Marketing & Advertising

What Is Ad Fraud? How Location Data Can Detect Ad Fraud

Online programmatic advertising is a huge, multi-billion dollar per year industry. Ad spend in this area is expected to reach over $300 billion next year. As well as this, other forms of online advertising are expecting similar growth.

The ease of programmatic ad buying and the vast growth of the market has lead to a rise in ad fraud that has infected the advertising ecosystem. Some estimates state that up to $42 billion will be wasted in 2019 due to fraudulent ads.

Some measures have been taken to protect against some basic types of ad fraud. But to counter more sophisticated fraud, advertisers need a more robust solution to counter the huge amount of ad fraud that exists in the industry.

Location could be this solution. Understanding device location and historical behavior can help to identify fraud better than other methods. In this post, we’ll look at the ways that advertisers can use location data to reduce ad fraud and limit the damage from malicious actors in the advertising ecosystem.

What is ad fraud?

Ad fraud is the practice of fraudulently impersonating online advertisement impressions, clicks, conversions or other KPIs in order to generate revenue.

Eliminating ad fraud with location

Does the device exist?

The first step is to identify if the device is a real device and not an emulator. Emulating a device is a common way of faking ad impressions and clicks. In some cases, emulators can even generate IP addresses to pass as a real device.

In this case, location needs to be more precise; the ad inventory needs to combine with sensors to verify that the device exists.


Are the ads landing where intended?

Let’s say that you have a campaign running in the US, and you are only targeting devices in the US. You can use location data to map where the ads are being delivered to the device. If there is a considerable disparity between targeting and delivery, then it’s highly likely that some of your campaign inventory is fraudulent.

Identifying fraudulent inventory is essential as often devices can change or move, and the targeting solution will not update these. But other times, audiences can contain bad inventory, deliberately including devices that don’t meet the criteria. This is why you should always carefully vet your data and audience providers.


Countering smarter ad fraud

What about eliminating the more intelligent fraud? Some ad fraudsters are generating fake IP addresses to spoof IP location monitoring.

Using a powerful location SDK can eliminate this. An SDK uses many signals to identify device location with greater accuracy correctly. Subsequently to trick a location SDK into registering a click in a fake location is much harder to do than a simple IP trick.


What about click farming?

In some cases, fraudsters will pay a real person to click and interact with specific ads in several different locations. Sometimes these devices are kept in one place; other times, they are the person’s personal device.

Location data associated with a device can be used to see if the device moves and behaves like a regular device. Understanding if a device stays in one place and combining this with other fraud detection methods, such as time to install, can dramatically increase the identification of ad fraud.


Towards a version of location ad fraud detection.

Integrating precise location into your ad stack can have a substantial positive impact on your ability to detect ad fraud. But using location data in a more traditional way can also help to understand if clicks and conversions are being bought.

For example, location-based attribution is the process of measuring if an exposed device eventually visits a physical location. This can also act as a way of vetting audiences and inventory.

Location data has many powerful applications across the advertising ecosystem. Detecting ad fraud is another application that allows advertisers and marketers to use location to reduce wasted budgets and identify partners that may be supplying them fraudulent inventory.


Big Data In Real Estate – How Location Data Is Transforming Real Estate

For real estate, there’s nothing more important than location Whether looking for a place to live or identifying the best place for commercial property – location is the most crucial factor.

Companies in the space are looking beyond simple location classifications like zip codes or areas. Instead, more robust modeling and new datasets have opened the door to understand how neighborhoods are changing and what the people who live in specific areas want from their location.

It’s no surprise to see that big data is transforming the real estate industry. At the center of this is location data.

Let’s look at how data is innovating the in real estate space.


Data for real-estate investment

Evaluating and investing in new real estate opportunities requires extensive research and due diligence.

It pays to be the first to identify an up and coming area, but many companies have access to the same resources to try and understand where these hot neighborhoods are.

Emerging data sets, such as location are changing this. Movement data is giving real estate investment companies a head start, allowing them to understand the potential of a neighborhood before the competition.

Big data is allowing companies to answer the difficult question such as:

  • How to identify new neighborhoods or areas that are good prospects for investment
  • How to understand regional risks when investing

Location data is incredibly valuable in this area, not just because of the geographical nature of real-estate, but because the value of a property or area is determined by what happens in the surrounding area.

Real-estate value is closely linked to social and economic conditions – concepts that can be understood by using device location data. So the better your data, the better your insight into the investment potential of a specific region or area.

Utilizing location data alongside real estate demographic data and other third-party data sets allow real-estate to identify an investment opportunity early and measure and analyze the investment that you have already made.


Planning and development

For commercial real estate, the power of data is just as impactful as in the residential sector. Forget about costly to integrate footfall management systems. Location data can understand footfall across all real-estate properties, including your competitors. The most successful companies are using these real estate data analytics to adapt and plan in real-time.


Identify the most valuable spots

Location data can identify footfall in retail properties, both passing by and entering. Detailed metrics into visits and visit length can help understand the value of each location.

These insights can even lead to dynamic rent pricing or could also function as an add on product for tenants wanting to understand the benefits of their commercial property.


Increase operational efficiency

Real estate data can also help to improve operational efficiency. For commercial properties with vast amounts of footfall, such as airports, understand patterns around movement can significantly contribute to enhancing operations and optimized unused space.

These insights can be extended when developing new properties and planning from the initial placement of the project, to individual placement of retail booths inside. Predictive analytics in real estate is now commonplace and is having a huge effect on planning and development in the industry.


Advertising and marketing

Data isn’t just for planning and investment. Data has transformed the world of advertising and marketing, and this is true for the real estate industry.

Over 90% of home buyers start their search for a house online. Data is filling in the gaps between what the consumer wants and the ads that real estate delivers to these potential customers.

Datasets are making marketing and advertising more personal for these prospective customers. Ads can be targeted to those who have visited estate agents or personalized based on regular behavioral patterns. All allowing the real estate company to engage with a new audience in a personalized way.

This behavioral data can also be used to create a more engaging search process – where results are tailored to the user. This extends through to any consultation process, with the rise of chatbots, such as WhatsApp Chatbot and automation solutions requiring powerful data to be more productive.

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Best Guide To Mobile App Engagement And User Retention

Improve user retention, boost app engagement metrics, and improve your bottom line. 

Simply put – mobile app engagement is providing your users with a reason to keep coming back to your mobile app or open your mobile app and perform the desired action.

You must create an engagement strategy that boasts high-quality communication.

As well as this, you must use the data, analytics, and insights from your users in a business dashboard to help you to learn which part of your app engagement strategy is working the best.

App engagement is all about putting the user needs first. There’s no quick fix. It involves defining your essential app metrics and KPIs. Then creating an app communication strategy that is relevant to your mobile audience.


What are the most common mobile app engagement metrics and KPIs?

If you’re looking at how to improve your mobile app engagement, then you’ll be looking at developing these specific mobile application metrics.


Active users

This is a crucial metric for app developers, and it’s kind of the going currency in the world of apps.

This metric will help you to understand how useful and important your app is by identifying how many users come back to your app.

This can be done on a daily basis or a monthly basis. Both are, but daily active users is a sign of a genuinely engaging app.

DAU = the number of users who opened your app in a single day

MAU = the number of users who opened your app in a month-long period


Retention rate

This metric tells you, as an app developer, what percentage of customers are coming back to your app. On the flipside, you’ll be able to see how many users you are letting go.

The periods that you are comparing will be dependant on the insights that you want. Should you compare MAUs to last month’s active users, or should you compare to the previous year?

With retention rate calculations, it’s essential to look at the metric you are measuring. For some apps, it will make more sense to measure logins rather than only app usage.

Retention rate = the number of users that use your app within a set time/ the number of users in the same group that uses your app in a previous time.


Churn rate

This is a simple one – it’s used to identify the percentage of your users that are lost.

Churn rate = 1 – your app’s retention rate

Session length

This metric is the amount of time that a user spends in your app each time they open it. This is an excellent indicator of app engagement as time spends in each app is one of the key ways to tell how useful your app is to a user.

Depending on your app, it might make sense to focus on this metric rather than DAUs.

Session length = time opened the app – time closed the app

In general, the more time your active users spend in your app and the more screens that they interact with, the more engaged your mobile users are.

That’s why focusing on experience, rather than just individual features, is critical. Let’s look at how you can increase these key app metrics.


Push notifications

Push notifications are one of the most effective ways to engage your app users. But, if misused, they are one of the quickest methods to app deletion. The truth is that many mobile apps seeking to engage their users fall into the second category.

So how often should you send push notifications to your users?

With push notifications, it’s about providing the most value to your app audience. If your mobile users are getting value from your app, then they are going to be more engaged.

That sounds obvious, yet so many app engagement strategies fail to consider it. There are so many push notification services that claim that quantity is key to boosting engagement. But, this will very quickly have a negative effect if you don’t consider personalization and relevancy in your push notification strategy.

It’s also vital to understand push notification statistics when trying to engage your audience.

So, let’s look at how you can engage your mobile app users by building a push notification strategy.


Only send push notifications when it’s relevant to do so

If you look at your phone right now, you’ll probably see an extensive list of notifications sitting there, unanswered. Never swiped and hardly even read. If you want to engage your mobile app users, you need to create a positive impression of your app in the minds of your users.

Spamming your users with push notifications isn’t the way to engage your users. You need your users to enjoy reading that notification when it comes. You need to provide them with value.


Content is key

The first step is thinking about the content. Just because it’s the latest app feature your team created, doesn’t mean that your app users will engage with countless notifications about it.

You need to think like an app user. What do app users engage with? What do you, as an app user, engage with most? Which apps on your phone are doing this best? If you can think like your users, then you’ll start to get on track with your app engagement strategy.

This involves clearly defining the instance in which your mobile audience wants you to speak to them. This may require in-app analytics and feedback tools, but we’ll get onto that in good time, don’t worry.

If you have a mobile restaurant application, for example, why would you send them notifications when they are in the bank. Why would you remind them to make dinner at 11 pm? Why would you ask them to choose their perfect recipes when they are on holiday? If you have an instant messaging app you don’t want to set any limitations. 

Highly targeted push notifications can increase response rates by up to 7x and will dramatically increase your app engagement rates.

But how do you control how, when why and what push notifications your mobile app users receive?


Location-based push notifications

That’s right; get yourself a platform or a service that lets you control when your users receive push notifications.

It’s simple – if your users are in highly essential moments, then send them highly relevant communication. That’s it.

Let’s look at the example from above. What a difference it would make to have a recipe app that suggested you make a saved recipe when you were in the store, and lacking recipe inspiration.

That’s what your app was supposed to do, yet you aren’t giving it the change to help users in the right moment.

This is where location comes in. By predefining several supermarkets using a simple online platform, you can ensure that push notifications are only delivered at relevant times.

The application applies to all apps looking to implement a successful mobile app engagement strategy. Define the optimal moment for your mobile users to use your app. Send them optimal notifications ONLY in these moments. Improve engagement. Get insights and use this to inform your engagement strategy and fine-tune.


What makes an excellent push notification?

Let’s look at some examples of good push notifications that will keep your app users engaged.

For this, I’ve imagined some generic apps rather than real ones. Although we’d be happy to give you a specific demo for your mobile app.


App with a physical location or venue

If your app has a real-world counterpart that was supposed to benefit from your app. Then location-based push is perfect. You probably have a good idea of how your audience uses your products or services.

A good engagement boosting example for this kind of app would be:

Of course, in this example, the user receives the notification just as they enter the restaurant. 


Stand-alone discovery apps

Okay, so you don’t have a retail store, just an app. That’s fine. You might need to spend some time learning how your users get the most value from your app. But that’s fine, that’s one of the most critical aspects of this kind of engagement strategy (again more on that in a short while)

In this example, you’ll need to understand what engages your users best, look at the data, and then rinse and repeat. An example:

In this case, the micro-moment could be as they are leaving another nightlife venue. Thus avoiding spamming all the users that have decided to spend a Friday night in.


Apps with a specific function

What if your app provides a vital service? Location-based notifications can help to engage users by bringing this function to them at the best possible moment. 

Here the user would be notified when they land at an airport. Those eagle-eyed amongst you might ask – how would you ensure that the user doesn’t get the notification on the outbound part of the trip? Well, triggers can be based on complex location signals; in this case, the second time they are seen inside the airport within a certain period.


Some homework (spoiler – it’s much easier with insights)

What micro-moment should trigger notification delivery?

What is the best way that you can personalize the notification based on this micro-moment?

What is the desired goal of the push notification?

What are the critical engagement KPIs that this campaign should improve?


Re-engaging your mobile app users

One of the most effective ways that apps can improve their user retention rate is to re-engage and retain their mobile app users.

Often many apps neglect the customers that they have spent countless mobile app marketing dollars on acquiring.

After 24 hours, an apps retention rate falls to 21%. By day ten, this figure drops to 7.5%. After 90 days, it’s a measly 1.89%.

Therefore a significant increase in retention rate can be the most important strategy for app owners. Rather than placing your entire budget into acquiring new users, you should be focusing on re-engaging your users. Just a small rise in app retention rates can have a huge effect on your bottom line.


Fixing your app on-boarding process

You need to ensure that the basics are in place for you to keep engaging your app audience. This means that your onboarding process should be seamless, provide value, and explain exactly what it is that your app does.

Think of these as the perfect blocks to build your app user experience. The engagement strategy is the cement that fine-tunes it and links it all together.

For a more detailed list of app onboarding best practices check out this.


Deep linking from push to relevant in-app location

So you crafted the perfect notification. Congratulations. Your users clicked it. And it directed them to…

The app home screen.

Again it seems obvious, but many apps get this completely wrong. Choose a push notification service that lets you link to highly relevant app experiences.

They probably exist in your app. So make sure you are improving the mobile app experience by allowing your users to get to it quickly.

If you want to improve app re-engagement then getting your users to notice your app is just the beginning. You’ll want to ensure that your personalized notification takes the user to the right place.

With many location-based push notification services, it’s possible to deep link to the right content based on the user’s current location.

That could be the most recent content to keep delivering your users a fresh experience and keep them engaged.


Think about your app experience

A note on personalization – ultimately, your app engagement metrics will improve if you place personalization at the heart of your app engagement strategy.

This means that you need to think of the user at every point in the user journey. If you want to take your app engagement to new heights, then you’ll have to personalize the user experience, clearly define your app’s KPIs and learn how your users want to engage with your app.

But that’s only the first part. How do you keep learning what your app users are engaging with and what elements of your strategy in performing best? Well, that leads me nicely onto…


In-app analytics and insights

None of the above will matter if you don’t commit to learning what works best with your users. Every app is different with different app engagement KPIs.

Analyzing your engagement data is key to building an effective app engagement strategy.

Push notifications give you valuable insights around your users. If you want to know how to improve your app user engagement, then you need to understand mobile app analytics.

The feedback from your push campaigns helps you to understand what engages your mobile users.

I’m not just talking about the age, time, gender, and device type of your users although those can sometimes be helpful.

I’m talking about understanding in which micro-moment your users are most likely to engage with your app.


Understand your app’s micro-moments

This is such valuable information. Many apps have an idea of what this moment might be. But often, their idea of what this is is quite different from what the insights say. Data should be everything for your mobile engagement strategy. And it’s time to take this data to the next level.

When you send a push notification to users, and you know that your users are opening them in a particular context, this is valuable information.

These insights even go beyond your app engagement. They can help fundamentally to inform everything to do with your app growth strategy. From most crucial new app features to UX and monetization.

If you know that more of your users are engaging with your notifications in a certain location, then you get a better idea of your app audience. You can understand them better and hypothesize the specifics that will help to improve app engagement.


Location-based insights

In a world with over a million apps, it’s important that you leverage every piece of data that you can around app engagement. You need to make data, your best friend if you want to keep developing your app engagement strategy.

If you can get data around your app users that the majority of apps can’t get access to then your onto a winner.

The truth is that many app engagement strategies fail to understand where their users go and how they behave.


Beyond basic engagement insights

For example, you might get feedback around how your users are opening your push notification, using specific in-app features, or even just opening the app.

These insights might be based on time of day, or maybe you can even get a breakdown of this data based on audience type (depending on which service you use).

But what if you could get a better insight into the mind of your user at that time? Basic insights are great, but it doesn’t always paint the perfect picture. You need to get as much data as possible if you are going to keep engaging your app users.

Location insights around app users can help drive mobile app engagement KPIs. If you can understand exactly where your users go and how they behave, then you can create a better idea of how to engage them.


Engagement data that retains users

For example, let’s say you have a sports app and you might send a re-engagement notification that performs reasonably well. You look at the data available to you, and you see that a sizeable chunk of these notifications was opened between 12-3pm. Now that is a great insight, but what if you could learn more?

If you layer location insights around that data, you might see a more useful pattern emerge. You could see that the majority of these notifications are opened in bars, and even more, specifically sports bars.

Now you can begin to hypothesize and fine tune your value proposition. You can see that the majority of your users are using your scores app while they are watching a game in a bar or sports venue.

This all links back to your app engagement strategy. You have a better idea of how and where your users get the most value from your mobile app, and this helps you to develop your app engagement strategy.

By understanding your user’s behavior, you are much better placed to say which factors are most likely to boosts your app engagement KPIs.


Make sure you choose the right app engagement platform

Choose mobile app analytics and communication platform that works for your app. You need a mobile engagement platform that allows you to reach your app users in the best possible moment and understand how your users respond and behave.



Think about which app engagement metric is most important to you.

Clearly define which aspects of your app engage your users.

Place the user first. Think about providing value to your users rather than communicating with them for the sake of it.

Use highly personalized notification to engage your users in the best in-app micro-moment.

Re-engagement can be the most effective way to improve your app revenue or bottom line.

Take a data centric approach to engagement.

Always be ready to hypothesize and learn from your engagement data.

Follow these rules and you’ll be well on your way to creating a mobile app engagement strategy that works for your app.



What is app engagement?

App engagement is used to refer to a number of metrics that measure how users are engaged with your app. These can give developers better insights into how their apps are being used.

What is app retention?

App retention is a metric which is esentially the percentage of people who continue to use your app over a given period of time

How do I boost app engagement?

A combination of good onboarding, active engagement such as push notifications and delivering personlised app experiences.


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Best Guide To Mobile App Monetization 2021 – Stats, Strategies & Insight

The ultimate guide to app monetization

This guide is everything you need to know about app monetization. We’ll breakdown different strategies and look at the pros and cons of each. You’ll learn how to optimize and generate impressive revenue from your app.

We’ll also look at app revenue trends as well as hybrid monetization. So strap in and get ready to monetize your mobile app audience.

This is a full-length guide and therefore, will take a while to get through. Luckily we’ve added some useful links throughout to help you navigate to the relevant sections.

This guide is relevant to all app owners and developers. Whether you have a free app or a paid app, some of you will be in the early stages of app monetization journey. Some will be experienced hybrid app monetization experts. This guide is for anyone who wants to generate revenue from their mobile app.

Either way, we hope this guide leaves no stone unturned in your quest to understand what is app monetization. You’ll learn how it works and how to make sure that you get the best revenue from your mobile app.


First of all a definition – what is app monetization?

In one sentence – app monetization is the process of converting your app users into revenue.

This process involves multiple strategies. Some categories of apps are more suited to specific app monetization models than others. Some apps focus on one particular area of app monetization, and others incorporate multiple aspects.

As a developer, you’ll need to generate revenue from your app. In the app economy, it can be challenging to stay afloat. Unless you have secured a nice amount of funding, it’s essential to read the advice in this post.

Follow our implementing guide and make sure that you ask any comments at the very bottom.


Why is app monetization important?

App monetization is crucial because it has become more common to find that apps are free at the point of install. The app business model, therefore, needs to be adjusted to account for this.

Developers must shift their revenue model to generate cash after download. This is where your strategy comes in. It’s crucial to take the time to make one that ensures these two things happen:

  • Your app generates growing revenue.
  • You keep your users and the user experience relatively intact.

A lot of people forget about the second point. It’s just as important to look at how mobile monetization affects the app experience as it is to maximize revenue.


Why is user experience important?

Experience is crucial to a successful app monetization strategy because revenue requires happy users.

Monetization mostly harms the app user experience. This can be mitigated and reduced, but it is still there. Lowering the user experience causes some users to be turned off.

Monetization revenue is generally calculated based on the number of active users. As this figure is directly affected by user experience, it’s crucial for developers to consider this when deciding an app monetization strategy.


Stats and figures around app monetization

There’s one stat that shows the importance of app monetization in today’s mobile world.

In 2015 global app revenues reached $70 billion. By the end of 2016, this had risen to $88 billion.

That’s a significant rise in a single year. But if we look at predictions, by 2020, the global revenue from mobile apps is set to hit $190 billion.

Now that’s a significant market for developers to tap into. But let’s dig deeper into app monetization.

App monetization strategies are still dominated by in-app advertising. Ad formats are indeed getting better – incentivized advertising is driving app monetization, and app revenue models are increasingly packed with advertising. Native ads are popular as well – something that the app ad space loves to point to as progressive in-app advertising.

Paid apps are still prevalent in both app stores, with 20% of apps adopting these app revenue models. As an app revenue model, this is remaining steady – but the growth of subscription models are becoming more popular as the idea of recurring income seems attractive to developers.

But what can you learn about your app revenue model by looking at these statistics around mobile app monetization?

App ads are on the rise. But will developers see the bubble burst? There could be a reward on offer for apps that steer away from the advertising app revenue strategy in the future. And this is entirely possible with the growth of alternative app monetization methods.


What can we learn from this?

Advertising is still the most popular app monetization strategy. But it’s interesting to see that it is decreasing per user. The main reasons for this could be the fact that revenue per user is declining as more apps look to get into advertising. This causes a race to the bottom in terms of revenue per user. But more on that later.

Another point to make is that pay per download is on the decrease. As more and more apps look to monetize after the point of purchase.

Finally, subscription models are becoming more popular. Pay monthly models are working in so many other industries. Look at Netflix, Spotify, etc. App developers are catching on and realizing that a subscribing, engaged user is worth more than a single paid user.

We’ll discuss all of these points in more detail as we look at the different app monetization strategies. We’ll also talk about the issues and patterns that occur in the app monetization world in the later ‘trends section.


The app monetization strategies – a complete overview – how will your app make money?

Now for the part where we get down to it. What app monetization strategies are there? Which ones are most effective? Which generate the most revenue for your app?

App monetization strategies can be complicated, and it can be very different from monetizing templates or courses. There are many different ways that you can generate revenue from your app. Some developers focus on one, and others take a hybrid approach. Or you can even make a best Notion templates guide, for example.

Take the time to familiarise yourself with all of these methods. We’ve tried to include which app categories work best for each monetization method.


In-app advertising

As we previously noted, this is still the most popular amongst app owners. It generally generates a lot of discussions. There is no simple one size fits all approach to in-app adverts. Each app implements advertising differently. But there are some general tips for advertising in apps.

  • Benefits – quick to implement, simple app monetization process.
  • Concerns – can affect the app experience, only generates significant figures if you have a broad app audience.

The short truth is this – without in-app advertising and mobile ad networks, a lot of apps wouldn’t exist.

So let’s look at the different types of in-app adverts that are common.


Banner Ads

These are the original app advert. These were more common when apps had a free and paid version. A quick way to generate revenue was to have an ad-free version. But, the fact that people were happy to pay not to see any banner ads illustrates the problems.

What’s so bad about app banner ads?

Let’s focus on the UX. They are ugly and intrusive. They divert the user’s attention from the app experience.

I could go on about how damaging the look of banner ads are for your app. But there are more negatives I’m afraid.

The ads are generally so small on a mobile screen that the advertiser doesn’t get much value from using the space. This means that they are usually not willing to pay much for the privilege. They have low engagement rates. For these reasons, the CPM is pretty bad.

The short of in-app banner ads – people don’t interact with them. They annoy the user, and you won’t even get paid much for using them.

Well, perhaps that’s why they are dying out then.


Interstitial Ads

Developers are looking at alternatives to bad in-app advertising, such as banner ads.

The main problems with banner ads are the size and the fact that they are intrusive. One potential solution to this problem is to take the same advertisements and show them as a full-screen ad to the user. This occurs between separate user flows. Hence the name interstitial.

To get the most out of this strategy requires you to fully understand your app users and how they use your app. Make sure that you don’t inadvertently ruin the user experience.

The best time to deliver an interstitial ad is at the end of a flow. For example, when a level is complete in a game app. It’s also a good idea to utilize interstitial ads when the app is loading. This gives the user time to understand the ad and think about its content.


Native Ads

Native ads are mostly ads that have been adapted to the feel of an app. The ads integrate seamlessly into the app. This usually involves a feed of some sort, where the ad looks like another post in the timeline.

More common amongst publishers as well, native ads are a step in the right direction. They do little to affect the user experience when applied correctly.

Native ads have a higher engagement rate. This is probably since they ‘blend in’ with the app features. This does raise some questions about the effectiveness of the ads. If the ad is essentially tricking the user into clicking as they think it is an organic part of the app, this will harm the user experience.

The key is to make the native ad look and feel ‘native’ while also providing a clear indication to the user that the content they will land on is an advert. Twitter does this well on mobile.


Affiliate Ads

Affiliate ads are a method of app monetization that allows apps to generate commission from other apps, products, and services by advertising them through your app.

Affiliate ads work because people like to be referred to something. If they trust the source, then this method can be quite useful in converting.

Again the key thing to remember is the experience. Try and link the advert to appear at relevant points in the user journey. Perhaps when the user is in between levels, the ad could suggest an app that is similar to the situation the user finds themselves in.


Reward ads

App reward ads are popular, where users spend a lot of time in the app, such as games. In this scenario, users are offered a reward to engage with content.

So for example, in a game, you may be offered an extra life if you watch a 30-second advert.

For this to work, you have to get the ad and the reward right. Try and keep the content relevant to your user base. Make sure the reward is delivered at the right moment and is valuable for the user.


Summary of ads

Generally, more developers are becoming concerned about how advertising affects the app experience. A broader conversation is emerging. Developers are asking – which is the best ad format to protect the UX?

We’ve come a long way since the early days of mobile banner ads. Mobile app advertisers have realized that protecting the user experience is vital to ensure the survival of apps.


Subscription and the freemium model

Many apps are now looking at subscription models as a way to generate app revenue. It’s becoming more popular amongst developers for a variety of reasons. Again, we have the fact that users are more used to not paying to download apps as the reason for this.

A subscription model means that the user can download the app for free. They then get access to all or some features of the app for a specific time. Once this period is over, they will need to pay a recurring fee to keep using the app.

It’s easy to see why this app monetization model is becoming so popular. The developer gets a constant stream of revenue. It’s easy to predict. In some cases, it can bring in much more significant revenues than other strategies.

This is because once a user pays to use your app service, they will invest time in the app. If this requires input, they are unlikely to want to stop paying for access.

The app subscription model works best alongside a compelling app with a clear function and user experience.

  • Benefits – steady, reliable income. Little effect on the user experience. Can significantly drive engagement.
  • Concerns – requires a lot of investment to create a great product and a seamless experience to get users to part with cash.


Apple loves subscriptions

Apple realized the benefits of apps that keep the customer for more extended periods. They have offered developers on the app store a better revenue share on the income from the subscription apps.

The standard split is 70/30 (Apple takes 30% of app earnings, the developer takes 70%). But Apple now offered an 85/15 split for subscriptions that last over a year.

This is now common in both app stores. It is fuelling the drive toward subscription models for app developers.


Data monetization

We talk about user experience a lot when talking about app monetization. That’s because it’s crucial to keeping your app audience engaged with your app. Without an engaged audience, it’s impossible to sustain effective app monetization.

That’s why data monetization can be one of the most effective methods of app monetization.


What is data 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.


Why data monetization?

The app experience is becoming less important for developers as they look to implement as many app monetization strategies as possible. Have we forgotten about user experience?

Many mobile app monetization strategies are based on delivering an advert to the end user. While these can generate app revenue, little attention is given to the effect that this will have on the user experience.

Many mobile ad networks are making a lot of noise about native ads as a method of app monetization, but is this the experience that users want from mobile. App revenue is growing, but surely the messy in-app ad bubble will burst when developers realize there are alternative app monetization options that are big app revenue generators, without negatively affecting the user experience.

These strategies exist, and more developers are adopting these app monetization strategies.


Data as a useful app monetization strategy

Revenue from customer data has been commonplace in other industries for a while now. This can and should be extended to mobile app monetization. With CPMs that are much higher than advertising app monetization models, it makes more sense for developers to try generating app revenue from user data. Along with the bonus that apps can hold on to their beautiful user experience as this app monetization model operated in the background.

Of course, many are quick to criticize this method of app monetization. But the issue demonstrates a broader problem that is prevailing around app monetization in general. Users are so used to apps operating on some free app monetization model that they generally forget that are paying with something other than money.

It can be flashing adverts, or it can be data app monetization. Either way, the conversation around app monetization needs to be more explicit. Users need to understand precisely why apps are free. Data collection process need to happen securely, and they need to have a transparent opt-in process, but that doesn’t mean that it’s not a viable app business model.


Powerful, first-party data

Data that you collect directly from your app is called first-party data. Many apps are not doing this, and they are sitting on a pretty large, untapped pile of app revenue. And that’s fine – but in this competitive arena of app monetization, and along with the development of secure, non-identifiable data collection methods – apps should no longer be afraid of leveraging this data.

The data can be used for a developer’s own needs – understanding user behavior and interactions with app/features is one. By leveraging robust and accurate user data, developers can understand how their app is used, where users get the most from their app and where to improve.

The data can also be used in tandem with an apps current advertising inventory to boost ad price. If you use data to trigger in-app advertising, then you create more relevant adverts. This means higher inventory price.

Data should underpin everything that you do in your app, from app engagement to app monetization. With the development of advanced audience SDKs, developers should no longer be afraid of leveraging data from their app audience.

A clearer conversation needs to be had around why apps are free at the point of use – a data monetization model is no different from a subscription, freemium, or ad monetization method. Stress needs to be applied around clearly communicating what it is that the user gets in return and providing clear opt-out channels for those who don’t wish to share their data.

Aside from these main two benefits, it also means that you are not held to account financially by the platform that your app exists on. The revenue is generated externally. That means that there’s no commission with the app stores. There’s no worrying about which platform your app is most prominent on.

How to get started – make sure you find yourself a valuable monetization partner. Ensure that they can abide by the relevant opt-in processes. Privacy and security are essential without a data monetization strategy.


In-app purchases, virtual goods and currency

This is a method that has become more popular with games apps in recent times. Apps generate money by selling virtual or physical goods from within the app.


Virtual currency

One way in which app developers have cleverly tapped into new revenue streams is to allow the user access to virtual currency. Users purchase this currency with real cash, and it used for various means within the app.

Usually, this currency is used to get ahead in the game or redeem certain features and services that would usually take a long period of time to unlock.

There’s a balance to strike here. The user must feel that they are getting value for their hard-earned cash. But they must also keep playing the game to pay more money. That’s why it’s essential to keep the game or app interesting for non-paying users as well. If other users that aren’t willing to get their wallet out stop playing, then paying users will also decrease if there’s no one to play with.


Physical product or service

There’s a lot of variety in-app monetization. If your app us a subset of your business then in-app purchases are going to be a large part of your app income. In exchange for your physical product or service, users can pay quickly and using the build in payment structure.

There’s not much to say about this strategy apart from that your physical good or service must be top quality if you want to increase your revenue.


The commission

Apple and Google both take 30% of every in-app purchase through your app.

That must make you wonder how the large service apps like Uber and Airbnb manage to make a profit on the back of that 30%. Well, they don’t pay 30%. If you’re big enough, you have the power to negotiate individual commission rates with the app stores. Unfortunately, for most apps, this isn’t possible, and you’ll have to abide by the rules.


Transaction fees

This method is kind of a pivot of existing marketplace methods. For apps that have a marketplace or if they include audience transactions of a significant kind, this is an excellent way to monetize app users.

The main benefits of this method are scale. If you can keep growing your audience and the audience activity within your app, then this app monetization method will scale alongside this growth.


User marketplace

The idea is that you take a percentage of a transaction between two users on your app. For example, when someone sells an item, you take a percentage of the amount. This is communicated upfront, but the difference to traditional marketplaces is that the seller doesn’t pay a listing fee. This encourages users to use your service.


Transactional apps

An emerging breed of mobile apps that use transaction fees to monetize is financial apps, or invoicing apps. These often offer the conversion of currency (think Bitcoin) or the option to trade in shares or other markets. Every time the user makes a transaction, the app makes revenue. An excellent example of this is the Bux app, where they take a percentage of each sale that occurs in the app.

This app monetization strategy provides scalability. It also gives developers the ability to accurately predict revenues based on users and numbers of active users. You can also increase revenue directly by investing in engagement and new users. This gives you better and more stable metrics to manage your app business.


Best practices for app monetization – how to improve the bank balance

It’s all about the experience

Protect the user experience at all costs. You’ll do more damage to your monetization by damaging the user experience. There’s a two-pronged approach to this. Keep your experience clean and ensure that app monetization does not harm your app experience. If you have to alter the experience in some way (ads etc.), then manage this so that the impact is minimal.

The other side of this involves actively increasing engagement. Improving app engagement ensures more time spent on your mobile app. This leads to greater monetization.


Keep bringing in new users

To scale monetization, you’ll need to keep investing in user acquisition. Don’t take your foot off the pedal here. You’ll always have user churn. This requires you to seek new users to grow monetization actively.


Hybrid app monetization

It’s perfectly fine to adopt multiple app monetization methods. It’s recommended. App monetization methods can be implemented alongside each other. Just b sure that doing too much won’t negatively affect the user experience.


Measurement and analytics

Measure your monetization, optimize and adapt – an important part of any app monetization strategy. Ensure that your monetization partner can provide in-depth insights on revenue, users, and geography. Always be on the alert to fine-tune your strategy using data.


Keep up to date

Keep on the lookout for changes in policy from the major app platforms. This is important as it could change your strategy overnight.

For example, the decision to reduce the commission on app subscriptions changed many app’s approaches to monetization. Keep up to date with the latest blogs and resources.


Your app is unique

Don’t take other developers use cases as proof that it will work for your app. Every app is unique. Just because something works for another app, doesn’t necessarily mean that it will work in the same way for your app.

Test methods before fully implementing them and always focus on the differences between apps when looking at other use cases and statistics.

If you do go down the ad route consider placement and timing

The ad route is entirely viable for many apps. In-app adverts can be successful, but make sure that you invest the time to consider the placement and timing of these ads. One wrong decision can cause you to lose a lot of users, so be completely sure that you get it right.


Are you missing out on data monetization

We’re always amazed at how many app developers are unaware of some of the different monetization strategies out there. There’s a massive drive towards in-app ads, and little alternative is presented to developers when they begin on their app journey. They are missing out on vast amounts of app revenue.

What I’m saying is – you could say goodbye to the wave of ads that you’ve been thrusting into the faces of your users.

But what if I told you there’s a better way to monetize your app. One that means that you won’t have to sacrifice your app experience. And one that can make you more money than your current monetization strategy.

Well, there is a solution, it’s called app data monetization.


How to monetize an app with revenue from data

There are a few different types of data app monetization, and it’s pretty specific to the app in question. But there’s a better way for developers to generate consistent app revenue. Better yet, you can do this while prioritizing the app user experience.

One of the most effective includes identifying precise location data from mobile apps to understand consumer habits or behavior better.


What are the benefits of app revenue data as a monetization strategy?

The potential for no in-app ads – you heard me right. This means that you can stop delivering those annoying banner ads to your users. Or don’t. DO both if you’d like. Data app monetization works in the background, so you don’t have to worry about it affecting your app experience at all.

Higher CPMs – That data is extremely valuable if it’s precise. Your partners are an essential thing to consider with this kind of monetization strategy. You must communicate with your users, and it’s essential to do this properly. Being upfront with your users on why they are receiving a free service is something that all developers can improve.

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

It’s ultimately one of the best app monetization strategies out there. It’s a great way to monetize your app without passing on the cost to your audience. Keep your app engagement and monetize at the same time.

We’re not saying that you should cancel your previous app monetization strategy. There are always app monetization challenges for developers, and data monetization isn’t the solution to all of those problems. However, it can still be tested in a wider app monetization strategy. This way you’ll learn if it’s right for your app.


A note on user privacy and opt-in services

It’s important to educate your users on app monetization. All users should realize that the reason their app is free is due to the monetization of themselves.

That’s why it’s important to be up front. Have the conversation with your users as a part of your onboarding process. The best kinds of app monetization strategy clearly explain to users why the app is free and how you ensure that it will be using monetization.

Building trust with your users is key to this kind of monetization strategy.


Trends in app monetization

App experience > app monetization

In-app ads remain a popular method of app monetization for developers. Despite them having obvious drawbacks when applied poorly.

A common trend that we see emerging is that more developers are focusing on experience rather than pure revenue.

In 2020 app advertising will be all about the user experience. Developers must strike a balance between the number of ads, where they appear and how the user interacts with them. This will be pivotal to app monetization success. App owners will also have to consider how these changes will affect their users in 2020. Too many ads will negatively impact the user experience. But that doesn’t mean that it’s impossible to provide value while delivering in-app ads.

Don’t expect revenue from app ads to jump to new heights anytime soon if anything expects app ad revenue to decrease as more apps adopt in-app advertising. Perhaps 2019 could be the year to supplement your app revenue with another method.

Mobile app advertising is maturing quickly. Make sure you look for a network that uses safe brands, smart ad targeting, and provides support for interactive ads.

When integrating an app advertising strategy, you may find a trade-off between ease of integration and spamminess of ads. In 2019 it might be worth taking the time to focus on putting user experience first.

It’s clear that keeping people engaged with your app will have a better effect on monetization. App owners will need to get balance right. More importance is being placed on experience with revenue from ads not going up anytime soon.

We see this in our research. Our survey of app developers showed that two-thirds of developers now think that focusing on the app experience or improving the app experience is more important than monetization.


Say goodbye to paid apps

Freemium and subscription-based apps are here to stay. Offering apps free at the point of purchase allows developers to get downloads easier. They can then educate the user on the benefits of upgrading or paying for premium features.

Freemium is allowing app owners to increase session length and generate engaged users. This is a great place from which to convert users into healthy revenue. After a positive app experience app users are more likely to opt-in for premium features. Having the chance to nurture and educate your users before this has a positive effect on your app monetization strategy.

Try not to appear like you are cheating your users. Make it clear that your app is a freemium app from the very beginning. They won’t want to invest a lot of time in a game or app to realize that they have to pay to use some features.

It seems that freemium is here to stay. With users finding it standard practice to not pay for an app at the point of purchase. Because of this, developers are finding it harder to justify an upfront fee. The freemium app monetization model is an excellent opportunity to engage and nurture audiences for app monetization.


Users will become dissatisfied if they have to spend a lot of money to get features

In-app purchases as a method of app monetization are still experiencing healthy growth. This may be slightly overstated due to the inclusion of ‘services’ as purchases (think Uber, etc.).

One of the main trends well see in 2019 is that app developers will need to focus more on engagement rather than only increasing app monetization.

Once a user has purchased in-app content, then they are more likely to come back and spend more time in the app. This translates to better engagement and retention and in turn, better monetization.

No category has benefited from in-app purchases more than the gaming category. Here, developers are helping by placing engagement first. The user now has the option to pay to advance through the game quicker or access powerups and features.

Developers need to make sure they are getting this balance right. In-app purchases are useful because a few users spend a lot. There will always be users who only want to play your game for free. True these users don’t generate revenue, but they are still important for your app to exist.

While not being a mobile app, developers can still learn a lot from the EA debacle in the new Battlefront game. Users quickly noticed that to unlock some of the features they would have to play the game for 1000 hours. Alternatively, they could pay to unlock them. This seemed somewhat unfair, especially when they had purchased the game upfront.

To keep users happy, developers will need to strike the right balance between monetization and experience.

In 2019 more and more users will become aware of how apps monetize their users. That’s why app monetization methods must be transparent and fair; in the long term, it will benefit you.


App subscriptions will look more like SaaS products

The subscription model is one that looks to remain popular in 2018. Again, users are used to trialing an app and its features before parting with any cash

Subscription models are becoming more complicated than a simple buy or don’t buy. Many pricing structures now more closely resemble a SAAS model. It’s common to see several pricing tiers with many different features.

This allows app developers to persuade users who would previously not part with any cash to subscribe to a lower tier of membership. This method of app monetization is still the best fit for service apps.

A side effect of this is that developers will need to help users understand the benefits of upgrading. More tiers and features mean a better explanation is required.


A conversation will need to be had with users about monetization of data and opt-out methods.

Users are more aware than ever of the need for developers to monetize their app audience. The conversation around app monetization is shifting to help users understand why apps are free.

In 2018 consumer personalization will be a high priority for brands. They will achieve this by using consumer first-party data to help provide an improved user experience.

Mobile app owners are sitting on a lot of behavioral data around their users. This is of value to those who wish to improve personalization for their customers.

Data monetization is secure, private, and becoming more popular amongst developers. Users are more likely to understand that this data will help to generate improved personalization. By communicating the benefits and education users about opt-in developers can monetize their app in this way.

A benefit of app data monetization is that the user experience remains intact. There are no intrusive adverts or the need for the user to pay anything upfront. This means that the user will spend more time in the app and engage with the app’s features. The app monetization strategy can be adopted alongside other methods of monetization.

Data monetization allows developers to monetize a much higher percentage of users. The users don’t need to be engaged for it to work. The revenue that you generate from each user will also be higher. This means you don’t have to worry about monetization in relation to the platform. It’s the same regardless of the device.

Expect revenue from data monetization to increase from a high starting point with better technology. 2018 will see the consumer become more aware of the power of big data and better educated on how it affects them.


Thoughts on these trends

Developers will continue to benefit from the app economy with revenue from app monetization set to grow throughout 2018. Free apps will become the new normal, compared to previously where single pay purchases were the most popular. This will allow developers to generate more revenue over a more extended period of time.

Developers will need to place more emphasis on the monetization experience. This means that the developers are more likely to miss out on revenue from app monetization if the app experience is not up to scratch. Due to the free to download culture, more emphasis on experience and education is needed. This will help to persuade users to enter into premium models and subscriptions or to engage with in-app purchases.

More and more developers will need to adopt hybrid monetization strategies. Developers should not rely on a single method of app monetization. Instead, spreading monetization across multiple strategies will provide stability, especially in a market that can change quickly. The preference of app users is volatile. The changing platform rules around app monetization may also affect developers in 2018. It’s crucial to stay one step ahead!


Useful app monetization links and resources

Mobile development digest – provides a great roundup of the latest information for developers, especially around monetization.

[iOS dev weekly](]( – another fabulous newsletter that often discussed app monetization.

Developers alliance blog – some interesting information for developers, and they regularly post around app monetization.

Developer slack communities – there’s a variety of slack groups that are incredibly useful to developers. Some have dedicated channels for app monetization.

Check out this course that helps developers to create viable app businesses. With tips on monetization and more, it’s quite useful.

Of course, always keep an eye on our blog for everything mobile apps.


Mobile app monetization summary

  • In-app ads are still a viable method of app monetization. However, more focus needs to be placed on the user experience.
  • Users expect to get an app for free at the point of download.
  • Subscription models are becoming more popular as the big app stores offer reduced commission rates
  • Data monetization is a powerful way to generate revenue without affecting the user experience.
  • Always plan – it’s never too early to think up your mobile app monetization strategy.
  • Hybrid app monetization strategies are effective – understand your audience and strike the right balance.
  • Use data to inform your monetization. Keep learning, keep tweaking, and generate money from your app audience effectively.

In-app purchases as a method of app monetization are still experiencing healthy growth. This may be slightly overstated due to the inclusion of ‘services’ as purchases (think Uber, etc.).

One of the main trends well see in 2018 is that app developers will need to focus more on engagement rather than only increasing app monetization.

Once a user has purchased in-app content, then they are more likely to come back and spend more time in the app. This translates to better engagement and retention and in turn, better monetization.

No category has benefited from in-app purchases more than the gaming category. Here, developers are helping by placing engagement first. The user now has the option to pay to advance through the game quicker or access powerups and features.

Developers need to make sure they are getting this balance right. In-app purchases are effective because a few users spend a lot. There will always be users who only want to play your game for free. True these users don’t generate revenue, but they are still important for your app to exist.

While not being a mobile app, developers can still learn a lot from the EA debacle in the new Battlefront game. Users quickly noticed that to unlock some of the features they would have to play the game for 1000 hours. Alternatively, they could pay to unlock them. This seemed somewhat unfair, especially when they had purchased the game upfront.

To keep users happy, developers will need to strike the right balance between monetization and experience.

In 2018 more and more users will become aware of how apps monetize their users. That’s why app monetization methods must be transparent and fair. In the long term, it will benefit you.


What is app monetization?

App monetization is the process of converting your app users into revenue. Publishers need to create revenue in order to offer free apps to users.

How do I monetize my mobile app?

There are many different forms of app monetization. The main ones are through advertising, in-app purchases, and data monetization.

How much money can an app make?

The amount of money that a publisher can make from their app varies based on the number of daily active users they have. It also depends on the type of monetizaiton but publishers can earn tens of thousands of dollars a month in some cases.

Marketing & Advertising

Programmatic In-housing – What About Programmatic Data?

Programmatic advertising spending now accounts for more than 80% of digital ad spending. This spend is increasingly being moved in-house. When surveyed, 35% of brands had, to some extent, reduced the role of agencies.

Over the last few years, marketers have developed this in-house programmatic media buying so that it makes use of first, second, and third-party data sets.

The role of data in the in-housing process shouldn’t be overlooked. It offers many benefits to brands that can successfully create a centralized data environment, such as maximizing ROI, increasing, and enabling real-time behavioral based triggers to create more personal and engaging programmatic campaigns.


Things to consider with data and programmatic in-housing

All of your data in one place

For in-housing to succeed, it’s essential to develop a data-centric environment.

This means that different data platforms, people, partners, and processes are brought together to create a single data solution.

It depends on the business as to whether the data sources are first-party only, or if the organization wants to integrate third-party data sources to improve its programmatic efforts.

Whatever the decision, the key to making a success of in-housing requires a single centralized data strategy.

Another requirement to succeed when in-housing programmatic media buying and programmatic data is to focus on the dialogue between DMP managers and media users. Developing a consistent and productive conversation between these two is one of the main benefits of moving programmatic in-house.

Making sure that you own the data you’re bringing in-house. In-housing allows you to take control of these data sets and use them across different kinds of marketing activities and touchpoints.

External partners are often unable to provide the same, and this means that you are unable to see the effect of marketing efforts on driving business growth.


Improved performance and ROI

Combining 1st, 2nd, and 3rd party data sets into a centralized in-house data solution maximize positive impact on advertising success.

The benefits of integrating 1st, 2nd, and 3rd party data into a DMP that is managed internally will provide a significant boost to marketing and advertising performance.

In-housing data allows organizations to understand customer data better and use it to implement better targeting throughout the funnel.

This drives ROI by facilitating smarter and more personalized cross-sell, up-sell, and optimization.

A combined in-house dataset allows marketers to improve further up the funnel as well. Internal datasets allow for better programmatic lookalike modeling. Anonymized IDs can be sent to DSPs to identify audiences across devices and model new audiences based on real-world behavior.

In-housing doesn’t always guarantee better programmatic marketing performance. But combining it with a data-centric approach can help to improve ROI for marketers in both in terms of short term results and longer-term programmatic performance.


Real-time capabilities

In-housing programmatic media buying allows organizations to be more adaptive and support real-time targeting capabilities. Combine this with data-centricity, and very quickly, marketers can optimize their campaigns to drive even more performance.

Data in-housing has a significant impact on effectiveness and provides a real-time boost to programmatic advertising. It reduces the time for tweaking and improves the speed in which advertisers can react to behavior signals.

Granular real-time optimization requires data in-housing. But the benefits for brands are clear as more look to align their programmatic spend and their datasets in-house.


Cost efficiency and transparency – data transparency and cost benefits

Some brands see in-housing as a potential problem in terms of data privacy. Taking control of customer data and managing it in-house alongside programmatic media buying can seem daunting, but it is instead an opportunity for brands to take control of the role that data plays in their organization development.

Robust data-centric organizations are looking at in-housing data because it puts them in control of the data and how it is used. This naturally requires an organization to look at its data sets and understand the consent and collection process. In a GDPR world, this is an essential requirement for programmatic buying companies.

Data and programmatic in-housing have also led to an increase in transparency. On the programmatic side, false impressions and the ability to understand exactly what costs are associated with each campaign have led to the rise in-housing.

For data, it is a similar story – transparency allows them to understand and perform their tests on data accuracy.

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Marketing & Advertising

What Is Bidstream Location Data – Why Is It Inaccurate & Imprecise

What is the bidstream?

The bidstream is a network of advertising requests that deliver ads to mobile devices.

A bid request refers to the moment when a publisher auctions off an ad slot to an advertiser. This request delivers an ad to the device.

When the ad is delivered, some information is passed back the other way. This information contains ad related information, but ofter it comes with additional details. These sometimes include a form of location.

This location data is then packaged and used for a wide range of applications.

But sadly this isn’t always a great proposition. Here’s the thing with your bidstream data…


The problem

Geodata is no longer just an experimental solution. Location data is fueling some of the most advanced marketing efforts.

Because of this, marketers are rightfully demanding greater transparency around this data, where it comes from and how it is created.

The problem with bidstream data is that it is often inconclusive, inaccurate, or even fraudulent.

The thing with bid stream is that it can very quickly provide a large amount of scale. Due to the sheer number of devices that display ads, the number of location points can be quite appealing.

However, too many marketers are blinded by this scale and refuse to focus on data quality.

This quality is what provides lasting ROI for marketers and allows for effective targeting, attribution, and insights.


The common pitfalls with bidstream data

General precision issues

Not all bidstream data is inaccurate but the data is often imprecise. What’s the difference? Well, it comes down the detail of the device location.

Some bidstream data is based on the IP address of the device. Sometimes this can cross over an area as large as 1km. In a city, this is not precise enough to understand the context of the device.

bidstream data that is collected in this way doesn’t go far enough to understand the context around device moment. SDK based data, for example, can understand the difference between a device walking past a store and a device visiting a store for a coffee.


Cached IP address

A common issue with bidstream data is that the device often passes back cached location signals. If a device has connected to a network before it can sometimes deliver this cached address, even when the device has moved to a new location.



Bidstream data is often confusing if you sit down and analyze it down to a device level. For example, we’ve seen devices move across the world in a matter of minutes!

This disparity demonstrates the issues that bidstream can present for marketers. The use of a VPN can cause these discrepancies.

These factors mean that bidstream data is unreliable. Some reports have places accuracy levels of bidstream data at less than 10%.

Marketers may be able to get their hands on large quantities of data through the bidstream, but this data has to be rigorously filtered to ensure any level of accuracy. Even then, these levels of accuracy are often unsuitable to carry out the type of campaign that will produce the desired results.


How do we know this?

We know what good data looks like because we deal with it every day.

We’ve spent years building a dedicated SDK that provides anomynized location data from mobile devices.

It’s the product of years of focusing on the inaccuracies involved in device location, and we’ve built many solutions to identify location data that is both precise and accurate.

But here’s the kicker – we thought about scale as well. We released that advertisers needed a way to scale this data to satisfy their marketing goals.

That’s why we worked on deploying our SDK to compete with the scale of bidstream.



Location data comes in many forms, and each has its advantages and disadvantages. Transparency is key, and marketers should understand that the data they use in their campaigns should be rigorously tested for accuracy.

The bidstream can generate large amounts of location data instantly. This data is often inaccurate and imprecise.

SDK driven data collection offers much-needed improvements in data accuracy and allows marketers to execute better campaigns.

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Marketing & Advertising

What Is Business Intelligence & Business Analytics?

For your business to grow, you need to understand which elements are working and which will work in the future.

With the rise of big data businesses now produce a vast amount of data. Making sense of this data is where business intelligence and business analytics comes in.

But what’s the difference and what can each do for you?

We’ll break down both and help you to understand how they can become a crucial tool for your business growth.


Business intelligence vs. business analytics

Business intelligence is often used as a blanket term to describe the approaches and tools that can be used to provide useful insights that can help you to understand how your business operates.

This usually involves data and some kind of analysis to establish trends and understand why things are performing in a certain way.

Business analytics is also about exploring data that is related to your business. The goals are similar – to better understand relative performance and make better-informed decisions that facilitate more significant growth in the future.

BA is slightly different from BI in that while they both address similar problems, BA is the process of using data to predict and draw conclusions. It is also used to predict what will happen in the future.

In this sense, the difference between business intelligence and business analytics is that the first is descriptive and the second is prescriptive.

Business intelligence explains what has happened with your business or what is currently happening. Business analytics is focused more on what will happen in the future, with emphasis on prediction.



So if we look at a dataset that we are familiar with – location.

Location data is a powerful tool in understanding business performance, and it can be used to inform decision making from management to marketing.

Let’s say we were a retail store with online advertising. We wanted to see if the advertising affected store visits or how they had affected an industry such as gig economy apps.

We could use location data to see which devices then entered the store. By matching these devices to those that were exposed to our online advertising campaign we could see the number of devices that were exposed to the advertising and then also visited the store.

This is an example of business intelligence. We’re taking the number of devices that visit the store and have been exposed to our advertising to create a simple conversion rate.

Let’s use the same store to illustrate business analytics.

We created a dataset that consisted of all the devices that visited the store in a monthly period. We used metadata in the Tamoco network that is associated with these devices to get a detailed understanding of the type of consumer that is related to the device.

By association, we now have insights into the type of customer that visits this specific store. This is fueling our business intelligence.

The next step is for us to create a predictive model that helps us to tailor our online advertising to the customers that visit our store. This will allow us to optimize our budget and maximize conversions.

Using this data to predict the type of customer that will visit our store and target advertising accordingly is an example of business intelligence. We are actively using the data to predict and inform future business decisions with the view of optimizing them.


How does this fit into your business

Modern businesses need a solution that can combine both. Location intelligence is one that allows companies to analyze performance and model data to make smarter decisions in the future.

Location is one example of a dataset that can fuel business intelligence and business analytics.

To compete in today’s landscape business need to be able to understand what has happened and what will happen in the future.

This is where it’s essential to get the right data.


Location for BI and BA

Business intelligence with location

Location data can help you to measure KPIs in the offline world such as store visits, and can improve conversion copywriting in real-world locations.

It can also help you to measure behavior anywhere in the real world. Real-time business intelligence solutions can identify population movement, macro trends and other valuable metrics for your business.

Business analytics with location

Large scale device movement data can help to inform a robust business analytics solution. By combining location data sets with other existing data or solutions, it’s possible to predict how your customers will behave.

The use cases for this are incredibly large. Everyday use cases could be city planning, connecting smart vehicles, optimizing advertising and marketing or optimizing the supply chain.

Marketing & Advertising

Lookalike Modeling – The Best Way to Build Lookalike Audiences

Modern marketers are always looking for ways to grow their successful campaigns and reach new audiences. Lookalike modeling is an effective way to identify customer attributes and use these to build new and larger lookalike audiences to expand the reach of marketing activity.

There are several ways to do this and marketers focus on these attributes and behaviors as the core identifiers of their target audience.

But what if there was a better kind of attribute to identify similar audiences. What if this behavioral data was a better indicator of similarity that just having visited the same product page?

And what if these datasets were underutilized in lookalike modeling – allowing you to build more relevant audiences for your campaigns?


The issues with lookalike modeling

Current data on lookalike is, for the most part, a valid way to build lookalikes. But often these datasets are for individuals that look like others in the seed audience.

This might appear obvious but do you want to build your audience based on looks? Wouldn’t it be better to focus on how consumers behave, rather than outdated demographics – such as a page like that occurred years ago?

Well, this is possible when the focus is placed more on act alike audiences, rather than lookalike.


Using location to create behavioral based lookalikes

Act alike audience is better than lookalike modeling because you are using more recent data and you are using data which signifies intent. A great example of this is location data. It’s current and traveling to a specific location is a much better signifier of consumer intent.

Behavioural based lookalike modeling is more effective because you can provide narrowly defined attributes and use this to build new and highly relevant audiences to expand your marketing activity.


Example – the current way

Let’s look an example, in this case, city gym going customers. This is currently how lookalike modeling works:

We take the existing attributes from our data set of ideal target customers. These might have the following traits:

  • Age: 24-49
  • Male 60%
  • Social profile matches sport interests
  • Mobile-focused

Using this information you could quickly build a lookalike audience that had similar characteristics. The problem is that this same audience profile might overlap with men who are merely interested in watching football matches at home.

This is the problem with focusing on what customers look like, rather than what they do and how they behave.


Using location and actions

With action-based lookalike modeling, marketers can rely on dynamic behavior to identify attributes. These attributes can then be used to build more effective lookalike audiences.

Let’s imagine we are still trying to target the same consumers – city going gym goers

We might have customers in our database that exist in our target group but share none of the characteristics discussed above. But they have still converted and carry potential value when building a lookalike audience.

Let’s use location to illustrate this example.

We can identify where the seed lookalike goes and then identify other devices that exhibit similar behaviors.

In this case, we can map our customers, and we can see that a high percentage of them visits both whole foods and a high-end drug store within a three month period.

We can then build a lookalike audience that consists of every other device that enters both of these locations within three months. This can be done anywhere in the world, and we can even use categories of locations (health stores) to make this work in across several different regions.

Our lookalike audience, in this case, would contain people that were demographically different from our customers. They wouldn’t necessarily look like our audience, but they would behave like our customers.

This can ever be extended to build new audiences based on visits to you or your competitor’s real-world locations, which means that you can create competitive lookalikes based on your competitor’s customers.


A better way – that can also be combined with your current lookalike modeling

Of course, these attributes can be combined with your current lookalike modeling. A good balance between demographic info and behavioral data is more likely to identify customers that will improve your lead generation.

With the rise of DMP solutions that now have ready to activate location data, it’s the perfect time to use behavior as a building block for lookalike audiences.

Moving to a behavioral-based advertising model with less weight placed on demographics marketers can build more effective audiences and maximize their KPIs.


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Marketing & Advertising

What Is Lookalike Modeling? All You Need To Know in 2021

One significant challenge marketers face is how they can grow their audiences once they want to achieve scale.

Growing targets will always mean that marketers need to reach more people. The problem that marketers encounter is how to grow these audiences while keeping them relevant to their product or proposition.

Expanding your audience beyond your current database is crucial to achieving future growth. What digital tools for marketers are there to reach new audiences? How can you ensure that a bigger audience doesn’t mean fewer conversions and less relevant consumers?


What is lookalike modeling?

This is where lookalike modeling comes in. Marketers need to find new customers and ensure that these new audiences are relevant to their businesses goals.

Lookalike modeling is the process of identifying new customers that look and behave like your current audience.

It involves taking a seed audience and defining key characteristics which differentiate these. From here smart modeling and other processes will help to identify a new larger, audience that is similar to your current customers.


What do you need to start building lookalike audiences?

As with many forms of digital advertising, lookalike modeling works using data. Data comes in many forms, and it’s really up to you to decide on which datasets are the most effective at identifying your target customer.

The most successful lookalike audiences are based on unique first-party data. This needs to encompass a range of first, second and third party datasets that cover both online and offline behavior.

That’s an awful lot of data to process, notwithstanding the process of collecting processing and managing that comes along with it. Luckily there are several solutions to help.

DMP for lookalike audiences

This data is combined with a program that can quickly identify other consumers who exhibit similar behavior. This process usually occurs inside a DMP (data management platform). It can also be done in some demand-side platforms (DSP) as well as in house.

in a little box – a Data management platform is a tool that aggregated and unifies data from many different sources to create a clear, holistic view of your data.


How does lookalike modeling work?

If that sounds slightly complicated, do not worry. Lookalike modeling is simple as long as you have the right dataset to work from.


Choosing datasets

First party, second party, third party, online, offline CRM, purchase, location – data comes in many different forms and comes from many different places.

You need to pull these datasets into a single place to maximize the effectiveness of your lookalike audiences.

This data is essential to get right. The more information you have, the more likely you are to build a better lookalike audience.


Define attributes

Next up you’ll need to identify the attributes or behaviors that identify your most valuable customers.

This will look different depending on the type of data sets you’re using. You can combine attributes from different datasets to create more specific seed audiences.

The more specific your look-alike model, the more likely you will find your target audiences. The stricter your seed audience, the more likely it will help you to realize your goals.

Of course, this will affect the size of your lookalike audiences. The more attributes you select, the more likely you are to filter out potential customers.

Ultimately it depends on the goals of your campaigns and what you want to achieve by building lookalike audiences. If you need to target specific people with a high-value proposition, then it might make sense to use more narrowly defined behaviors.

However, if you are looking to focus on reach and awareness then being less strict with your attributes will generate a larger audience that will most likely drive more awareness.


Some examples of datasets and attributes

Location-based lookalike audience

Purchase data

frequency and amount

Browsing history

Interest in specific products


Building the lookalike audience

This is done in the DMP or DSP and will look slightly different depending on the type that you use.

For external lookalikes, this might be done via a third party. For example, location-based lookalikes will usually be done by the provider.

The process is similar depending on where it occurs and look like the following.


  1. Analyze the seed audience
  2. Apply algorithms to find profiles that match
  3. The result is a lookalike audience


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What can you use lookalikes for?

The primary use for lookalike modeling is to find new prospects for your business.

Building lookalike audiences allow marketers to scale their campaigns to relevant consumers. With the instant reach available to marketers via digital targeting platforms, lookalike modeling can instantly help a business scale their key metrics and improve their bottom line.

Lookalike targeting can also help to extend the reach of specific campaigns. All campaigns eventually run dry, no matter how effective they are. Using lookalike audiences, these high performing campaigns can be extended to reach new audiences that will hopefully have a similar level of conversion.

Audience modeling is part of every successful media buying strategy. All media buyers should be aware of how lookalikes work in order to make informed decisions concerning their ad campaigns.


Best practices to build lookalike audiences

  • Find the line between reach and conversion – you need to focus on the number of attributes that you select. Too many might reduce the reach of your lookalike. Too few and your lookalike audience will not be closely related to your seed audience to produce the desired results.
  • The more data, the better the lookalike modeling will be
  • Think about new datasets that your competitors aren’t using. This will give you an advantage and allow you to build better lookalike audiences.
Marketing & Advertising

1st Party Data, 2nd Party Data, 3rd Party Data – What’s The Difference?



Data is changing everything. Data is everywhere. Over 2.5 quintillion bytes of data are created every single day, and it’s only going to grow from there. By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth.

For marketers, this is an awful lot of data to keep in check. It’s easy to be overwhelmed by the amount of information available. Even more so when we hear terms like 1st party data, 2nd party data, and 3rd party data.

To help make this easier there are several ways of classifying data. This helps us as marketers and advertisers to understand the relationship between datasets and understand what each dataset can be used for.


Why is this important?

Data is one of the most effective tools to drive successful marketing. Marketers that can make sense of the data at their disposal and deploy it successfully have demonstrated the rewards that come with it.

Depending on your goals different kinds of data will be more relevant to you. That’s where data classifications such as 1st, 2nd, and 3rd party come in.

Let’s look at what these terms mean and how each type is relevant for your marketing.


1st party data

First party data is data that you have collected directly from your audiences or your customers. It includes

  • Data from your CRM
  • Behavioral data collected from interaction with your business (website, app, stores)
  • Data around subscriptions
  • Data generated from your social media accounts

This data is generated directly from your customers or your audiences. This data set is genuinely regarded as the most valuable as you are aware of the method of collection and it is generally free or costs little to attain.

First party data is easy to collect and manage in solutions like CRMs and DMPs.

As 1st party data is collected by you directly, any privacy issues are minimal (assuming you are following the correct procedures!). You own your data directly, and you know exactly from where it came.

First party data is extremely valuable as it provides valuable insights around customers and audiences for little to nothing. Companies that aren’t collecting and activating first-party data are missing a trick.


What you can do with first party data

For organizations taking control of first-party data collection should be an immediate priority. Because you have complete control fo the data, it is of higher quality. There are many different uses for 1st party data from monetization to engagement.


Engagement, personalization

Your first party data can help you to personalize your marketing and help you to engage with your customers.

1st party data can effectively segment your audience and allow you to create more specific, and personalized ads for your customers.


First party data can be invaluable in helping you to understand your customers. You can identify and map out the customer journey or see how your users behave and interact with your business or product.



First party data is highly monetizable as you can demonstrate the methodology of data collection. The data carries maximum revenue as you didn’t have to purchase the data. First party data monetization can help to generate revenue to fund other areas of your business.


2nd party data

Second-party data is a term that is making more of an appearance these days. We hear a lot of people asking “what is second party 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.

You can purchase 2nd party data straight from the provider – there’s no middle party in these instances. This allows you to form a relationship with the provider and understand the value in the data.

The data is usually collected from the same sources as your first-party data. However, some specialist 2nd party providers provide unique datasets that offer new insights.

These come from behavior outside of your audience, so it’s likely to include potential new customers.


What can you do with second party data

Second-party data is a relatively new concept, but it carries enormous potential for marketers. Because it comes directly from the partner and because you will likely have a direct relationship with the company it’s easier to verify the accuracy of these data sets.

The data is more consistent and will be more precise than a bunch of aggregated third-party data sets. 2nd party data offers more transparency for the end data user, making it easier for you to understand the value that the data can bring to your business.


New audiences, new business

Second party data is great for prospecting and reaching new audiences that aren’t already a part of your audience.

This allows you to expand into new regions or new demographics with new products.



Second-party data can help you to fill the gaps in your existing 1st party data sets. Your datasets might be high quality but might not be large enough to scale your business in the way that you want.

Supplementing 1st party data with 2nd party data is an effective way to make your campaigns reach more people without compromising on quality.


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3rd party data

Third party data is data that is purchased from outside sources where the seller is not the direct collector of the data.

These data sets are usually aggregated and could have been resold multiple times. These aggregators pay other businesses that generate the data and collect it into a single dataset.

This large dataset is then broken down based on demographics, behavior or another characteristic. This allows the data to be split into segments that are easily resold.

Third party data is often bought programmatically – this means that it happened quickly and on a large scale. This means businesses can purchase large amounts of data for their campaigns quickly. The downside to 3rd party data is that it’s much harder to verify where the data came from and how it was collected.


What you can do with third-party data

Third party data accuracy is hard to verify, and its availability is public, so that means that other companies (and potentially competitors) will be using the same datasets as you.

But that doesn’t mean that it carries no value for marketers. It can be useful when it’s appropriately combined with your first-party data.


Expand your audience

Combining third-party datasets with your first party data can help you grow your addressable audiences. Lookalike modeling can identify characteristics in your current dataset and look for similarities in third-party data to find similar prospects.


Tips for marketers and advertisers

Third-party data has been prevalent in many marketing campaigns in recent history. The vast amount of data that is available along with the results it could potentially generate have made it a valuable asset when combined with your first-party data.

However, two significant developments have occurred which have made second party data a much more reliable option for marketers.



Unless you have had your head in a ditch for the last few years, you’ll be well aware of the GDPR in Europe. Privacy concerns in third-party datasets are always present for marketers.

Often third-party datasets are hard to verify in terms of consent. This is where a direct relationship with a second party data provider is useful. You can verify the consent process and ensure that the data is collected in accordance with the relevant privacy standards.


Accuracy and transparency

The truth is that your first party data is more transparent than any other kind of data set. Hover that doesn’t make it accurate. Third party data is difficult to verify in terms of accuracy and often transparency isn’t even part of the discussion as it’s an aggregated dataset.

Second-party data is as good as first-party data as long as you can identify the methodology and verify that it meets your accuracy standards. This is again why a direct relationship with your data providers is a useful situation to be in.

Many second party data providers can provide their data directly into your data management solutions. SO where your first party data sets aren’t enough, you can purchase new data from reliable and transparent providers directly in your existing infrastructure.

Data is here to stay. As a marketer, it’s your job to ensure that your data is activated and that your second and third party data is accurate and transparent.