location intelligence, it is changing how decisions are made in cities and towns around the world.
Transparency – why do we need it
As the amount of location data available to companies increases, there is likely to be more bad data. Poor third-party data sets are becoming more frequent, with providers unable to validate the accuracy and precision of the data.
We’ve already discussed the need to for accuracy and precision in location data – the difference can mean a falsely attributed visit, irrelevant targeting or a negative impact on customer engagement.
The most accurate providers will be able to verify their first-party data sets. They can provide a detailed methodology around how they collect data. This is one of the main benefits of working with a provider that controls data collection – their data is first-party and therefore reliable and transparent.
Explainer: 1st, 2nd and 3rd party data
Third party data is data that is purchased from outside sources where the provider you are working with is not the direct collector of the data.
Second-party data is somebody else’s first-party data. This data comes from their first-party audience, the source is clear, and the provider usually demonstrates the accuracy and collection.
First party data is your data that is collected directly from your audience or customers.
Of course many companies don’t collect first-party location data so they work with a location data company to source the data for their campaigns, or other business needs.
In this scenario, second party data is much more reliable than third-party data. You can understand how the data is collected as the methodology is transparent, and the data accuracy can be verified. Of course, this doesn’t confirm that the data is accurate – but at least you can check yourself if this is true.
The best providers can explain how they collect data, how they filter out inaccurate data and can usually provide a reliability score with data to allow the end-user to understand the data they are working with.
2018 saw the introduction of GDPR in Europe. In the US, the upcoming CCPA act data privacy will still be front and center in the data community. We are quickly moving towards a world where each individual will have control over their data.
Businesses using location data will need to take a similar approach. It’s pivotal to allow the individual to take control of their data. Companies must inform users of how their data is used. They must provide clear opt-in and opt-out solutions so that transparency can be placed at the center of the big data revolution.
Businesses that utilize location data will need to be clear about how they collect and use consumer data. Location data providers need to have a clear opt-in process that allows consumers to understand how their data is used.
Data providers should provide solutions at the point of collection, which allow them to manage consent preferences through to the point of data use.
As with the verification of accuracy, understanding data privacy is more accessible if your provider is working with first-party data.
For example, at Tamoco, we have built consent functionality into our SDK. This allows the publisher to collect user consent at the point of data collection in accordance with the IAB framework.
For the data user, this means that they can understand how consent was given, and for which purposes.
When choosing a location data provider to work with, there are many things to consider. With several different sources, signals, and methodologies available, it’s essential to understand exactly what each provider is offering.
We have put together the following list of questions that are useful when selecting a location data company.
Questions to ask a location data company
What is the source of this data?
How much of your POI data is 1st party vs. 3rd party?
How do you organize the geographical area around a POI or place?
Can you share how precise your POI/place data is?
How many POI locations do you have?
What metadata is associated with these places?
How do you verify your place database?
How do you collect location data? Is this process first-party, or is the data 3rd party?
What type of device data do you use (GPS, wifi, beacon, etc.)?
Is your data sourced from an SDK?
Do you have a method in place to filter out data that isn’t relevant for my campaign or merely inaccurate?
what is the scale of your dataset?
The number of companies in the location data space can make it hard to differentiate between them. Below are a few red flags that you should keep an eye on the next time you’re speaking to one of these companies.
All of our data is accurate to 5m
Some data providers will make big claims regarding how accurate their GPS derived data is. As mentioned earlier, GPS can be accurate within a 4.9 meter radius, and this can be further improved when combining with WiFi and Bluetooth signalling.
The truth of the matter though is that GPS accuracy will vary massively, possible reasons for this are:
- Mobile devices lose and regain mobile reception as they move around
- Buildings, bridges, trees and roofs can block and reflect GPS signals
The better data providers don’t just look at the accuracy of GPS signals. They will take additional data fields into account, such as looking at the motion type, speed, altitude etc of the device to determine the likelihood of a device visiting a store at a given point in time.
Accurately measuring how a device moves is a complex issue, and you should be wary of data companies giving simple answers with blanket statements.
Our visit data is correct because of our precise polygon geofences
Accurately mapping POI is important to try to understand whether a device actually spent time there. However, a lot of data providers out there will claim that the reason they’re able to attribute POI visits is because of the precise polygons they’ve been able to draw around POI.
As mentioned above, GPS accuracy has a high degree of variability. You can have a precise polygon geofence around a 20 square meter retail unit, however if all the signals you place inside the geofences have +/- 50 meter accuracy, you’re not doing a good job at understanding who spends time in that POI.
Want to learn more?
At Tamoco, we are always innovating in how we collect and use device location. We’ve spent years fine-tuning our methodology to correctly verify how a device moves and behaves in the real world.