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What Is Location Data? All You Need To Know In 2023

This guide will tell you everything that you need to know about location data:

Introduction

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 is 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. Its applications are broad and run across a number of different industries and verticals.

But before we get onto that, what exactly is location data?

What is location data?

The smartphone

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 with 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 identity 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

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

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.

 

Beacon

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.

 

An identifier

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 latitudes and longitudes 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.

Location data sources – where does location data come from?

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. It’s also important to have a data governance strategy to manage the data effectively.

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:

 

The bidstream

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”

 

Telcos

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.

 

Location SDKs

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 location based app is 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.

 

Publisher datasets

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

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

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.

 

No POI

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.

Bad POI

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.

Places database

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.

 

Location data use cases – how to use location data

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? How do you integrate this data effectively?

 

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

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

Visits

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).

 

Interests

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?

 

Programmatic

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.

 

Some examples

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. B2B content 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.

 

Examples

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.

 

OOH

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 analysis 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.

This analysis 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.

 

Beyond advertising

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.

 

Retail

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.

 

Finance

Location data is an essential tool for finance analysis. 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.

 

Real estate

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.

 

Government

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.

 

Verification

Transparency – why do we need it

As the amount of location data available to businesses 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 businesses 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.

 

Privacy

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. Businesses 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.

Companies will now need a robust framework of data management and governance to move forward.

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.

 

Buying location data

So you have a valid use case for location data, how to do you go about purchasing location data?

We have put together this section to help you to understand what to look for when working with location data providers.

 

What does good location data look like?

As we mentioned earlier, it’s important to look out for a few things when buying location data you should look for the following attributes.

Quality

In terms of quality, you want location data to be accurate, and you want it to contain the attributes that you need to achieve your goals. In terms of accuracy, you should look out for a score in the data set. This will tell you how much you can trust a data point. It might make sense to filter out data that sits below a certain level of accuracy, depending on your project. Don’t be afraid of asking your location data provider if they can give you a trust or accuracy score.

Then you should look at the metadata. Most providers will provide more than a lat long. As a good rule of thumb here are the attributes that you can expect to find in a location data set:

Field

Description

Example

device_id The advertisment id for the phone 6E82079C-8346-4DA5-BF5B-76214862F7DC
device_ip IPv4 address 192.0. 2.146
event_ts The timestamp of when the location observation was collected 2020-12-06 16:39:28.000 UTC
latitude The north-south position of a point on the Earth’s surface. First part of the device location 30.27297
longitude The east-west position of a point on the Earth’s surface. Second part of the device location -97.731528
geohash A 1.2km x 609.4m grid which the Latitude and Longitude falls under 9v6s0p
accuracy The accuracy of the device in meters 10
region ANSI standard two letter state code TX
country ISO 3166-1 alpha-2 country code US
device_type Device type Phone
device_os The type of operating system. iOS or Android Android
device_make Extracted from the phones user agent. Defines the device manufacturer Samsung
device_model The device model number. SM-A300FU
app_id A anonymized and internal identifier for a unique app supplier 2684758

 

Scale

When purchasing location data you need to ensure that the provider has sufficient scale in the country where you need the data. You should consider how large the country is and then ask how many unique devices are in the dataset. This ratio will give you a good idea of the provider’s coverage in that area.

Some projects (training AI) might require a higher level of coverage, whereas other use cases could be workable with less.

Ready to buy location data? Speak to one of our experts today.

 

Best location data providers

Ultimately different location data providers and companies will be better suited for different types of location data.

Here we’ve don’t the legwork for you and broken down the best location data providers for each data type.

Best provider of raw location data

Raw location data is location data or mobility data in its purest form. This data is often pseudonymized but you should check with your provider. The primary use cases here can be vast, but they can feature in a number of industries from automotive to finance and marketing.

Best provider: Tamoco.

At Tamoco they carefully curate personalised feeds or raw data. It’s powered by leading ML algorithms to filter out bad data and it’s fully filterable by region, time or another attribute. Best of all it’s delivered programmatically in a way that suits your project.

 

Best place to buy property data

Property data is used to augment raw data to provide an outline of buildings and or pieces of land that don’t show up on a classic map.

The primary use of these datasets is to asses building risk factors.

Best provider: SafeGraph.

 

Best place to buy visits data

Visits data is a form of mobility data that is used to identify how many times a device is seen in a particular location. This data uses a proprietary technique to attribute. raw location point to a POI.

The used for this are found in retail, finance and governance.

Best provider: Tamoco.

 

Best place to buy mobility data

Mobility data is counts of people that visit a POI but they are generally anonymized and done on a higher level than a single POI. This kind of data will usually include timings such as average times of visits.

This data type is used for advertising, urban planning and insurance..

Best Provider: Tamoco.

Tamoco provides extensive mobility data and is one of the leading mobility data companies that operate globally. Its smart tech allows datasets to be generated quickly based on fully custom requirements.

 

Where can you buy location data?

You can find location data in a number of places from marketplaces, and exchanges or by talking directly to location data companies. These companies will have a dedicated team of experts who can help you to understand the nuances of location data, mobility data or geospatial data.

 

How much does location data cost?

The cost of location data can vary hugely depending on your use case or the size of the dataset. Other factors that can affect the cost of location data are the region (some country’s data is worth more than others) or the quality.

 

Questions to ask a location data company

Place/POI

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?

 

Device

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?

 

Red flags

The number of Businesses 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.

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.

How accurate is location data?

Location data is only as accurate as the source. GPS is usually the most reliable but only outdoors. Usually a combination of Bluetooth, GPS and other signals will provide a more accurate reading of device location.

Is location data compatible with GDPR?

Yes. 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.

What is location data used for?

Location data can be used to target, build audiences, measure and gain insights and understand the offline world.

Categories
Data

Location Data Accuracy – What Makes Good Location Data

Location is a powerful tool to understand how audiences move in the offline world. It is being used across a number of industries, fuelling everything from innovative mobile apps to providing detailed intelligence and analytics around device movement.

For these applications to prosper the data that underpins them needs to be accurate. This is not always the case. We’d like to talk about some of the attributes which make location data reliable and actionable.

 

Accuracy and precision

These two things sound similar. But in the world of location, there is a subtle distinction. Accuracy refers to how close the measured location is to the actual location of the device. Precision refers to how close together a number of separate measurements are.

The precision provides the granular insights that overlay the accurate data sets. At Tamoco we add this precision through sensors and our proprietary SDK.

 

How we determine device location

Tamoco uses sensors that exist on our network to help with issues in accuracy.

The Tamoco SDK identifies these sensors which are in ‘known locations’. This allows the SDK to determine where the device is in relation to wither Bluetooth or wifi signal booster strength.

In some locations, GPS location can be inaccurate and imprecise. For example, inside a shopping centre where the GPS signal is not as strong. This is where Tamoco’s sensor-driven approach provides extra precision.

What are the sensors and what are the benefits?

Beacons

Beacons provide very accurate and precise data. These sensors can be used to place devices with an accuracy of 1m. Beacons are the most precise location sensor that is widely deployed.

WiFi

Very useful in identifying precise location in densely populated areas. These sensors help identify device location in areas such as shopping malls and large buildings.

GPS

Under the right conditions, GPS can be extremely accurate. The signal quality deteriorates quickly when the device is indoors and in areas where the device does not have an unobstructed view of GPS satellites.

The Tamoco SDK uses multiple sensors simultaneously to identify the location of the device. Often the device location is constantly updated as more sensors are identified. The SDK uses this information to determine context and then discount outliers in the data.

This process of identifying location can be visualized as a linear process. In this process, the Tamoco SDK uses a number of sensors to validate and adjust an initial location signal to a finalized and verified location, in the form of latitude and longitude.

 

Other factors that help to identify location

We use other device information to identify the accuracy of each location signal. We use vertical and horizontal accuracy to show the reliability of the lat-long derived from the device.

These fields are intended to provide transparency around location signals. Tamoco wants to include these so that partners, developers and clients can understand the level of accuracy in every data point that they process.

Standardizing this into an industry-wide and agreed measurement is the next step. Only location data providers with inaccurate data sets will have a reason not to adopt these standards.

 

Benefits

Why is this relevant for publishers?

These levels of accuracy mean that app partners can maximize their CPMs from the monetization of location signals. Many monetization partners pay higher amounts for accurate data sets. They are, unsurprisingly, unwilling to pay for data that is either inaccurate or imprecise.

If you use location in your app experience adding extra accuracy and precision will boost the experience for users. Delivering location-based communication and contextual app experiences are more likely to be a success with greater location accuracy.

Why is this important for data buyers?

Of course, the most important thing for a data buyer is the accuracy of the data. This is true regardless of the desired use.

Accuracy in location data means that insights are more reliable. Targeting is more effective. Using location data for complex tasks like visit attribution is nigh on impossible if the data is unreliable.

All data buyers should be sure that the location data they use is verified. Location data providers should be able to explain their methodology in detail to demonstrate that data sets are precise.

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Categories
Marketing & Advertising

Best Guide To Location-Based Marketing & Advertising 2022 + Examples

So as a marketer you want to know how location-based marketing can help you to reach your marketing goals?

It’s time to take a serious look at location. Big data is tearing up the rulebook in a number of different industries. This trend continues with data-driven marketing becoming the new normal. Mobile has changed many things, but it’s having a huge effect on the way that markers are using data to reach their goals.

The missing link in this equation is location data. The rise in mobile adoption has provided a much better and accurate understanding of how audiences behave in the offline world. This location data is allowing marketers to do incredible things, based on cold hard evidence.

We’re going to look at some examples of location-based marketing. Get ready to see how you can use location-based marketing to create effective campaigns. Learn how to use location data to provide powerful insights and measure attribution with precision.

 

What is location-based marketing and big data marketing?

Location data can be seen as a branch of big data. When the term big data is used people generally think first about quantity. Whilst this probably has to do with the reason that the terms exist, big data isn’t really about quantity.

We think that data is big in the sense that the impact is big. We think of location data as big because of it’s quality in both application and insight.

With that in mind, we can define big data as the collation of data from multiple sources. To inform better decision making, powerful targeting, and improved attribution.

Location data is big data that uses information about a person or group of people’s movement or behavior. This is used to understand wider trends and patterns. Location-based advertising and marketing use this data to fine-tune marketing efforts. But it is also used to generate better engagement and get valuable insights into customer behavior.

 

How can my business use location data and location-based mobile marketing

For marketers, it has sometimes been difficult to understand the benefits of location data. Especially whilst trying to get around the technical side of how it works. In the beginning, many companies had inaccurate data sets. But now the science behind location data has advanced greatly. This enables marketers by providing quick and reliable results. All by incorporating location data into their marketing strategy.

These uses are now much more accessible and easily combined with existing marketing efforts. Plug and play location-based marketing is now available. With this in mind let’s look at some of the key marketing practices that benefit from location data.

 

Location-based segmentation

Audience segmentation is a key challenge for any marketer. In order to optimize marketing dollars, it’s important to make sure that you are reaching the right people. It can sometimes be difficult to get this right, and often involves a lot of hypothesizing and testing as well as optimization.

Location can help to build powerful audience segments as it’s a key indicator of intent. For example, let’s say you make the active decision to walk into a specific location in a shop. It is then likely, at some level, for you to be interested in some of the products in that location.

Location can also be used to build historical audiences based on location history. This means that you might target a group of people who are health nuts. You’ll have many ways of doing this currently. But location adds something that isn’t possible through traditional targeting options.

You can build an audience of people who visit gyms twice a month and have been to a dedicated health and fitness store in the past three months. If you have a strong idea of the type of target audience your product sits well with then location is a powerful tool for identifying custom segments.

An important point to make is that these audiences can then be used in the way that best suits your needs as a marketer. You can target them through social media ads or send the data straight to a trading desk, DSP, DMP or other ad network. You can use it to overlay custom audiences to understand overlap. You can even use location to see the accuracy of your existing audiences and targeting.

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Location-based targeting

Location tech is valuable for marketers because of the instant nature of data. Real-time insights allow targeting to occur in the moment, especially on mobile.

Location-based targeting is a powerful tool in any marketers arsenal. For targeting when users are in the right frame of mind for conversion, location is effective in driving engagement.

But location-based targeting is great for physical businesses with products in the real world. The ability to target audiences when they are either geographically close or in the right moment, location can be very effective in driving footfall or driving brand engagement.

An example is a location-based campaign that targets users when they are close to a physical store of venue. When the user enters a pre-defined location they are given a message that informs them of the CTA that is nearby.

This can be in the form on a push notification via a third party app on their phone. But it’s also possible for marketers to feed this real-time data into existing media buying tools that can deliver ads via other programmatic media. As long as this is real-time the audience is still in the relevant moment and therefore effective.

These kinds of campaigns get much higher engagement and conversion rates. Marketers can use location data to ensure that their real-time targeting is effective. With location, you’ll also get insight from these kinds of campaigns. These insights can help you to optimize your entire marketing department.

 

Location-based attribution

This is an area where location data is offering unique insights for marketers. The ability to measure the effect of advertising in the offline world is a relatively new concept. Especially at a level that rivals the detailed insights that are readily available in the digital realm.

Location-based advertising attribution is helping marketers to understand the complete effect of their efforts in the real world. Offline attribution is effective in a number of ways:

 

Measuring OOH

OOH, real-word adverts are big business for marketers. But there’s always been a problem – how do you measure the results? It’s difficult to attribute store visits or purchases to OOH. It’s also difficult to understand exactly how many people are exposed to advertising in the first place.

Location data makes these insights accessible. By listening to areas around OOH it’s possible to measure how many people have passed or remained close to the OOH advert. From this data, you can create insights on how many people have been ‘exposed’ to the OOH ad. Of course, this isn’t perfect as there’s no guarantee that everyone walking past saw or understood the message.

But location data makes it possible for marketers to then measure how many of these people perform the desired goal. This may be that they visit a retail store associated with the ad. This is an effective tool for marketers to be able to measure, test and optimize OOH advertising.

 

Measuring experiential or other offline advertising

Of course, this tech can be used to measure other forms of advertising. Take experiential, for example. Usually, these campaigns end with the consumer leaving with a sample of some kind. But attribution doesn’t come easy and many campaigns end with the basic insights. These are usually how many people visited the experiential stand, or how many samples were handed out.

But location data enables marketers to then say, with great precision, this many people engaged with our experiential stand. You can then identify the percentage of these people that visited the store within a certain period of time.

You can also generate a dynamic QR code to track, analyze, and retarget your customers. All you need is a dynamic QR code generator.

These insights are invaluable. They provide marketers with the opportunity to get digital insights on traditional offline marketing campaigns.

 

Measure the effect of digital advertising on offline goals

Location data is a powerful tool to associate online digital advertising to offline conversions.

For example, if you have a Facebook campaign you will have an idea of how many people saw your ad and even how many of these clicked your ad. But if your conversion is in the offline world, ie visiting a physical store, then this is where your campaign traditionally ends.

Sure there are some things you can do to pick up customers on the other side, like offer codes or loyalty schemes. But none of these will offer the same precision or reach as location data-driven marketing attribution.

Using location data marketers can understand the offline effects of digital advertising.

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Business intelligence and business analytics

For marketers, getting those insights on a micro and macro level are crucial when creating your strategy. In terms of insight, data is the new normal. You want to base your marketing decisions on data that is accurate and instant.

Understanding the customer is critical to any marketing department. Location intelligence is a powerful tool in the area of customer analysis. Try enriching customer data with demographic and anonymous lifestyle information. This allows marketers to create more effective databases and be better placed to predict where best to spend the marketing costs.

For brands with a physical venue or store location data can provide powerful insights into business performance. Understanding footfall and trends can help to inform on the ground business strategy. For example, retail location data can also help strategize how best to compliment physical retail stores with digital advertising.

Combine this with the ability to see data on competitors and other physical location and you have a powerful toolkit that marketers can use to put data at the centre of their decision making.

 

Personalization with location

For marketers, a key goal is to try and personalize the relationship between brand/product and the consumer/user. Of course, this can be difficult as it’s not always easy to completely understand your audience. It’s even more difficult to personalize your communications based on this, especially on a one to one level.

But analytical intelligence can be one solution to the personalization problem. Identifying the location of a customer can help brands and marketers to customize their message so that it is personal.

This could be a simple as including terms like welcoming back in your messaging. Or you can create entirely different communication for customers that are in different locations or have demonstrated previous patterns of behavior.

Communicating with your customers in this way can help to build stronger relationships and increase brand loyalty. This allows you to communicate with the right customer when they are in the right place with the right message.

 

How does location-based marketing work?

Location data is sourced from mobile devices. Sensors are used to understand and pinpoint these devices. This process is anonymized so that the user’s personal details are kept private.

These sensors come in a variety of forms – from beacons to Wi-Fi to geofences. Using a combination of sensors allows for greater accuracy and better scalability of data.

 

What is good location data?

You’ll need to make sure that your data provider is doing two things:

 

Can validate the accuracy of the data they collect.

This means you’ll need to understand what types of location data there is. Some are more precise than others. Some are real-time and others are delayed.

Generally, a data provider that can explain to you their methodology and is transparent about their data sources is a good start. Look for sensor-driven data sources such as beacons, GPS or wi-fi. First party data sources are much better than third-party, where the provider cannot validate the accuracy.

 

Can ensure that data is collected in a safe and secure way

Does your data provider have the correct opt-in procedures? Do they comply with current data collection legislation? These are all important questions that any good location-based marketing company will be happy to explain to you.

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What is location-based marketing?

Location-based marketing, also named geolocation marketing, is a form of mobile advertising that is highly personalized based on where the consumer is or has been.

How does location based marketing work?

Location based marketing works by using real-time device location to build detailed prolifes of how consumers move and behave in the real world.

Is an app needed to engage with location-based marketing?

Not at all, even brands without a dedicated mobile app can start using location based marketing to engage with consumers.

Categories
Data

How Big Data & Location Intelligence Is Changing The World

There’s no doubt that the explosive rise in the number of smartphones has changed the world as we know it. The increased number of sensors and connected devices has produced mountains of data. This is being used to transform the way that we live our lives.

IoT, location data, location intelligence, big data. Whatever your name for it, it’s hard to dispute the potential across a variety of industries

It’s now apparent that granular location data can provide unprecedented insight into the offline world. More businesses are realising the value of mobile location data and the impact it is having across the globe.

As we move away from unreliable data sets, sensor-driven accurate data sets are taking centre stage. This kind of accurate data has many applications. But I’d like to look at some that are having the greatest disruptive impact.

Business intelligence

The ability to notice trends by using data isn’t new. The ability to do this based on people’s activity in the offline world, in a close to real-time manner is.

Location intelligence reveals relationships between big data sets that often would be missed. It turns these insights into actionable business intelligence. Helping inform decisions, from the boardroom to the storefront.

From the small bar that is competing with huge chains of venues through to the small retailer competing with online mega corporations. These businesses are gaining valuable insights from using this kind of big data to inform their business strategy.

The truth is that mobile location data has now matured enough to solve many problems that both small business and enterprise face. Let’s look at a few:

Financial services – understanding footfall through big data sets is valuable for the financial sector. Mobile device data can help to forecast earnings and other KPIs before they are formally reported. This helps inform investment decisions.

Retail – big data can help both small and large retailers. Understanding store visits, as well as customer behaviour 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.

 

Infrastructure and planning

We’ve all heard of the term smart city. We like to think that there’s more to it than just adding a few data points and putting the word smart in front of it. It is, in fact, more than that. We’re moving towards urban centres with huge populations and aspiring towards self-driving vehicles. Big data is the key to unlocking this truly smart future.

The rise in mobile device 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 greatest. For example, examining 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 routes.

The same is true of traffic and congestion. In increasingly crowded and polluted megacities, it’s important 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 crucial to building the kind of smart city that can sustain current levels of population growth.

Big data is having a huge 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.

Marketing and advertising

Big data and marketing have always complimented each other. Marketers have always looked to use data sets to improve the efficiency and effect of ads. Using big data to create tailored and relevant audiences is not a new practice.

But mobile location data allows marketers and advertisers to connect digital advertising to how consumers behave when they are offline. Understanding how consumers move in the offline world is helping marketers to become more effective. It’s assisting marketers in providing more personal advertising to consumers.

Location intelligence is disrupting many stages of the consumer lifecycle. It’s bringing the analytical capabilities that have been available for the web to the real world.

 

Segmentation

Mobile device data is helping to build up complex pictures of how people move and behave. This helps advertisers to build complex customer profiles. Brands are finally understanding the places that their customers go and how they interact with the physical world around them.

This is far more effective than other methods of audience segmentation. This is because a person’s location is often a much greater sign of intent than when they are searching for something on a computer, or browsing on their phone whilst sat on the couch.

This allows marketers to identify exactly where consumers are on the buyer journey. Moreover, it allows them to do this with a greater level of detail.

 

Personalisation

One big breakthrough that big data has had on marketing and advertising is by increasing the ability personalise at scale.

Location data is allowing brands to be helpful and human by understanding the situation of the customer. The concept isn’t new, but the accuracy and increasing size of data sets in the space have allowed commutation to really get personal.

Location help provide promotions at the moment when the customer can actually redeem it. It allows the ‘customers also bought’ experience to reach the real world retail store. In this way, big data is providing digital solutions to offline problems. Location intelligence is tailoring brand communications to a person’s unique experience of the real world.

 

Customer experience

Big data has changed customer experience for the better. Location intelligence can help to automate way-finding, ordering, assistance and queue management. Understanding the physical location of a person has helped improve the guest experience across many sectors.

Stadiums, resorts, airports, transport hubs all stand to improve the experience of the people who spend time and money in these places. It might be location based ticketing – you buy your ticket by walking onto a train. Or it may be ordering food and drink to your location.

There’s still huge scope for big data and location intelligence to be applied to improve the customer experience.

 

Attribution

Mobile device data as we have seen has connected many digital walks of life to offline consumer behaviour. Another way that this technology is revolutionising the marketing and advertising space is through attribution.

Traditionally many advertisers have been blind when it came to measuring the impact of offline ads on offline KPIs.

But the mobile device location data is filling in the blanks. Location intelligence can understand when a person is in front of, for example, OOH advertising. It can then measure how many of these people are then seen inside a store or in front of a specific physical product.

Connecting the two provides an accurate way for marketers to measure the impact and ROI of offline advertising inventory. it also allows them to measure the effect of digital advertising on an offline goal. These things have just not been possible with certainty before. But big data has changed the way that advertising can be measured.

 

AR

If AR is really going to live up to its promises, it will have to rely on complex data sets and accurate location intelligence.

As AR gains prominence it’s application will move beyond fun to play games to useful productivity applications (you can even combine it with some powerful notion templates to really level up). As AR develops, it’s used as a way of reaching audiences with content and advertising will grow. Like previous marketing activity, it will be improved by the use of big data and location intelligence.

AR will require huge amounts of accurate and real-time location data to function properly as a user moves around the real world.

Optimising the supply chain

Big data and location intelligence is impacting organisations that want to optimise the supply chain.

The obvious application of location intelligence in the retail supply chain lies in the ability to understand and track deliveries and supplies. It already being used to generate data sets which can optimise and improve these services.

But location intelligence isn’t just helping business to optimise the process. It’s helping to understand the demand for products. There’s a lot of history of people building something in the hope that people want it to then find out that actually, they don’t.

Another way that big data is helping manufacturing industry to optimise is by helping it to adjust the type of transportation, pickup location or place of sale.

With the rise of location data, these insights are now fuelled by information from the offline world. Insights that have previously been unattainable or have lagged are now available in real-time. This lies at the heart of what is disrupting how the supply chain operates.

 

Privacy and transparency

As usual with new disrupting activity, the focus in on the responsibility of these new technologies. And rightly so. Indeed those in the big data space will need to be more transparent in how data sets are sourced.

It won’t be enough to simply check a box and start collecting and aggregating personal data. More needs to be done in order to clean up the data supply chain. More control needs to be handed to the user.

In this way, it’s our responsibility to communicate the value of big data and location intelligence to the user. It’s having a huge positive impact across the globe, and that’s just more reason to get the privacy part right.

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Learn more about big data

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Marketing & Advertising

The Evolution Of Location Based Marketing & Advertising

Let’s get stuck in with a definition.

Location-based marketing is the practice of using physical location to inform and optimize advertising, communication, targeting, loyalty and attribution.

This sometimes also known as location-based advertising or proximity marketing. At the most basic level, it means creating a one-to-one relationship with the customer. Emphasis is placed on communication in the right place, at the best time and with the relevant message.

 

A history of location-based marketing

Despite popular opinion, location-based advertising has been around for a while. Sure, it hasn’t always been backed by the smart technology in your mobile phone. Buy it has existed in some form. Local marketing strategies have been a key part of marketing since the practice began.

It might not have been as effective, but brands have been trying to target users based on location since well before most of you reading this were born. Advertising space has always been purchased based on its location. Be it a metro station in an affluent Paris Arrondissement. Or a teenager holding a sign advertising bagels in a certain street in NYC.

Location-based marketing has developed a lot since then. The underlying technology has advanced at an alarming rate. The ability to understand where audiences go and the ability to market to these is improved.

 

IP addresses and targeting over Wi-Fi

Location-based marketing has always been around. But, it did up its game in the 90’s once the internet found its way into most family homes.

The dial-up broadband revolution had begun. Most families now had internet access in their home through (a large) desktop computer. For marketers, this meant that advertising could be delivered to a user based on their IP address. Thus marketers could now target based on location, usually an area the size of a postal area.

There are a few problems though. Unless you have a very specific audience in a single geographical area, it’s difficult to ensure that you are targeting the right people. By today’s marketing standards, an entire postal area is just not specific enough.

Another problem that remains is the lack of ability to understand who is on the other end of the screen. When IP targeting was becoming popular, many families had a single computer. The whole family used this. Dad to research which TV to buy next. Mum to book the family holiday. Kids to play online games. This means that ad dollars could easily be wasted. As advertisers were under the impression that they were delivering highly targeted ads.

 

The beginnings of mobile targeting

Location-based mobile advertising was the next advance in location tech. Phones become truly mobile in the early 2000’s. They were used by the masses and even your gran had one. This meant two things:

It was now possible to advertise to an individual based on physical location whilst they were on the move.

Location-based marketing companies now had a means of segmentation down to a single person.

This was fantastic and all, but the main issues were still accuracy. Whilst the ability to target an individual on the move was well received, issues still remained. The area in which location could be accurately placed was still too large. Mobile targeting via phone masts was a vital step on the road to personalization. However, those in pursuit of accuracy were still frustrated. It often requires a form of phone validation to ensure the right person is reached.

 

GPS, geofencing and geotargeting

In the late 2000’s smartphones really took off. The first smart devices appeared and with the first iPhone, the race to create powerful mobile devices went mainstream. This led to rapid growth of mobile device adoption with GPS capability.

This changed mobile targeting for good. It was now possible to use a device’s precise GPS satellite positioning to understand device location. This process is known as geofencing, geomarketing, or geotargeting.

A geofence is a virtual boundary that is defined in order to perform a specific response once a device enters or leaves the defined area. More advanced geofencing is possible. An example is focusing on dwell time. Triggering a response when a device is within a geofence for a minimum amount of time.

The geofence allows for mobile targeting to occur on an individual level anywhere. It means that audience segmentation can occur based on individual movement. But it did more than previously possible. It was now far more accurate. This meant that personalization (relevant to location) was now available. Location-based marketing was now personal and precise.

 

Satellite problems and indoor confusion.

Location-based mobile targeting improved in accuracy with the geofence. However, on a precision basis, it’s still not perfect.

GPS has issues in some indoor spaces. So it might be incredibly effective at understanding where a person is in their car whilst driving. As soon as they enter an indoor space, GPS can be temperamental.

Some may argue that this level of detail is neither here nor there. But the problem arises when location data providers can’t differentiate between accurate data and inaccurate data. Advertisers and marketers don’t know when the data fuelling their campaigns is incorrect.

 

Beacons – iBeacon and Eddystone

You thought that it was location-based marketing you were doing before? How very wrong you were. Beacon adoption was a trend that changed location-based marketing and advertising even further.

Thanks to mobile devices and mobile targeting, advertisers would now focus all efforts on accuracy.

Beacons were the natural next step in this journey. Beacons are small Bluetooth devices that can interact with a mobile device. This interaction allows the device to understand exactly where the mobile device is with an accuracy down to 1 meter. Not only does it do this, it can measure positioning on a vertical basis.

There are two main types of beacon technology; iBeacon and Eddystone. These devices are deployed everywhere from shopping malls to sports stadiums.

This was the true beginning of proximity marketing. Delivering personalizable content with accuracy in precise micro-moments. These moments are relevant to the time, place and person. Beacons also allowed for accurate attribution in the offline world.

 

But what about scalability?

The problem with Beacons is that they require hardware to be deployed. Unlike Geofencing, where geofences can be set around any place that satellites can reach. Beacons must be physically deployed inside a store, or inside a football stadium. So these individual businesses can enjoy a high level of location-based marketing. Once a person leaves these areas they cannot get the same level of location-based insights.

 

Enter the network approach

The solution to this problem? The proximity network approach. To maintain beacon based, accurate levels across entire cities, it’s essential to collate this location based hardware. That’s what Tamoco’s proximity network is. It’s a complex network of location-based sensors. This allows for mobile targeting, location-based, marketing and location intelligence and insights at a consistently high level. Moreover, this is available whether the device is indoors, outside, on the first floor and so on.

One issue that many proximity marketing companies have had with location-based advertising is with scalability. Creating a proximity network of location allows for this scalability.

 

Sensory agnostic and a view of the future?

At Tamoco we take a sensory agnostic approach to location-based marketing. We believe that as technology advances, we need to able to adapt to the changing was that advertising, targeting and marketing will change.

A good example of this is the emerging tech around AR. An AR headset or device is still a sensor. Location is a means to an end for targeting through this medium.

By taking an agnostic approach and ensuring our network approach, we ensure that advertising and marketing can keep up with the cutting edge trends. Moreover, we can ensure that this is available with a precision and scale that those original metro advertisers could only dream of. Using location to improve marketing is here to stay.

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Data

Location Data And Location Intelligence In 2018

The big data analytics space is growing at an alarming rate. We’re all increasingly connected through our mobile devices. This growth has provided huge amounts of data around audiences and how they behave. What kind of competitive advantage can we expect to see from this location data going forward?

Location data is rising in importance. But there are challenges that companies face as location data becomes commonplace. Businesses will need to understand the best application for this data. We’ll look at this as well as how location intelligence will be put to use across many verticals.

How will location data change the way that businesses approach marketing?

Attribution

Location data will become invaluable to marketers as they look to close the online to offline attribution loop. Traditionally this has been a problem for marketers. It’s difficult to attribute real-world visits and purchases to online marketing. 

Location intelligence will help to illuminate what happens in the offline worldwith unprecedented accuracy. Big data will provide insights well beyond the capabilities of loyalty scenes.

As programming becomes more prevalent, location intelligence will be used to ensure that budgets are optimised. With the improvements in attribution, marketers will be able to accurately understand where ad fraud occurs.

Personalization

Big data breeds personalization. Many a case has been made for the importance of big data in personalization. Location intelligence will help marketers to automate personalization. This, in turn, will deliver accurate, personalized content to the right user in the right place.

Big data insights from location data

Location data will provide a competitive advantage, and not just in marketing and advertising. More industries are realising the benefits of real-time mobile location data.

Location data is being collected and stored by over 90% of companies with over 500 employees. The applications of this data are wide-reaching. Understanding consumer behaviour accurately, and in real-time will signal the adoption of location intelligence outside of the marketing and advertising industries. Location data will allow cities to become smarter. It will allow transportation to become more efficient. 

 

Accuracy and reliability of big data

The accuracy problem

The problem has previously been the accuracy of the data. For companies to use big data to inform business decisions, the data has to be reliable beyond doubt. Location intelligence and precise sensor-driven data now provide this certainty. 

Accuracy and precision will become of paramount importance as technology gets better.  It’s important that businesses that benefit from location data and location intelligence can safely say that they are working from accurate sources. 

First-party location data

Challenges in sourcing accurate first-party data were found with scalability. With a lack of first-party data, unreliable third-party data was used. Often out of date and imprecise, the insights were not reliable. Especially for businesses serious about using location intelligence for commercial success.

Today Tamoco adopts a network approach to location intelligence. This means that through our mobile SDK we can understand mobile device location directly. Thanks to our network approach, we can understand these signals across multiple sensors types. 

This improves accuracy and will allow businesses to act based on a more precise understanding of audience behaviour. 

Real-time data

Location intelligent decisions must be informed by real-time data. Location data must be instantaneous. Data that is out of date is simply not useful for businesses. This means that data must be communicated in real-time to optimise and achieve the desired goals. 

This is another reason why first-party data is important. It allows for location intelligence to occur quickly.

The location of things

The IoT will soon be in everything. For example, by 2020 IoT technology will be in 95% of new product designs for electronics. Now that’s great but it’s only the beginning.

This huge growth will produce large amounts of data. However, it’s may be difficult to interpret data sets without location. Location adds context and a better understanding of what is happening in a specific location. This allows us to better understand the offline world. 

The location of things – the IoT is actually only useful if you know where the thing is. Tamoco’s sensory agnostic approach allows us to gain location intelligence across a wide range of sensors. These range from beacons, Wi-Fi and Geofences to connected IoT devices. This approach allows us to understand location across many different sensors. This creates a deeper understanding of the offline world. Businesses using location data will have to take a similar approach to understand the connected world we live in.

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Marketing & Advertising

How To Improve Mobile Targeting With Location Data

Mobile advertising is growing incredibly fast. Smartphone adoption has never been higher. Accordingly, marketers are adopting a mobile-first approach. As marketers look to target audiences on mobile devices, they will need to adapt to stay effective at targeting the right person, with the right message at the right time. 

Mobile targeting is set to pass other channels as more budget is being allocated to reaching audiences on the move. To stay ahead here’s how to improve mobile targeting and advertising using location data and today’s location based marketing technology. 

 

Location intelligence is an effective method to improve mobile targeting

Location data is a useful tool to help target mobile devices effectively. It allows in-depth segmentation of mobile audiences. It also facilitates real-time message delivery based on a device’s location.

This level of accurate data insight improves the effectiveness of mobile targeting. Location based marketing has been proven to increase engagement and conversion rates. This is because mobile targeting occurs contextually, at the best possible moment.

Location data is used to build complex mobile audiences profiles. This is achieved by understanding these anonymous location signals. Mobile targeting campaigns should feel personal and relevant to the user.  This is achieved by applying offline behaviour to the targeting process. 

Adding location intelligence to mobile targeting means less chance of delivering content that is irrelevant or annoying. In a world of increased ad-blocking, users want marketing to be helpful. Location based marketing allows mobile targeting campaigns to be just this. They reach users in the best moment with personalized and useful relevancy of the content

 

Personalize mobile content and combine this with relevant mobile targeting

Location intelligence allows brands and marketers to target users in real-time with accurate and personalized content. Ensure that mobile campaigns combine accurate targeting with contextual content relevant to the user’s location. Personalization should extend beyond simply addressing a user by name. It should be personalized to a level that reflects the location and situation of the mobile device. 

Personalization is key to successful mobile targeting. Improving the relevancy of the content will improve the success of campaigns. High-level mobile personalization is achieved with accurate location data. Understanding the situation of a mobile user allows content to be targeted and personalised. Users are more likely to interact with content that is targeted to their situation. 

 

Make sure your data is accurate and precise

Using location intelligence to target mobile users in the right micro-moment will improve campaign performance. Targeting devices at exactly the right time ensure that you will reach the user in the best place at the right moment. This requires the location data that fuels your campaign to be precise and instant.

Ensure that your location data partner that can source accurate data in real-time to inform your mobile targeting campaigns. Ask how your data is sourced. Using third-party data may not be helpful as it could be incorrectly sourced or out of date. 

There’s a lot of bad location data out there. Effective mobile targeting should be based on first-party data sourced from precise sensors. For example, at Tamoco, we source location signals across multiple different sensor types. This helps to understand device location with accuracy. It also ensures that errors in location data are not included in data sets. Data is also sourced from first-party sources. This means that there is little chance of data being out of date, or lose accuracy through the reselling of source data. 

Marketers should take accuracy and precision seriously. It means that mobile targeting campaign budgets are as efficient as possible. It ensures that brands and advertisers don’t waste time and money targeting the wrong audience. Each advertising dollar goes further as brands get better, more accurate mobile targeting. 

 

Apply mobile targeting and attribution across channels

Use location intelligence to understand if your mobile targeting is working. Location based marketing provides accurate attribution after devices are targeted. This allows marketers to understand campaign performance in the offline world. The aim of your targeted mobile campaign might be to send users to a specific store. It makes sense to measure the performance of this using location based attribution.

Many mobile marketers aren’t aware of the huge potential for location in closing the online to offline attribution loop. It can help brands to make better mobile targeting decisions over time.

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Apps

How To Improve Your App Growth Strategy With Location Data

By 2020 it’s predicted that there will be over 5 million apps in the world. To compete with this number of apps you’re going to need to have a well-planned app strategy to keep your current users happy and find new ones.

If it’s your job to ensure that your app is growing its user base and engagement rates, you might be overlooking a useful tool.

Location data can help with many app challenges facing developers. Using location intelligence you can improve your app engagement strategy, fine tune your app marketing, find new app users and develop your app monetization strategy.

What is location data?

Location data is simply data that provides insights around audience movement. 

This means that users who opt-in to location sharing, receive an improved (or personalized) experience in return. This data has been used for ad targeting in the past, but its application in mobile apps is relevant for developers as it helps improve mobile app experience, and boosts mobile app growth.

 

Onboarding, user acquisition and app marketing

Your app marketing strategy can benefit hugely from location intelligence. When finding and acquiring new users it’s important to understand your current users as well as possible. This helps you to understand the type of user that will get the most from your app.

Location insights form your app can help you to understand how your users behave in the real world. These insights are valuable when creating an effective user acquisition plan. Using location intelligence you can understand patterns in user behaviour which can help you to fine-tune your app marketing strategy.

It’s essentially understanding your users and identifying traits that group them together. But using location data these insights are more profound.

You can use this data to decide where is best to spend your marketing budget. If you can see that your current users spend a lot of their time in cafes. Then it would make sense to focus your app user acquisition strategy on users in similar locations.

You can add location intelligence to your current paid advertising, be it PPC, social advertising or web adverts. By ensuring that only users that are likely to download and enjoy your app are targeted, you’ll get a better result, and you’ll save money.

 

Engagement

Your app engagement strategy is fundamental to the success and growth of your mobile app. Learning how to engage your mobile users is part of every developer’s journey – but this can be boosted with location insights.

At the heart of every mobile app engagement strategy lies push notifications. In-app engagement requires communication with your users. But, be very careful. It can be easy to focus only on app engagement metrics and overdo push frequency. This will only serve to annoy your users, and your app engagement strategy will suffer.

Best push notification practices include location. It’s all about communicating with your users in the right micro-moment. Using location this moment can be so accurately defined that your push notifications won’t feel annoying in the slightest.

To increase mobile app engagement you’ll need to define these moments. Using location data you can see in which moments (time and place) your users are most engaged with your app.

From here you can segment notification delivery to users based on where they are. Deliver only in these micro-moments to ensure that your push notification strategy stays relevant.

You’ll see higher engagement rates, your user experience will be improved and they will get more value from your app with location.

 

Monetization

There are many app monetization challenges that developers must consider. Of course, every app needs to generate revenue – but your app monetization plan must consider the user experience.

Many developers don’t realise that they are missing out on one of the most effective mobile app monetization strategies – monetizing app data.

Mobiel app audiences generate a lot of data. This data is valuable for many different businesses for a variety of reasons.

There are a few crucial benefits of this kind of app monetization strategy. First of all, you can say goodbye to in-app ads.

You can source location data in the background of the app – meaning that the app user experience is not affected by monetization – as it is in other app monetization strategies. That’s why location data monetization can be the best app monetization strategy in terms of user experience.

Secondly, you’ll get more app revenue per user. Location data CPMs can be up to 4 times higher than traditional monetization strategies.

As well as this, the platform becomes less relevant. If you’re tired of thinking of android app monetization strategies vs iOS monetization? Well, data app monetization is a way to level the playing field. You’ll get a much more consistent income across your audience, regardless of platform.

Of course, you’ll need a reliable partner that is experienced in securing big data. It’s also important to get a partner that can help you explain the process to your users.

 

How to add location to your mobile app

You can get access to all of these services with the Tamoco SDK It gives you access to location-based push notifications – which help you to improve app engagement.

The SDK will help you to access location insights around your mobile users. It will help you to form an effective mobile app monetization strategy. Or you can utilize the data for the personalization or improve your app experience.

For a demo of what you can do with location get in touch via the form below.

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Marketing & Advertising

From Location Data To Movement – Why Brands Should Pay Attention

From location to movement – why brands should pay attention.

As marketers are realising the importance of location data, it’s important that they understand the difference between location and movement. They must ensure that the data that informs their strategy is accurate. 

 

What are location signals?

Location signals are useful for many different kinds of marketing, and can even provide value for a wide range of businesses beyond this domain. When a mobile device notices a location sensor, it can discern its position in relation to this sensor. This occurs in the background, the user does not have to be on their phone. As most people carry their mobile device on them regularly, this means that it’s possible to understand audience behaviour in a multitude of places. This has not been possible before. This is what we call the offline world and it’s an area that is incredibly useful to understand for a variety of reasons. Offline to online marketing is becoming more accessible for brands as the results of its effectiveness come in. 

 

Why location signals should be accurate

If “data is the new oil” then the quality needs to be considered by those buying the data. Many location data providers are sourcing unreliable location signals. These individual signals are often only from one sensor and can often be misleading for the end data user. By using a series of different location sensors and multiple location points location data becomes more reliable and actionable. Much location data that is sourced from older methods are broken. This needs to be fixed. Accurate location signals combined with complex layering of location data means a moment away from individual, vague location signals towards audience movement – a leap that is much more beneficial for mobile marketers.  

 

The move towards movement data

It’s important to note that there may be single location signals which do not accurately represent a person’s behaviour. A great example of this is receiving a location signal which places a device close by to a brand’s store. This seems useful for the brand, but multiple data points are instead required to create a detailed profile for this person. Using one location point doesn’t tell us if the person is entering or leaving the store. We don’t know where they came from. We don’t know which section of your store they visited. These extra points are accessible with a network approach to location data. That’s the benefit that we have over deploying sensors only inside your store.

Movement data is much more valuable as it demonstrates a person’s intentions and provides much more context around audience behaviour. By using multiple signals it’s possible to get a much deeper understanding of customers and to eliminate potential data outliers. 

 

Layers of location data and complete understanding of consumers

As a brand looking to get the most from accurate location data, it becomes important to focus on this movement instead of individual location. It also means creating multiple layers of location behaviour and building complex understanding of audiences in the offline world. This means constructing accurate profiles around audiences and providing context to understand how and why customers are interacting with your venue or product. For example – realising that someone commutes from an affluent borough and works nearby a jewellery store would constitute a context ripe for marketing for said upmarket jewellery store. 

It’s these layers of location data that build up complex contextual audience profiles and facilitates accurate attribution and allows brands to communicate with their audience with personalised one-to-one marketing. 

 

Application in mobile marketing

Movement signals become extremely valuable for business when applied to mobile marketing. In-depth location data allows brands to communicate one-to-one with their audiences. By understanding the customer journey completely brands can deliver content to audiences that are relevant and valuable to the end user. This level of personalisation involves communicating with audiences in certain locations. This is the traditional application for mobile location data, but this is just the initial step once we have a large number of movement data. 

There are multiple applications beyond content delivery based on movement. Large scale data around consumers offline activity provides incredible insightsthat brands can act upon across their entire marketing strategy. You might realise that many of your customers work nearby, and browse your products on their lunch break. Maybe most visitors come via a specific tube station. This is valuable information when thinking about OOH advertising or store planning. The development from location signals to complex audience movement data should be at the centre of any effective marketing strategy. 

Another area where this is applicable is retargeting. Movement data can help to segment audiences and target people who are further along the consumer journey. This means a much higher ROI and by reaching audiences at the right time with the right content, more effective results. The important aspect of a network approach to this is that this movement data can be understood in external locations – not just inside your own venues. 

It turns out that not all location data was created equally. It’s now time for marketers to push their marketing strategies towards movement.

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Marketing & Advertising

Location data and the leap towards smart advertising

Brand advertising is changing thanks to location data. It’s becoming much more personal and the relationship between advertiser and consumer is evolving.

 

Connecting the offline and the online

Location data is leaving its mark on marketing and advertising. Huge amounts of data on what goes on in the offline world is allowing brands to change the way they communicate with their customers.

Location data provides insights into the offline world. This works by understanding where a mobile device is in relation to a proximity sensor. This allows brands to understand the customer journey in great detail, and more towards more personalised, intelligent advertising.

 

Personalisation is key for marketers and advertisers

Before these kinds of insights were possible, brands used targeted adverts to reach their audience. The advert was often the same regardless of who engaged with it. There was little place for personalisation. The impact of accurate location data has produced the move away from this traditional method of advertising to new personalisation strategies and the desire for brands to create a one-to-one relationship between brands and their customers. This kind of advertising enjoys much higher engagement rates.

This is happening because of location data. It provides the insights that can facilitate this new kind of advertising as it paints a clearer picture of what is going on in the offline world. Personalised advertising is booming and should form an essential part of any digital strategy.

 

Answering the why and how

It’s now possible to understand the consumer journey in great detail. Methods of collecting location data have made this scalable, allowing brands to create a complex understanding of the behaviour and interaction of large audiences.

These insights answer important questions around consumer behaviour. This level of insight has previously been unattainable. Brands can now confidently answer the why question confidently. That is – why consumers are in certain locations at certain times and why they are interacting with certain brands or products. This attribution is now extended to the offline world.

It provides brands with the ability to learn and adapt in ways that have previously been impossible. This allows brands to be able to answer complex questions surrounding their customers, products and marketing strategy. With location data, brands can build up complex understandings of their customers and locations. This helps to answer important questions, such as why is footfall up on Wednesdays? Or why a certain product is outselling another?

 

A complete, smarter kind of advertising

Brands are utilising this and developing a much more effective way of communicating with their customers. This involves a one-to-one method of advertising. This feels personal and relevant for the customer and provides them with value. Audiences get content that is appropriate to their situation, rather than annoying adverts that they don’t want or need. Location data is changing digital and mobile marketing, and the way in which brands and audiences communicate.

Contextual advertising and location-based mobile marketing is the best way to personalise the experience. This level of personalisation, based on experience, is part of the driving force towards a new kind of advertising.

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Discover location based marketing

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