Categories
Marketing & Advertising

What Is Behavioral Targeting? – All You Need To Know in 2023

Advertising can be a challenging endeavor. Carefully crafted campaigns can often fall short of desired goals, with no apparent reason. Marketers can easily reach the wrong audience or fail to deliver the correct message that can covert or engage consumers.

Today, random targeting is a thing of the past. Marketers have a variety of methods to ensure that the right message reaches the right person at the right time. Advances in behavioral tracking and the increase of powerful datasets have enabled advertisers to boost conversion rates across both online and offline campaigns.

Campaigns that use behavior tracking and utilize behavioral targeting are yielding incredible results.

 

What is behavioral targeting

Behavioral targeting is a marketing strategy that uses historical behavior to personalize the types of ads consumers see.

Historical behavior is sourced through powerful datasets that illustrate how audiences behave. Marketers can then use this to create ads and campaigns that match each consumer’s actual behavior.

Behavioral targeting involves building up a detailed user profile and using this to deliver better messaging and better timing. It limits the possibility of advertisers delivering irrelevant ads and helps to boost advertising campaign KPIs.

 

What are the benefits of behavioral targeting

Behavioral targeting is a powerful marketing tool that is rooted in the modern, data-centric world that we live in. But it isn’t all about using numbers and tech. Behavioral targeting provides value to both advertisers and consumers.

 

Advertiser benefits

Improved engagement for advertisers

Understanding consumer habits helps advertisers to identify audiences that have engaged with specific products or touchpoints. It also helps to identify audiences that are in the right moment or behavior for a particular campaign. Targeting users with no behavioral intent or brand awareness will limit engagement. Using behavioral targeting will increase a number of critical metrics, such as clicks or conversions.

 

Matching consumer needs with creatives and messaging

Personalized messaging converts more users and ultimately reduces the amount of wasted ad spend. Relevant ads are much more likely to move consumers along the purchase funnel than generic ads that are not personalized. Ads that align with a consumer’s previous behavior are much more likely to convert than ones that don’t.

 

Improving the bottom line

Ultimately advertisers want to get the best possible return on investment on their campaigns. Delivering ads that match with audiences previous behavior is more likely to drive conversions than ones that are generic. With behavioral targeting, companies can see a rise in new business, repeat customers, engagement, and other key metrics.

 

Consumer benefits

An improved ad experience

Consumers aren’t always keen on giving up their personal data. But they also dislike ads that aren’t relevant or ads where the experience is unengaging. That’s why, when surveyed, more consumers prefer personalized advertising. This personalization ultimately improves their experience.

 

Better efficiency

Ads can be a quick route to purchase, providing a fast way of identifying the best product for their needs without a long searching process. This increases efficiency for consumers, allowing them to get to storefronts quickly and finding the most relevant products, rapidly.

 

Awareness of new products

By seeing ads that are personalized to them, consumers can keep up to date with new products that interest them. Retargeting based on behavior can also help to complete purchases that a user was distracted from.

 

Publisher

As well as behavioral targeting benefits the advertiser and the consumer, it also helps the publisher. Where these publishers use ad monetization as a revenue stream, the ads mustn’t be irrelevant to the user as it might reduce engagement with their product, app, or publication.

 

How does behavioral targeting work in 2021

The process of behavior-based targeting on the highest level consists of collecting information about a user or a person and then using this information to deliver ads that match this information.

Collecting information can be done in many ways, and it can come from many different sources. Often a data management platform (DMP) is used to aggregate this information for advertisers.

Here are some common data sources that are used for behavioral targeting:

These sources provide a huge variety of data that includes:

 

Website cookie data

Data on how users behave and interact with websites is a valuable method of behavioral segmentation. Users spend a lot of time browsing the web, so the information is rich – pages visited, for how long, in which regions. Therefore these insights can provide a lot of information that is useful to boost engagement and conversions.

 

Mobile device data

Cookies also work on mobile devices. Understand the behavior of the potential customer on a mobile device can help to understand which format and which message could work best in an advertising campaign.

These web-based insights can be combined with social signals, check-ins, and mobile purchases to understand the best way to target audiences.

 

Geographic location

Anonymized location can be extremely valuable for advertisers. Especially when accurate and precise. Since the early days of bidstream datasets, device behavior can be accurately tracked to build up detailed profiles of behavior than can form powerful, behavioral-based segments for advertising.

 

Subscription data

Businesses that have some log-in system require the users to enter details and information about themselves. These fields can be used to understand the users, with address, interests, and contact details help with behavioral targeting. Let’s say you are looking to purchase a Notion template, it makes sense to pre-fill any forms with relevant subscription data, if you have it.

 

Demographics

DMPs and other marketing software can collect large amounts of demographic segmentation information, such as age ranges, interests, and gender, to create a detailed profile of audiences. This process usually works without using personal information but these ranges are used to create campaigns that can communicate more personally with audiences.

 

The process of behavioral targeting

The data collection process

User data can come from several different sources. Depending on the source, there are many different ways to collect data. For website behavior, a pixel is used. This process creates and updates cookies that understand how the user interacts with the site. Apps have a similar process. SDKs can collect other behavioral information, such as location data.

This data is usually stored in a DMP, but there are other adtech solutions for storing this information.

 

Organization and segmentation

Once this behavioral information exists in a central location, the next step is to sort individual users into groups that share the same behaviors.

This segmentation varies significantly depending on the company, product, or goals. For example:

  • Potential customers that go to the gym
  • Visits gym location 2 times a month
  • Current customers who like meat
  • In CRM and visited meat weekly delivery page
  • Users who are interested in SEO (maybe even more specific, like a this one, this Bristol SEO agency, or even a industry specific one like this one)
  • Existing customer who have read at least one blog post related to SEO trends 2020

 

Delivery and application of behavioral targeting in advertising campaigns

Specific ad campaigns are delivered to match each segment. This process makes the advertising more relevant for each segment and increasing the chance of engagement and boosting conversions.

 

Activating behavioral targeting

All of this behavioral data can be used across multiple campaigns and in different advertising channels. That’s the benefit of having a centralized place to store the data.

There are multiple ways to activate this data to create behavioral-based ad campaigns that deliver. Here are some examples of how to enable behavioral targeting to drive engagement and increase conversions.

 

Examples of behavioral targeting

Cross-selling and upselling

Knowing what your customers like and understanding how they interact with your business is a powerful way of knowing which additional products to promote to them. If you can link product A and B, then your audience that has shown interest in product A that are likely to engage with a campaign promoting product B.

 

Behavioral targeting in targeted email campaigns

That’s right, and behavioral targeted email campaigns doesn’t just sit in the world of programmatic media advertising. Creating personalized email campaigns based on how your audience is using your site or app is a great way to start.

Examples include targeting cart abandonment sessions, including viewed products in routine updates or directly linking content related to what your audiences have already read rather than generic content. Behavioral targeted email campaigns is a powerful way to increase email productivity and boost your targeting options.

 

Remarketing with behavioral targeting

An advantageous and accessible way of using behavioral targeting is to retarget. By identifying users that visit your site, you can reach them on other websites to encourage them to visit again and complete goals.

The most common solutions for this are facebook and google as they have simple to install tracking pixels that can understand users that visit specific pages on your site. You can then activate these segments directly in their platforms.

 

Location-based targeting

Location-based targeting is an excellent way of reaching audiences based on their real-world behavior. You can retarget audiences that have visited your physical stores, or a competitive store. This can also be applied to e-commerce and other online stores.

These targeting campaigns can be useful because the insights are related to how consumers behave in the real-world. This allows you to create compelling segments based on how people behave over time.

What is behavioral targeting?

Behavioral targeting is a marketing strategy that uses historical behavior to personalize the types of ads consumers see.

What are the benefits of behavioral targeting?

Behavioral targeting can deliver better engagement, better messaging, and better marketing results.

How does behavioral targeting work?

The process of behavior-based targeting consists of collecting information about a user or a person and then using this information to deliver ads that match this information.

What are some examples of behavoral targeting?

Cross-selling, targeted email campaigns, remarketing and retargeting, location-based targeting

 

Categories
Business

The software development process – a detailed guide

Introduction

Among all stages of elearning software development, data privacy is considered of high importance. Such elements as consumer rights, management of customer information, and data breaches are significant for a company’s success. If you ignore data privacy laws, you should be ready to pay a lot of money, waste time, and probably lose your business’s reputation.

Software elaboration teams should be familiar with current data privacy laws and make sure the software apps, created by them, take into account regulatory mandates. Moreover, these developers should elaborate software solutions in a way that flexibility for future regulatory requirements is ensured.

Privacy Rights Every Customer Has

Software elaboration requires much time and effort. If you believe you have no sufficient skills and experience, you can rely on special educational software development companies that will perform the entire work instead of you. Thus, if you link to jatapp, you can discover real specialists. Here you can hire professional and dedicated elearning software developers who will assist you to create an engaging e-learning solution.

For example, these experts are aware of what privacy rights consumers have while elaborating a software. Thus, guidelines and best practices have been established that enterprises should follow to defend and utilize customer data securely and ethically. Laws define the main data privacy rights consumers are able to make use of freely.

·         right to removal or correction of imprecise personal information;

·         right to knowledge of what personal information the e-learning software development company is gathering about customers;

·         right to have access own personal information and knowledge whether the company is utilizing it;

·         right to knowledge of what personal information the company is selling and who buyers are;

·         right to refusal of the sale or sharing of customer’s personal information;

·         right to limitation of the utilization of customers’ sensitive personal information by a company, etc.

These laws establish that consumers possess all the rights to their personal data. Every educational software development company must abide by data privacy laws and do with that information exactly what consumers request them to do.

Data Privacy Principles during Software Elaboration

In the beginning, all developers were wondering how and what they should perform in order to adhere to data privacy practices. Let’s consider detailed significant steps that businesses should undertake in their way to ensuring data security and compliance.

Keep Customer Data Reliably

Previously, companies did not answer for losing customer information. Only those who stole it were responsible for this. Still, nowadays everything has changed and enterprises started to hold responsible if they do not possess measures for proper protection of consumer data. Nevertheless, current data security laws as a rule utilize vague language and do not provide detailed and concrete security practices for developers to stick to. The laws tend to offer security measures titled “best practice” and “reasonable”. Since software security is constantly developing, all detailed laws require standing reconsideration.

Although prescriptive decrees are not available, nevertheless, some methods exist to assist elearning software companies to improve the data security of their software apps. These methods are the process of integrating proper data-mapping methods into software systems to guarantee companies are completely familiar with all the data they gather, where it is kept, and the way it passes through the business. While creating software, elaborators need to operate with their business partners to reduce data gathered in the software app to just what’s needed. For extra protection, all data should be kept reliably and optimally encrypted at rest.

Besides, it is recommended to abide by DevOps hygiene rules such as automation and uninterrupted monitoring for eventual data breaches or malware. Adopting these rules can assist in improving code security. Moreover, two-factor authentication establishment in software apps will be useful. These measures can be easily combined with other measures, for example, staff education in data security, carrying out formal risk assessments, and maximum access limitations to data.

Only after these practices are put into effect, your educational software development business will be better defended from stolen data. Also, your business’s position will be improved if it will undergo checking for compliance.

Develop Data Security and Privacy Policy

Among guiding principles in data privacy laws, there is one of high importance. This is data transparency and it implies your company provides a clear representation of what data is gathered from the consumer and how your business utilizes it. Adhering to the rights of consumers and granting them capabilities to be aware that their data is secure and available is a clue to data privacy in software elaboration.

Such transparency must be brought to the attention of the public with the assistance of a data security and privacy policy on the website or software app of any e-learning software development organization that gathers and keeps user data. Every data privacy policy should include certain requirements.
Some of these requirements are:

·         “what do you gather?” (do you gather personally identifiable, behavioral, or payment data?);

·         “how do you gather it?” (do you utilize third-party cookies or data from social platforms?);

·         “how do you utilize it?” (do you utilize data for marketing or functional and transactional purposes?).

Being transparent and having privacy policies can assist your educational software development organization to prove it is compliable with data privacy laws.

Conclusion

Transparency in how you gather the personal data of consumers and what you do with it can be handy in complying with data privacy laws. What’s more, is that it can assist you to strengthen the trust of your customers. A starting point can be an understanding of what customers desire from a point of view of data privacy involving transparency, safety, and portability in combination with solid communication and clear comprehension of their rights. Educational software development companies should take into account these business needs when elaborating on software apps.

Both companies and customers get benefits from knowledge of data privacy laws and consumers’ rights. Creating software solutions with the possibility of flexible adaptation and response to changes in data privacy legislation can assist you to improve the position of your business to achieve success.

Categories
Data

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
Apps

Best App Revenue Calculator – Calculate Ad Revenue 2021

Our new tool allows you to calculate how much revenue you can make from your app. For a detailed breakdown of how the calculator works, please scroll down.

About the app revenue calculator

This tool allows you to understand how much revenue you can make from different monetization strategies. The tool will tell you the monetization potential of your app across different monetization strategies.

The calculator focuses on two monetization strategies – advertising and data monetization.

 

What the results mean – how the ad revenue calculator works

In-app ads

To calculate app revenue for advertising the calculator needs the following input:

  • Average daily sessions
  • Session duration
  • Ads delivered per minute
  • OS breakdown – this is because CPM can vary significantly on each OS

We have researched each ad monetization platform to create an average CPM for each OS on each platform. Based on this, we can calculate roughly how much your app can make from both iOS and Android users on each ad monetization network.

Of course, these revenue estimates are a general guide. You might have an app that is more effective at delivering ads. You might also try another type of ad format, or receive payments on a conversion basis. This calculator considers cost per 1000 impressions (CPM) on a display ad inside a mobile app on a specific operating system.

The average CPMs for each mobile advertising network are Applovin, AdColony, Admob, InMobi, and Chartboost. The CPM of each network is visualized below

 

What is CPM?

CPM means cost-per-mille or cost per thousand. This essentially means how much you’ll earn from a thousand ad impressions.

Data monetization/Location monetization

The other type of monetization that this calculator looks at is data monetization. To achieve this, it requires some similar user metrics. It also requires the general breakdown of where your users are in the world.

As location monetization requires user opt-in, you’ll also need to estimate the number of app users that opt-in to location permissions on your app.

The calculator works by using Tamoco’s CPM range by country (what we already pay our app partners) and uses this to project what your app could earn.

This method of monetization can be used alongside advertising to supplement advertising revenue.

What is app monetization

App ad monetization

In-app advertising is one of the most common forms of app revenue. Publishers allow networks to deliver ads to their users for a share of the revenue from advertisers.

As the industry has developed many different ad forms are appearing. From interstitial to gamification, there are now hundreds of app formats in mobile applications. For a full breakdown, check out this guide.

 

Location monetization

Large app audiences can be valuable for many different reasons. One of these is that whenever a user interacts with your app, they generate a form of data.

This information can be anonymized and then quantified. It can then provides valuable insights into customer behavior. This is known as big data. It is used for many things – from how to build smart cities to deliver better and more personalized advertising to users.

Again, the following guide is the best place to understand app monetization.

Bonus: If you’re a freelancer or a solopreneur who manages and monetizes his/her own app, you can use this hourly rate calculator to get a better understanding of how much your earnings should be compared to your expenses.