Marketing & Advertising

Out Of Home Advertising (OOH) – All You Need To Know In 2021


It’s certainly an exciting time to be involved in the out of home advertising space. The demand for OOH campaigns has remained constant over previous years. Still, as we move into a new decade, the industry is understandably excited about what the future hold for real-world advertising.

A combination of tech advances with the traditional benefits of real-world ads has made the world of OOH unavoidable for many marketers and advertisers. We take a detailed look at the industry, where it’s been and what it might look like in the coming months and years.


What is out of home advertising?

Out of home advertising (OOH) is a form of advertising that can be found outside of a consumer’s home. Traditionally this includes everything from billboards to bus shelters, benches, and everything in-between. If you see an ad outside of your home (and it’s not on your mobile!), then you are most likely looking at some kind of OOH ad.

We all spend time outdoors, and with the growth of digital advertising, it can sometimes be hard to get your message heard. OOH solves this and is now combining with digital advancements to make it a powerful tool for advertisers and marketers.


Types of OOH advertising examples




Tube system, metro, and other travel locations



Street furniture


Benefits of out of home advertising

Out of home advertising can be a brilliant alternative to the world of online digital advertising. It can have limitations; for example, the rise of adblockers has meant that digital ads viewability isn’t always perfect. Combine this with the sheer amount of information that consumers are faced with online, and it’s easy to understand why digital advertising isn’t always the best solution.

With out of home advertising, this isn’t a problem. OOH ads are difficult to avoid, and they can have a significant impact on consumers due to their size and contrast to the real-world environment.

Alongside this, OOH has a positive effect as a complement to digital advertising. One study found that consumers are 48% more likely to interact with a digital ad after being exposed to an OOH ad first.


What you can do with out of home

Create impactful advertising campaigns

The reason that advertisers love OOH advertising is due to the potential impact that it can have on consumers. They take notice of these large OOH ads.

OOH campaigns can’t be ignored, compared to TV, radio, or mobile, which can often be turned off, or the consumer can move to another channel.

This means that advertisers can launch highly visual, impactful campaigns that attract the attention of consumers and allow brands to get their message to cut through.


Be creative

Out of home is a great place for creativity to thrive in the world of advertising. Large scale, impactful ad space is a fantastic place for creativity to thrive. Couple this with the need to provide a clear and lasting message, it’s perfect for testing some of your brand’s most creative ideas.

Combine this with the rise of data and new interactivity that is growing in the OOH space, and it’s easy to see why creativity is a key component of OOH campaigns.


Use location intelligently

Out of home advertising is extremely location-driven. Being in the real world, advertisers must consider where to place their ads to have the most significant impact.

With the rise of DOOH and other adtech stacks, advertisers can now do much more with location than previously available.

For example, it’s possible to understand, in real-time, the demographics of consumers that are nearby to OOH ad inventory. Based on this, advertisers can deliver dynamic ads that best suit the consumers at any given moment.

In the same way that digital marketing services have evolved to provide detailed insights and analytics into ad engagement and conversion, OOH advertising has now caught up. Campaign impressions can be measured, and attribution is now possible — all making OOH ads a powerful toolkit for any marketing department.


Purchasing in real-time

In previous years the purchase of OOH ad inventory was a laborious and time-consuming process. Today, digitization and innovation have meant that the time between purchase and viewability has been decreased to minutes.

This means that campaigns can be more adaptive and more likely to engage with consumer’s current surroundings and situations. For example, changing creatives based on the weather.

These advances have meant that the category is growing quickly amongst marketers and advertisers, with much of this growth being attributed to the digitization of the OOH space.


Trends in out of home advertising

The DOOH element of the industry is growing quickly. The industry is expecting to grow rapidly as the revolution that has swept across other areas of advertising and marketing to new heights reaches the OOH industry.

But what is everyone talking about in the industry? What are the trends that will dominate the following decade and beyond? We take a look at what we think will be the key trends as the industry grows and becomes more of a digital proposition.



While the majority of OOH inventory is physical, more digital screens are now a crucial part of OOH campaigns.

Digital screens are providing better optimization, and this means that advertisers can create more personalized messaging. ON top of this, it’s possible to use different kinds of triggers to trigger a more dynamic form of OOH advertising.

This innovation is no longer in its infancy, and advertisers have shown precisely how effective digital OOH can be. As well as this, they have demonstrated the scalability of DOOH.

Better and more accurate data is assisting in these innovations. Advertisers can now offer dynamic media based on the demographic and behavior of mobile devices in real-time.

Real-time advertising is critical, but in reality, it is part of a growing trend in which the industry is becoming a more reactive solution. The large amount of data that marketers now have at their disposal is fueling this. This versatility is driving OOH personalization and leading to fantastic results for advertisers that are using DOOH to achieve their goals.


Purchasing digital OOH media programmatically

A considerable advancement in the space is the Programmatic buying of OOH media.

This was previously a process between the advertiser, digital marketing agency, and the owner of the OOH.

The buying of OOH inventory has not just become automated and available to buy instantly. Still, it is now available in many of the same platforms and locations that marketers can purchase their mobile or display ads. Because of this, advertisers can now build campaigns seamlessly across several channels and mediums, including OOH.

This real-time purchasing also facilitates the use of first and third-party data sets. Encompassing this into campaigns can have the same effect that it has had in the world of digital marketing, namely, maximizing personalization and boosting ROI.


Attribution and measurement

One of the areas where the OOH industry struggled in comparison to digital media is in the area of measurement. Marketers can see detailed insights into the effect that their digital campaigns are having on awareness and conversions, all quantifiable and easily visualized. But now data has enabled this for the OOH industry.

As a result, brands can now see the number of impressions an OOH ad has generated. Analytics and insights have moved on from using surveys to gauge these metrics.

But this innovation with data doesn’t end with impressions. Smart data can help to close the out of home attribution loop. Movement data around store visits can bring digital attribution to the offline world and OOH ads.


Tips for OOH campaigns

As with any kind of marketing campaign, careful planning is crucial to make sure you get the most value from OOH advertising.

We live in a world where the smartphone can dominate attention. So breaking into that is a crucial goal of OOH campaigns. This is, as we have mentioned, becoming easier with the rise of DOOH and other interactive technologies.

Here are some essential tips to make sure that your OOH campaigns are a success, and you get the best return from your OOH budget.


Data is a key tool

One of the most significant transformations to hit the world of advertising and marketing has been the availability of data. This has led to better personalization, improved targeting, and more accurate measurement.

Make sure that where available, you are making use of data in your OOH campaigns. Understand what the options are to use your own datasets to improve your campaigns if buying OOH media programmatically look at how data can improve the results of your campaign.

For marketers, OOH is now an exciting place to be. Data is fuelling innovation and creating powerful DOOH campaigns.

We now have access to a large data ecosystem that wasn’t available ten years ago. These data sets have enabled advertisers to do more with their activities and campaigns.

However, the advertising is only as good as the data the fuels it. Marketers must be aware of the data they are using in DOOH. Third-party data needs to be highly vetted, and direct partnerships with 2nd party providers are a much better solution.


Try a shareable campaign

The best out of home advertising campaigns are designed to create a buzz. These campaigns work better when people are talking about and sharing them.

A great example is a recent campaign around the BBC TV series Dracula – the dynamic and provocative creative was widely shared on social media and became a viral sensation. All because it was the perfect blend of creativity and sharability.


Sometimes busier locations are better than more locations

It can be tempting to buy more sites or locations that are cheaper. But with OOH advertising, it can be much better to take a different approach.

To create the most impact, it can be better to choose a high traffic site that will reach as many people as multiple locations.


Less is more with OOH

With out of home, it’s important to make sure your message is simple. Less is more when it comes to OOH because most consumers will only look at your ad for a short period.

With a few seconds of attention, it’s important to keep the number of words to a minimum and to use visuals that are likely to grab attention. The aim is more to intrigue than to inform.


Next steps

Advertisers and marketers

See what you can do with OOH, get started with a campaign, or get the data you need to create a campaign that will smash your goals.

Read more 👉


OOH companies, agencies and inventory providers

Want to offer a smarter solution for brands and advertisers? Get in touch for compelling attribution, measurement, and other data tools that will transform your OOH offering.

Learn more 👉


What is out of home advertising?

Out of home advertising (OOH) is a form of advertising that can be found outside of a consumer's home. Traditionally this includes everything from billboards to bus shelters, benches, and everything in-between. If you see an ad outside of your home (and it's not on your mobile!), then you are most likely looking at some kind of OOH ad.

What are the main types of out of home advertising?

The main types of OOH ads are billboards, Street furniture, POS displays, bus shelters, kiosks, and telephone boxes. As well as this all advertising in transport locations.

Why is out of home advertising important?

OOH advertising is important because it gives brands a chance to communicate with a large number of consumers with powerful messages.


Talon boosts Ada’s capabilities through Tamoco partnership

Talon continues to invest in proprietary data management platform, Ada, boosting the platform’s capabilities through a partnership with Tamoco, a sensor-driven location data network. 

The partnership follows Ada’s continued success in helping advertisers to accurately target OOH campaigns on actual observed behaviours, delivering clear return on investment. Managing and activating billions of audience and location data points, Ada generates intelligence about people’s real and recent behaviours, including how they travel, the OOH inventory they engage with and the actions they take afterwards.

Tamoco’s first-party dataset will be available within Ada to support advertisers to accurately measure exposure around OOH inventory. The growth of the Ada platform demonstrates what can be achieved in the OOH space with more precise data and powerful data management tools.

Building on the platform’s existing capabilities, Tamoco are able to provide the same privacy compliant, high quality raw location data, as other trusted providers. The partnership will help to expand Ada’s breadth and depth of location data across the UK, providing advertisers with a deeper understanding of how people live, work and travel.

Barry Cupples, Group CEO at Talon, commented, “Our partnership with Tamoco reflects our continued investment in Ada. The demand for accurate, in depth understanding of audiences is growing and using Ada we are able to actively demonstrate not only how audiences behave but how best to target them efficiently, providing a clear impact on advertisers bottom line.”

Jonathan Conway, Chief Strategy Officer at Talon, added, “This partnership is strategically important for Ada. By working with large amounts of high quality, privacy compliant data, we are able to simplify the way advertisers access critical campaign planning and performance information. It enables us to continuously improve the platform and leverage deeper insights for more OOH campaigns.”

Daniel Angel, CEO at Tamoco continued, “Working with Talon allows advertisers to access detailed location data to attribute and validate their campaigns. The OOH space is evolving quickly and it’s important that data management platforms, such as Ada, continue to support datasets that focus on privacy compliance and accuracy so that advertisers can get a clearer picture of how their OOH advertising works.”


CCPA Compliance – How To Prepare Your Mobile App For New Privacy Laws

As of the 1st January 2020, the California consumer privacy act (CCPA) will introduce new rights for every citizen living in the state of California.

These changes will affect the way companies look at privacy. The legislation is currently only applicable to consumers in the state of California. However, as we will discuss, the act will likely have an impact across the US.

Along with the GDPR, which offers consumers similar data and privacy rights in the EU, the CCPA is something that all businesses need to consider. This starts with a brand’s databases, CMP, and website, but it also includes any mobile app.

Apps will be subject to the same scrutiny, and under the regulation, developers will need to find a solution to comply with the legislation fully.


What is the CCPA

First, let’s look at the technical side of the new legislation.

The act allows any consumer-based in California access to all information or data that a company has related to them.

The act also states that this information should include a full list of the third-parties that the data is shared with.

It also allows consumers to request that companies delete this data or stop them from sharing it with one or all of the relevant third-parties.

As well as this, the CCPA also means that companies will have to do more to explain to consumers what types of data they are collecting, why they are doing it, and how consumers can opt-out.


What does CCPA cover?

The act seems to take a broader approach than GDPR in terms of what constitutes personal information:

  • Any personal identifier such as name, alias, address, unique or online personal identifier, IP address, email, account name, social security number, passport, or driving license number.
  • Commercial data that includes records of property, product or services, or other historical purchase data.
  • Geolocation data
  • Biometric data
  • Professional information or employee data, such as employee time tracking, or employee engagement. You can even use a timeclock calculator to gather this data. 
  • Internet or other electronic network activity information including, but not limited to, browsing history, search history and information regarding a consumer’s interaction with a website, application or advertisement


What happens if my mobile app is not compliant?

According to the CCPA, companies will have 30 days to comply with the when regulators notify them of a violation. After this, is there is no resolution, the regulator will issue a fine of up to $7,500 for each record.

Despite this initial fine, companies are under threat from another area that is covered in the act. The bill allows an individual to sue a company. This occurs if a consumer gives written notice to a company that they have had their privacy rights violated. If the company cannot find a resolution, then the consumer can bring a class-action suit against the company.


How to become compliant

For mobile apps, it can be more challenging to become compliant with privacy laws. Many tools for manage consumer privacy preferences are web first, and there aren’t a lot of tools that exist for developers to manage consent and comply with the regulation.

Under CCPA, apps will need to understand the data that they have on all of their users. This needs to be attached to a single consumer to provide information about the data that the company has on an individual. This means a centralized location is needed that can access this information.

As well as this, how the data is used will need to be communicated to the user, including third-party uses.

Lastly, consumers need to be able to access this, manage their choices, and request that this information be deleted.

So, many dedicated nodeJS developers need an interface that clearly explains which data is being collected and why. It will also need to allow users to opt-out and define which third-parties can access this data.

Sound complicated? Well, luckily, there is a solution.


Tamoco’s mobile-first CMP

A CMP is a powerful tool that should be implemented anywhere where consumer data is being processed or stored. For these reasons, it makes sense to have a CMP that can cope with large amounts of consumer preferences and can manage these in several different locations and platforms.

The Tamoco CMP collects user preferences in applications. It allows consumers to collect and manage use preference for data collection and data use.

Our CMP is the world’s first mobile CMP that allows developers to comply with data privacy legislation such as the GDPR and the CCPA.

With a straightforward integration app developers can take control of their app and deliver privacy management at scale for all of their users.


What is the CCPA?

The act allows any consumer-based in California access to all information or data that a company has related to them. The act also states that this information should include a full list of the third-parties that the data is shared with. It also allows consumers to request that companies delete this data or stop them from sharing it with one or all of the relevant third-parties. As well as this, the CCPA also means that companies will have to do more to explain to consumers what types of data they are collecting, why they are doing it, and how consumers can opt-out.

Apps Marketing & Advertising

What Is A Consent Management Platform? All You Need To Know 2020


Since the introduction of more detailed privacy regulations, such as the GDPR and the CCPA, businesses have started to take consumer consent and data privacy seriously.

Consumer data comes in multiple forms, and it’s used for many different purposes, from advertising personalization to monetization.

Because of this, collecting and managing consumer preferences on how all of their data is used across these different use cases is not exactly the simplest of tasks.

Privacy laws have meant that businesses need a robust solution that provides consumers with this choice. Enter the consent management platform (CPM) – a toolkit that is designed to do just this.


What is a CMP

For consumer-facing publishers, there is a huge issue here. These businesses work with multiple partners across the advertising ecosystem. Each partner has numerous uses for consumer data, from advertising to personalization. Asking and managing this consent across an entire user base is a daunting task.

This is where a consent management platform comes in. By collecting user preferences for different data types and different uses and various partners, CMPs provide this functionality.

Think of a CMP of something that sits between the publisher and the user. It informs users about the type of data that the publisher will collect, whether through forms, or another method, and what this data will be used for. It allows consumers to modify these settings, stores this, and gives consumers a chance to opt-out and change these settings.

What this looks like for the consumer is usually a simple dialogue. This dialogue allows them to choose how their data is used. These preferences are stored and ultimately control of how user data moves between the publisher and the broader advertising ecosystem.

As a lot of new privacy regulations require businesses to offer this level of functionality to consumers, consent management software is a vital tool for any modern company.


Why do you need a CPM?

To give users the option to take control of their data.

CPMs provide the consumer with the opportunity to control their data and how it is used. They allow consumers to understand who is using their data and for what for.

CPMs give consumers the ability to revoke this access and update these preferences at any time. The tools then automatically communicate these consumer requests throughout the data supply chain.

This proves detailed control of personal data at an end-user level. This level of functionality puts the user in control and increases trust between a publisher, app, or other consumer-facing platform and the users that ultimately bring them revenue.


To comply with privacy regulation

The main reason that you need a CMP is to comply with relevant privacy laws and regulations. These tools are useful because they can be universally integrated across every consumer-facing platform, allowing companies to comply instantly.

Regardless of whether you’re an EU based business or not, correctly managing user preferences should be a priority. For website owners and publishers, offering users the choice and allowing them to achieve these at any point is fundamental to how regulators see the data-driven world.


To deliver better experiences, improve personalization or monetize user data

First-party data uses still require the same level of opt-in as data that is sent on to third-party solutions.

That means if you are using customer or user data for analytics or insights, you’ll need to implement the choice controls that come with a CPM.

This also applies for personalization, whether first-party page personalization or passing data onto third-parties to deliver personalized ads on your inventory.

As well as this, CPM functionality is required for data monetization or other activities where a user’s personal data is used for monetization purposes.


Are all consent management platforms compliant with GDPR and CCPA?

Well, no. You’ll have to check with the current privacy laws to be 100% sure. An excellent way to understand which CPMs are is to check to see if they use the IAB framework.


IAB transparency and consent framework

The IAB GDPR transparency and consent framework was built to understand what is needed from a CPM from a technical standpoint to comply with the GDPR. If that sounds like a mouthful of acronyms, then don’t worry, it can be a little confusing.

What this does in practice is sets several hoops for CMPs to jump through for their consent management platforms to be GDPR compliant. So, look out for this term when choosing a CPM as it means they have taken the time to verify that they are following best practices according to the leading industry body.

At the time of writing, there is currently no equivalent for the CCPA.


The Tamoco consent platform + SDK

A CMP is a powerful tool that should be implemented anywhere where consumer data is being processed or stored. For these reasons, it makes sense to have a CMP that can cope with large amounts of consumer preferences and can manage these in several different locations and platforms.

The Tamoco CMP collects user preferences in applications. It allows consumers to collect and manage use preference for data collection and data use.

Our CMP is the world’s first mobile-first CMP that allows developers to comply with data privacy legislation such as the GDPR and the CCPA.

With a straightforward integration app developers can take control of their app and deliver privacy management at scale for all of their users.


How Big Data Is Changing The Event Planning & The Events Industry

Big data plays a crucial role in many industries, and event management is just one of them. People all across the world are relying more and more on their smart devices, and it has become increasingly easier for event organizers to gather and use data to cater more directly to event attendee’s experiences.

Event planners are leveraging big data to deliver highly personalized events and boost attendee engagement. In this article, we’ll look at some of the ways you can use big data to provide better event experiences and learn more about your audiences’ needs and expectations.

Let’s put everything into context before we begin.


What Is Big Data?

Generally speaking, big data refers to the enormous volume of data that is collected daily. The data itself doesn’t necessarily have to be useful on its own. It’s the insights, trends, and patterns the data reveals that event planners are most interested in.

For event organizers, big data analysis involves collecting vast amounts of data from their attendees, sponsors, and target audience. This might be in the form of emails, surveys, tweets, photos, or location data.

Event planners can use big data to organize attendee-centric events, increase attendee engagement in real-time, and deliver enhanced event experiences.

The good news is that implementing data analytics in your event planning doesn’t have to be complicated. You can work with the data sets that are already available to you and extract valuable insights from them.

If you’re not collecting any data from your target audience or attendees, the first step is to identify the key data points you’re most interested in. For example, if you’re hosting a business conference, you will need to know where attendees are traveling from so you can provide each attendee the best advice for lodging and commuting to the event venue.

Now that you know what big data is and why it’s important for event organizers, let’s look at how you can implement big data in your event management business.


4 Ways Big Data Is Driving the Events Industry

Below we discuss some ways big data can help event organizers plan for successful and engaging events.

#1: Improving Targeted Promotions

Promoting your event takes up a lot of monetary resources, which is why you need to make sure you can cost-effectively maximize your audience outreach. By gathering and analyzing data on your attendees, you’ll be able to find ways to connect with them.

For example, attendee surveys might indicate that most of the respondents first learnt about your event from your social media pages. So, instead of focusing on company spending on offline marketing strategies, you might consider spending more on your social media campaigns.

Location data {information about geographic positions of devices such as smartphones or tablets} allows event marketers to connect their digital marketing efforts to how prospective attendees behave in the real-world. As a result, event marketers can provide even more personal advertising to their target audience.

To attract more attendees, you need to ensure that you’re advertising to the right audience; otherwise, you might experience a drop in event attendance and audience engagement. Analytics can help you discover when your target audience is actively using social media, what is the best way to connect with them, and how you should craft your message to get their attention.

Big data can help you identify what your prospective attendees respond to, which you can then use to improve your event planning. Having access to this information will not only help you customize your ads to attract more attendees but will also be useful in delivering personalized event experiences to your guests.


#2: Gaining Insight from Analytics

Analytics can help you figure out the specific topics and themes that most interest your audience, the guest speakers they want to listen to, and any tools or presentation technology they’d like you to implement in your events.

By gaining access to the right information, you’ll be able to discover their pain points and what your guests expect from your events. Start by collecting online search data or using pre-event survey forms to gather this information directly from your attendees. Similarly, you can search through social media sites, community sites, as well as this you can make use of powerful location intelligence and analytics to reveal your attendees’ motivations.

Big data can help you identify the different factors that affect audience behavior and leverage them to your advantage. You’ll be able to predict trends months (or even years!) into the future. Predictive analytics lets you make near-accurate guesses and allow you to see which existing topics have growth potential so you can jump on them before your competition.

Google Trends is also an excellent tool for discovering what your prospective attendees search for. By comparing search terms with historical data, you can predict what will be popular in the future, and when you should host an event around it.


#3: Personalizing Attendee Experiences

The data you collect from your attendees gives you insights into how you can enhance attendee satisfaction. You can use technologies such as location data, RFID, VR, and beacons to deliver unique event experiences.

For example, you can send information to nearby devices using beacons, allowing your attendees to only focus on the booths they’re most likely to be interested in. This is also a great way to offer location-based experiences to your guests.

Pepsi organized a dance party at SXSW, where dancers wore wristbands that would gauge their reaction to a stimulus. The wristbands measured body temperature, the volume of music, body movement, as well as the physiological arousal through body sweat. This information was used by the event’s DJ to figure out what music people loved the most. In addition to this, it enabled the crowd to control lighting, bubble machines, and smoke machines.


#4: Crowdshaping

Another way big data can help you improve your event planning efforts is by letting you better manage crowd densities at the venue, also known as crowd shaping. It’s an effective way to make sure attendees can enjoy your events without openly influencing their behaviors.

For example, if you see a crowd of people around the book signing tables, you may decide to extend the time allocated for that particular activity in real-time. If a guest speaker is about to take the stage, you can send out notifications to let attendees know that they can get their books signed after the lunch break.

In addition to this, you can use data from past events to improve upcoming events. For example, if data from one event indicates that attendees like open spaces to walk around during breaks, you can use this information to book future venues with plenty of extra space.

In its simplest form, geolocation is capable of giving you information about the location of a person. Beyond that, it can be used by event organizers to gather crowd density data about events. This allows them to manipulate crowd flow more effectively.

Crowdshaping and big data come together to help you identify problems in your crowd flow and enable you to solve them by making proper adjustments quickly. In the long run, this information will allow you to make better event planning decisions (such as opening more check-in lines) and go for venues that meet your audiences’ specific needs.

Event organizer at C2 Montreal gave RFID badges to their attendees that collected their location data. This allowed organizers to see where the majority of the attendees gathered and which event places received less traffic. They found more crowds near food tables, which led them to send more food service staff to those places.



The data you gather on your attendees is a valuable resource that can help you improve your event experiences. You can always start small and use whatever data you have to learn about your audiences’ needs and expectations. With time, you’ll feel more comfortable collecting and working with big data.

Event Espresso lets you collect, control, and own all the data you collect for free. You can export your event attendees’ data into Excel or CSV format and use it however you want.

Marketing & Advertising

Advertising Cookies & Retargeting – What’s Changed + Solutions

It seems that you can’t go anywhere in the world of online advertising at the moment without the conversation moving onto the role of advertising cookies, and what the future holds.

With the implementation of GDPR last year, the California Consumer Privacy Act coming into play in 2020, the cookie has come under increasing pressure.

Combine this with Apple’s Intelligent Tracking Prevention and the whispers that Google is also looking to block third-party cookies, and you can understand why everyone in the space is a little worried about what the future holds for the cookie.

We’re going to look at this future, how the cookie works, and how marketers and advertisers can adjust for any upcoming changes in how the advertising industry uses web cookies.


What are advertising cookies? How do cookies work for advertising?

First of all, what role do cookies play in the world of advertising?

Well, cookies are small code snippets that store information related to how the user behaves on the web. This cookie is stored on the user’s web browser and can be used accessed to store and change data related to the user.

Cookies can store a wide range of information, such as the pages you have visited and for how long.

We can divide these further types:

First-party cookies

First-party cookies are created by the publisher or website owner when a visitor is on their site. This is usually used to understand which user is returning and ensure that the page content is right for that user. Often, this is something like language or another element that helps with the user experience.

These cookies also include analytics, such as Google Analytics, which used cookies to measure how users use the site.

Third-party cookies

Third-party cookies are used in the digital advertising ecosystem for retargeting and for behavioral-based targeting. Adding these types of cookies to pages allows advertisers to understand how users behave across the web. Using these, they can build a profile that can be selected to target with ads that are more personalized to each user.


What are cookies used for?

Advertising cookies can be used for analytics and for managing the user experience. But we are interested in the role that they have in the advertising ecosystem.

Here cookies are used mainly to retarget users based on which site and which pages they have visited. What started as a simple way to deliver products to users who had already seen them has now developed into sophisticated methods to target users that have previously visited a specific page or product.

The other side of the advertising cookie is to build audiences based on profiles. As a user visits a site, this information is used to build a profile for that user. This profile contains information such as age, gender, and interests. These profiles allow marketers to build and create new audiences that are relevant for their product or proposition.


What’s changing

The most significant change to the advertising industry in the last few years has been the drive for transparency and user privacy.

Privacy regulators have introduced legislation that limits how advertising cookies can be used to collect user data. These have created a massive issue for the advertising ecosystem, which relies heavily on third-party cookies to build profiles and target audiences based on behavior.

This is because programmatic advertising relies on these third-party cookies as the basis for user-level targeting and attribution. Without this process, marketers can’t target users with more personalized ads and understand when these ads lead to conversions.

As well as this, the people who bring the internet to users have also started to take a tough stance on the issue. At the time of writing, Apple has already announced an anti-tracking update to its native browsers, which blocks the use of third-party cookies. Firefox has implemented a similar policy, and there are reports that Google, who’s browser user base makes up over 60% of web usage, is looking at a similar process.

Another implication for advertisers is that the world has become more mobile-first since the invention of the web cookie. Users are using apps and mobile solutions much more instead of sitting behind a computer.

With all this, it makes a little more sense that advertisers are worried about how these changes will impact their business. But it’s not all doom and gloom – we have some examples of how advertisers can still deliver personalized ads and retargeting campaigns that work.


Potential solutions

Focus on people and context

Instead of looking at the type of consumer and using this to build audiences, advertisers can focus on context.

Rather than focus on the user, placing greater emphasis on where the user is can be an effective way to target audiences. For example, using keywords to gauge purchase intent. Or using a user’s real-world location or environmental factors to understand factors beyond the user that make them ideal for targeted advertising.


Focus on first-party data and reliable first-party providers

First-party data will become an even more valuable currency for targeting users. Solutions that can combine first-party insights and compliance with privacy regulations will be invaluable for advertisers.

These datasets can help to target consumers reliably and with consent from the end-user. For example, anonymized first-party mobile location can be used to retarget users that have visited a physical store.


Wait for a persistent identifier

A persistent identifier is a solution that is commonly suggested as the ecosystem moves away from the cookie. Using this form of identifier, that sits with the consumer and requires explicit compliance with privacy regulations could be a solution.

The problem here is getting this to exist in one form, that’s standardized and that everyone can agree upon. Some areas of the advertising supply chain have introduced this already – but these don’t follow the same standards, making it difficult for advertisers.


Look at other channels

Advertisers will begin to look at channels that don’t require third-party advertising cookies. These will include traditional channels such as email, TV, and app-based ads.

These systems allow advertisers to use a persistent identifier for personalized advertising and marketing.



Marketers and advertisers will need to think about how they can focus on people based personalization in a world where the advertising cookie no longer exists. First-party data or reliable first-party data providers will become a vital source of behavioral data. Using alternative behavioral information, such as location, is a great way to deliver retargeting and personalization at scale.

Mobile ad IDs are currently universal and tracking identity across the moble infrastructure is much simple than the web. The role of advertising cookies is changing and quickly. People-based advertising and first-party data could well be the solution that the industry is looking for.

Marketing & Advertising

What Is Market Segmentation? Everything Marketers Need To Know

Whether it’s running effective ad campaigns or crafting the perfect message, market segmentation should be a fundamental part of every strategy.

You have spent the time building a B2C or B2B marketing strategy, and now it’s time to make sure that it resonates with the right audiences.

As audiences get bigger, it can be easy to overlook the value of clear and concise messaging. The bigger an audience, the more diverse the needs of that audience are, any digital marketing course will tell you that. That’s where market segmentation comes in, allowing you to focus your B2B marketing efforts on specific segments.

Segmentation provides an edge over the competition as you can prove that you understand who your audience is and what they need most.


What is market segmentation?

Market segmentation is the process of dividing audiences into smaller groups that share the same characteristics to optimize marketing and advertising and sales results.

This practice is a standard business practice that involves identifying key traits that group audiences and using these to build more specific audiences. Market segmentation also allows brands to create more personalized ads, marketing, and sale journeys.

At the heart of this is the idea that consumers are likely to respond to more personal and customized messaging and campaigns. As a marketer, it’s hard to appeal to your entire audience with a single message. Some might respond differently to a specific message. That’s why market segmentation is essential – it the method of segmenting audiences to provide a more tailored solution.


Types of market segmentation

There are many types of market segmentation. Generally, these methods can be defined into four main categories.


Geographic market segmentation is the process of targeting consumers based on a defined geographical boundary. Consumer interests can vary dramatically between different regions, and often preferences can be similar across smaller geographic areas.

Sometimes this process of market segmentation can be a simple as weather associated with a geographical location. It wouldn’t be very productive to sell warm clothes to a region where temperatures are low year-round.

Broader regional segmentation includes looking at countries, cities, and postcodes to group audiences. Specific postcodes can determine household income and even interests.

Geography goes hand in hand with both language and culture. Understanding and segmenting your audiences based on location can help to convey the right message (think soccer in the US and football in the UK).

There are many ways of understanding location, from surveys and addresses through to more up to date and accurate solutions such as location data providers.



Demographic segmentation is a powerful way of creating specific audience segments that share similar preferences and requirements.

This form of segmentation is a standard method as many targeting and adtech solution provides some way of doing this when building audiences. It’s essential to do the same to your audiences or customer base to segment effectively.

Traditional demographic breakdowns include age, gender, marital status, occupation, education, income, race, nationality, and religion.

Demographic market segmentation is vital to finding product-market fit, but it’s also a powerful way to determine which channel is the best fit. Optimizing channel delivery relies heavily on reaching the right person in the right medium. Demographics are a great way to do this.

Media consumption changes significantly between demographics, so it’s essential to use this form of segmentation effectively to engage your audience the right way.


Behavioral segmentation

Segmenting audiences based on behavior is a popular solution in today’s digital world. Improvements in technology have enabled more touchpoints with greater detail into how audiences behave.

Behavioral analysis provides more options for market segmentation based on how audiences interact with your business. There are many behavioral-based traits to segment audiences, including:

  • Web activity – how consumers behavior on your website is valuable behavioral data. Segmenting audiences based on time spent, which pages they visit, and other measurable traffic insights is a powerful way to divide larger audiences into potentially effective smaller segments.
  • Usage with your current product or services. These includes app usage, platform usage, or perhaps just the fact that the person has purchased the last three versions of your physical product. These factors are a great indication of loyalty and where consumers sit in the funnel.
  • Offline behavior – movement data can help you to understand behavior in the real world. This an effective way of building segments that are grouped based on real-world movement. Segments such as gym-goers or coffee drinkers are powerful as they combine behavioral and geographical elements.



This method of market segmentation is focused more on the intrinsic traits of audiences. It’s similar in a way to demographic segmentation, but it’s more concerned with the emotional and other underlying factors that audiences believe in.

These kinds of insights can be valuable in understanding the motivations, needs, and exact preferences of audiences to create highly personalized segments.

Psychographic traits include personality, values, motivations, opinions, and lifestyle choices.

There are many ways of collecting this kind of information, including surveys or in some sort of feedback process. But offline and online behavior can also be a good indicator of psychographic traits.


Why market segmentation

So now you are aware of the four main methods of market segmentation. It’s time to understand why it’s worth doing. Many marketers identify improved audience segmentation as the most critical priority. Market segmentation offers many benefits to publishers, marketers, apps, and other businesses.

Improves the effectiveness of advertising campaigns

Market segmentation can help to improve marketing activity and advertising campaigns by reaching the right person with the right message at the right time. Segmenting your audiences allows you to provide more personal and engaging advertising, rather than a one size fits all approach.

Targeting a specific audience allows you to tailor the message and timing so that the audience is more likely to respond and engage with your campaigns. Targeting your entire audience with a single campaign is hugely wasteful. A considerable amount of the audience will not be relevant to the campaign, and thus you will be wasting revenue.

Essentially segmentation means that you can remove the irrelevant consumers from this audience, ensuring that you optimize ad budgets, increase ROI and improve the effectiveness of advertising campaigns.

Even if your product or service sits across a broad audience of potential customers, it’s still essential to segment audiences. Some messages will sit better with different segments and work better across different channels, even if the product is the same.


Informs new products and product development

Segmentation is also a useful tool to drive product innovation and can help to hone product strategy. These insights means that product teams can create products that better fit the needs of their customers.

Market segmentation can also identify a need for more specific products that sit in smaller groups within your customer base. Segmenting products is powerful because it helps to sell more and will make customers happier.


Helps to identify new audiences and segments

This process is useful when looking at maximizing the effect of your marketing strategy, but it can also be valuable when looking to grow and scale.

Segmenting your current audience can identify new traits that you didn’t know existed. Seeing this can open up new opportunities related to the newly identified segment. These opportunities can lead to new, engaging marketing campaigns or can even lead to the inception of new products and services that are in need but not currently offered.


Improves business functions and can help to make big decisions

Market segmentation can help a business to understand precisely what their customers want and focus their efforts on these to create a highly specific and valuable product or service. This specification could significantly improve brand perception, lead to more sales, and increase repeat business and engagement.

These insights can lead to better decisions across the business. From product-market fit to product delivery and communication, market segmentation can help to identify the best ways to go about these important issues.

Audience segmentation can also help with decisions such as pricing and can even help inform dynamic pricing strategies.


Common mistakes

There are some common pitfalls that you should look out for when creating your market segmentation strategy.

Too small segments

The most common problem is that businesses go a little over the top with segmentation. Set too many parameters with too much detail, and then end up with a tiny audience.

This mistake means that you will eliminate audiences that don’t fit into your filter but still carry purchase potential. As well as this, you will lose the opportunity to gain quantifiable metrics and insights into specific audience segments.

Make sure you think carefully about how to segment your audience. One or two filters work best, depending on the size of your total audience and total addressable market, of course. The sweet point between segmentation and scale is extremely valuable if you can find it, so be sure to experiment extensively!


Not up to date

Predominantly in the world of traits of digital marketing and advertising, a common mistake is that marketers identify key audience segments that work for their business. But they fail to update these as the needs and products change over time.

Always stay on top of your strategy and constant update and tinker with new methods of segmentation.


Targeting segments that don’t convert

Another common mistake is to identify segments that are both large enough and up to date. But these segments still need to carry conversion or purchase potential.

If the segmentation method creates a group that is not a good product fit and does not have the necessary buying power, then the ROI will not improve just because the audience is segmented.



What is market segmentation?

Market segmentation is the process of dividing their audiences into smaller groups that share the same characteristics to optimize marketing and advertising and sales results.

Why is marketing segmentation important?

Marketing segmentation allows companies to reach consumers with precise needs in a personlized and relevant way.

What is the goal of market segmentation?

Market segmentation can improve the effectiveness of marketing efforts, drive conversions and increase ROI.

Marketing & Advertising

What Is Behavioral Targeting? – All You Need To Know in 2021

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.



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.



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 London SEO consultant, Bristol SEO agency, or even a industry specific one like B2B SaaS SEO consultant)
  • 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



Best Guide To Location Data 2021 – All You Need To Know

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


The global adoption of smartphones has grown at incredible speed in the last decade.

Mobile devices are a powerful tool for understanding the aggregated behavior of consumers.

Understanding device location opens doors to a wide range of use cases that are unique in many different ways.

Mobile location data provides a granular solution for consumer understanding. Combining this understanding with other datasets are helping to solve business problems and achieve goals across many different industries.

For these reasons, location data has quickly become the holy grail of mobile. It’s applications are broad and run across a number of different industries and verticals.

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

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 to a time identifier.

This device data is assumed to correlate to a person – a device identifier then acts as a pseudonym to separate the person’s identify from the insights generated from the data.

Location data is often aggregated to provide significant scale insights into audience movement.


How is location data generated?

Companies are collecting location data in many different ways. There are several different techniques to collect location data. These techniques differ in reliability (but more on that later).

For now, the primary process of collecting location data requires the following ingredients.

A location source/signal

The first ingredient is a location signal. This signal is not a product of the device itself – it comes from another piece of technology that produces signals. The device listens to these external signals and uses it for positioning. These signals are as follows:



GPS is shorthand for the global positioning system and was first developed in the 1970s. The system is made up of over 30 satellites which are in orbit around the earth. This technology works in your device by receiving signals from the satellites.

It can calculate where it is by measuring the time it takes for the signal to arrive.

GPS location data can be very accurate and precise under certain conditions, mostly in outdoor locations. In the best instances, the signal can be reliable down to within a 4.9 metre radius under open sky (source) .



Wi-fi networks are another source of location signals that are great at providing accuracy and precision indoors. Devices can use this infrastructure for more accurate placement when GPS and cell towers aren’t available, or when these signals are obstructed.



Beacons are small devices that are usually found in a single, static location. Beacons transmit low energy signals which smartphones can pick up.

Similarly to Wifi, the device uses the strength of the signal to understand how far away from the beacon it is.

These devices are incredibly accurate and can be used to place a location within half a meter with optimal signal strength.


Carrier data/cell towers

Mobile devices are usually connected to cell towers so that they can send and receive phone calls and messages. A device can often identify multiple cell towers and by triangulation, based on signal strength, can be used to place a device location.


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”



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 is a measurement that helps us to understand how close the device’s geographical reading is to the actual location of the device.

So how do we measure accuracy? The location accuracy of the device changes depending on the type of signal and the device. Accuracy is measured by looking at the signal type (GPS, wifi, cell tower). The device provides us with a reading of the location and then an accuracy rating. This unit is usually a measure of distance and is the margin of error associated with the measurement.



Precision is the level of detail associated with the location measurement. The more this is is similar to the other measurements in the dataset, the more precise the data is.

In location terms, we use lat/long to measure this. Firstly we check to see if the data points are realistically within the same area.

The number of decimal points in the lat/long is essential in measuring the precision of location data. The more digits there are after the point, the more precise the data is.

The following table helps to explain precision when looking at lat/long:

Decimal Places Decimal Degrees DMS Qualitative Scale
0 1.0 1° 00′ 0″ Country or large region
1 0.1 0° 06′ 0″ Large city or district
2 0.01 0° 00′ 36″ Town or village
3 0.001 0° 00′ 3.6″ Neighborhood, street
4 0.0001 0° 00′ 0.36″ Individual street, land parcel
5 0.00001 0° 00′ 0.036″ Individual trees, door entrance
6 0.000001 0° 00′ 0.0036″ Individual humans


Not all mobile location data is equal

As many in the industry have stated: the type of location data and methodology is of significant importance. The relevancy of different kinds in different scenarios is often contented.

Mobile location data requires some fundamentals to provide granular insights that we discussed earlier.

So what’s the best way to accurately and precisely collect location data and what happens when signals such as GPS aren’t working?

We think this is another argument for SDK generated data. For example, the Tamoco location SDK can listen for multiple signal types simultaneously. Processing these signals allow the SDK to measure accuracy and then determine which signal to use.

Our SDK, therefore, uses Bluetooth and Wifi to help position the device in areas where GPS signals are weak. This sensor agnostic approach means that the SDK can place the device with better accuracy and more precision by using multiple signals.

Remember, when we talked about the three main ingredients that combine to produce location data. We’ve covered the device and its identifier. We’ve also covered the signals that the device used to position itself.

However, we are yet to cover the additional data that is needed to make use of the dataset. As we have discussed location data is usually a lat/long associated with a device and a timestamp.

We need to understand what this location is to make any use of the data. Knowing a device location is half of the challenge. To do this, we use database that allow us to connect this online data to the offline world. We call this a POI dataset.


What is POI

A point of interest (POI) dataset is a data representation of the physical world. A single POI is a geographic boundary and is usually associated with a physical location (think a store or building).

As with location data POI datasets come with a series of challenges including accuracy. Business regularly move, and as changes happen in the real world, the datasets evolve accordingly.

At Tamoco, we set up our own Place database to explore-in-depth how devices move and behave in the offline world. This database is slightly different from a POI dataset.


Explainer: Tamoco places

  • Contains metadata associated with the place – opening hours, floor level, polygon footprint and other essential information that can help to verify if a device entered the POI.
  • Combines with an associated geographical boundary (geofence) that can be used to understand the device activity inside and how long it stays inside.
  • Combines with any known sensors (beacons, Wifi, or other signal based tech) to help understand when a device is visiting the POI and not in fact staying in a place nearby.


What’s the importance of POI?

Perhaps the best way to understand the importance of a useful POI dataset is by using a real-world example.



In the above example, we don’t have a POI dataset. We have multiple lat/long, which might be accurate and precise, but we get no value from this as we have no connection to the real-world.



Here we have a POI dataset which connects the lat/long to a physical location. However, the POI is slightly in the wrong place, which means we think the device has visited the coffee shop, but they are waiting outside, or elsewhere. The implications of this will become more evident in the next section.


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 location-based targeting involves identifying when a device is in the desired location and usually involves a mobile targeting. The process is simple – when the user is in the desired location, deliver an advert instantly on that users device through programmatic advertising.


Historical location targeting

This form of targeting is usually called retargeting, and it is similar to real-time that we discussed above. The difference is that over time, the devices that appear in a predefined location are used to build an audience. The advertiser will then retarget this audience at a later date.


Visits vs interests


Targeting based on visits is a clear way of building an audience that has visited real-world locations such as a specific coffee shop.

Depending on the value of the POI database this can be extended to include devices that have visited all of the stores across a brand (for example every Starbucks) or every visit to a type of venue (example – visits to coffeeshops in Austin).



Using location to target people based on interests is another way of reaching a highly specific audience. This method is similar to visits but usually consists of several repeat visits to a location or combined visits that fit particular criteria.

For example, an interest-based target audience, such as big coffee drinkers could contain devices that have visited any coffee shop at least three times in a weekly period.

Another example could be active consumers – these could visit both a gym and a health shop within a month.

Interest-based location targeting is interesting because you can create very specific segments. However, as with other aspects of location-based targeting, the more specific you get, the less scale you can achieve with your campaigns.


Channels for location-based targeting + examples

By combining these, you can create highly targeted audiences using location data. But how do you then reach them?



Using device identifiers marketers can feed relevant devices into their programmatic stack to automatically buy ad impressions and target the desired devices in near real-time.

The same data can be used to retarget at a later date in a social feed or via another programmatic channel.

The benefits of this strategy are that you can automate a lot of the marketing process. By using location-based audiences, you can ensure that you are reaching the right audience with the right message.

Tamoco offers these as pre-built segments (both visit and interest based) that can be activated directly in your DSP for targeting, or in your Data Management Platform (DMP) for combining with other data sources.. This process can be used to reach consumers across several programmatic channels and on different devices.


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.



Digital campaign attribution

An agency is running a campaign for a clothing brand. The campaign is delivered to audiences programmatically. The campaign aims to drive footfall to stores stocking a new range.

The impressions and clicks can be measured by the agency, but the brands want to know if the campaign is driving customers to their stores.

Using location data and matching against the IDFA/AAID’s targeted during the campaign, an exposed audience is created. A control audience is also built to compare the exposed group against users who weren’t targeted during the campaign. By having an exposed and control group who were equally likely to visit the clothing brands stores before the campaign, it is possible to isolate the impact the advertising had on store visits by seeing how store visits between the groups move during, and for a period after, the advertising period.



A brand runs an OOH campaign across multiple OOH sites and wants to understand which of these was the most effective in driving online purchases, or whether the OOH advertising was driving online purchases in the first place.

Through an accurate understanding of where the OOH sites are located, and by accurately and precisely understanding how a device moves in relation to the site (an accurate view of this needs to factor in how much time a device spends close the site, how fast they move past the site and a number of other factors the Tamoco SDK factors in) it is possible to build a group of devices that were likely to have been exposed to the OOH advertising.

These devices can be compared to similar devices that weren’t exposed to the advertising, and their device identifiers can be matched to customers in the companies CRM or DMP to measure the impact the OOH advertising had on store purchases as well as which of the OOH locations was the most effective in driving purchases.


Analytics and insights

Location data is a useful tool to analyze how large numbers of people move and behave to identify large scale trends and patterns.

These kind of insights are usually difficult to attain at scale in the offline world. Location data works as an indicator of where people go and how they behave – and how these change over time.

In the realm of advertising and marketing, location-based 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.



Location data can be useful for both smaller and large retailers. Understanding store visits, as well as customer behavior through mobile device data, is having many positive effects on the retail sector. These insights can help inform business decisions such as store layout, opening times, staffing, and more.



Location data is an essential tool for finance 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.



The rise in mobile location data has provided better opportunities to understand how cities work. It’s helping to create systems and infrastructure that reflects this.

Combined with the increasing number of connected devices in cities, central planning authorities now have a set of tools that can inform decision making in many different areas.

Mobile location data is contributing to a better understanding of where demand for public infrastructure is most significant. For example, we could examine mobile device location data to understand the most cycled roads within a city. This information is precise and invaluable when planning where to implement new cycling routes.

The same is true of traffic and congestion. In increasingly crowded and polluted megacities, it’s crucial to understand how traffic issues can be alleviated. Understanding traffic flow and where to build new road structures or introduce new low emission zones is vital to making the kind of smart city that can sustain current levels of population growth.

Location data can have a substantial positive effect on this kind of planning. Thanks to the accuracy and uniqueness of mobile device data and location intelligence, it is changing how decisions are made in cities and towns around the world.



Transparency – why do we need it

As the amount of location data available to 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.

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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.



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.


Questions to ask a location data company


What is the source of this data?

How much of your POI data is 1st party vs. 3rd party?

How do you organize the geographical area around a POI or place?

Can you share how precise your POI/place data is?

How many POI locations do you have?

What metadata is associated with these places?

How do you verify your place database?



How do you collect location data? Is this process first-party, or is the data 3rd party?

What type of device data do you use (GPS, wifi, beacon, etc.)?

Is your data sourced from an SDK?

Do you have a method in place to filter out data that isn’t relevant for my campaign or merely inaccurate?

what is the scale of your dataset?


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.


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.