Author: James Ewen

James is the head of marketing at Tamoco

Categories
Apps

Best Guide To Mobile App Monetization 2023 – 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 with various app monetization models.

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.

It will help custom software development services define how to monetize their product properly. Choosing the right pricing model means finding the balance between value and revenue, which is an essential task for establishing a successful and profitable business

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 and implementing affiliate tracking.

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 buying Bitcoins) 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. You could also try something such as a pre recorded live stream to do this.

 

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. Apart from focusing on the app experience, the focus should also be placed on providing application support. This would help in ensuring that there is complete and uninterrupted functioning which will in turn help in improving feedback and retaining the consumer.

 

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](http://iosdevweekly.com/](http://iosdevweekly.com/) – 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.

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Marketing & Advertising

Programmatic In-housing – What About Programmatic Data?

in-housing programmatic

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 DSP platforms 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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Business

The Impact of the Internet of Things on Business: Benefits and Drawbacks

The Internet of Things technology has changed many industries, including logistics, healthcare, retail, and even communications. Nowadays, IoT enhances business processes and software solutions with sensors, devices, and platforms.

Gathering information allows companies to make smart decisions and build more efficient business processes. The Internet of Things is more than simple data collection, this powerful technology also brings Artificial Intelligence, Robotic Process Automation (RPA), and detailed analytics into business everyday life.

In this guide, you can find out how the Internet of Things affected businesses and optimized their inner processes while supporting employees. Additionally, we have covered the advantages and disadvantages of this technology for the business. 

What is IoT in Business?

Let’s start with the basics — the Internet of Things was created to connect physical devices and objects through the Internet. They can exchange information and communicate with each other. 

There are a bunch of IoT devices on the market:

  • smart home appliances (e.g., thermostats, lighting systems, security cameras);
  • wearables (e.g., fitness trackers, smartwatches);
  • industrial sensors (e.g., temperature sensors);
  • medical devices (e.g., pacemakers, insulin pumps).

The number of current IoT devices (7 billion) is quite impressive, but what’s more interesting — the figure is expected to grow by over 3x to 25.44 billion total IoT devices by 2030.

As these devices increasingly rely on sophisticated Printed Circuit Boards (PCBs), understanding the differences in PCB assembly methods, such as SMT vs THT in PCB Assembly, becomes essential. For instance, the choice between Surface Mount Technology (SMT) and Through-Hole Technology (THT) can significantly impact device performance, reliability, and manufacturing costs. By optimizing the PCB assembly method used, companies can enhance their IoT devices’ overall effectiveness while managing expenses. This is crucial since PCBs serve as the hardware foundation for IoT devices, providing the necessary infrastructure to connect and integrate various electronic components such as sensors, microcontrollers, and communication modules that enable data exchange and functionality.

Undoubtedly, IoT for business is used to optimize and automate a lot of time-consuming or repetitive processes, increase efficiency, and improve customer experience.

Let’s discuss an example — an IoT-based supply chain. 

IoT devices (temperature and humidity sensors, GPS trackers, etc.) collect information in real time about the goods a company stores or transports. It’s also possible to check inventory level and shipment status, environmental conditions in warehouses and vehicles, and other data. 

In case of any change, the system prompts the necessary reaction. For example, if a sensor detects deviations from the picked temperature, the app sends a command to adjust the temperature and notifies the supply chain operators. 

The center of any IoT-based system is a server that manages communication between devices. For example, if you use the MQTT messaging protocol, the server is referred to as an MQTT broker.

So, the question arises — what is an MQTT broker? Simply saying, it’s a central hub where all the connected devices communicate. It allows devices to send and receive messages. 

There are a lot of other opportunities to use IoT for business. This technology can monitor the performance of machines in a factory, track the good’s movements, or collect information about customer behavior in a retail environment. As a result, companies can better analyze information and make data-driven decisions.

The Impact of IoT on Businesses

We’ve already mentioned the significant contribution of the IoT in various industries. Let’s move on to the impact of the Internet of Things on businesses. 

Below you can find the list of changes IoT has already brought to many companies:

  • Cloud-based solutions

Cloud technologies ensure reliability and stability for IoT operations. Due to the popularity of IoT devices, cloud development has also increased. Companies need to create powerful cloud-based solutions to store, process, and manage information from the IoT system.

  • Efficiency improvements

IoT devices perform a lot of time-consuming and routine tasks. As a result, workers and employees have more time to focus on crucial or more creative tasks. Additionally, with IoT devices, companies can analyze and optimize different business parts and streamline their business processes.

  • Better decision-making

Based on the collected information, IoT systems can identify customers’ behavior patterns and insights. Consequently, companies make more informed and effective decisions. IoT devices can even make autonomous decisions based on predefined rules and algorithms. For example, IoT systems can autonomously adjust production parameters based on real-time data, optimizing efficiency and minimizing errors.

  • High customer experience

The Internet of Things for businesses can be used to collect information about customers and their behavior. Companies analyze and optimize different areas like goods demand prediction and customer recommendations. It leads to improved customer experience and increased satisfaction rates.

As you can see, the impact of IoT devices on business is quite positive and enable greater efficiency. However, it’s also important to consider the risks of implementing the Internet of Things into your business. 

How to Integrate IoT for Business: Challenges and Opportunities

The Internet of Things technology has benefits and drawbacks. Each company should carefully consider each of them. To give you a hint, below is a list of the most widespread challenges and opportunities you can face integrating IoT systems. 

IoT Business Challenges To Consider

  • Security

The primary challenge of IoT is security and data privacy. Devices tend to be vulnerable to security threats when they transmit sensitive information. Businesses have to invest in data protection mechanisms, including encryption, authentication, access control, and regular security updates.  

  • Interoperability

There are a variety of IoT devices on the market that were created by different manufacturers and use various communication protocols. Since IoT devices need to work as one single system, it can be complicated to integrate and set up devices. Companies have to spend money on technology platforms and standards that enable seamless communication between devices.

  • Scalability

Businesses integrate an IoT system and need to continue scaling their systems. However, the scaling progress can be a struggle due to monolithic architecture and unsuitable platforms. It’s important to take care of the system’s scalability before implementing it. Take into consideration the future business growth and the vast amount of information generated by IoT devices. 

IoT Business Opportunities to Take into Account

On the other hand, the impact of the Internet of Things on business can bring several significant benefits.

  • Cost reduction

IoT helps companies reduce costs and optimize their internal processes. The system monitors and analyses information on energy consumption, equipment performance, and more. So, businesses can predict usage and optimize their resources. 

  • Data collection

IoT systems can process, store, manage, and analyze a vast amount of information. The system automatically creates reports on device conditions allowing employees to save time, streamline their work, and reduce manual intervention. 

  • Environmental sustainability

Last but not least, IoT allows for monitoring and controlling energy consumption. It allows companies to optimize their supply chains and transportation routes. The Internet of Things technology helps organizations reduce their carbon footprint and promote sustainable practices. Resource management becomes a crucial factor these days. IoT devices can show pollution levels, water quality, and other environmental factors. 

To sum up, the impact of IoT on business is significant. This technology has already brought a lot of positive changes to companies in different industries — from logistics to healthcare. 

The Internet of Things technology has the potential to transform business processes by enabling automation and optimization.

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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.

 

Teleporting

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.

 

Conclusions

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.

 

Examples

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.

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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.
Categories
Business

Vue v React – Which is best?

One of the most active subfields in software development is front-end development. It might be challenging to keep up with all of its new trends and features since it has evolved so rapidly. The two most well-liked frontend frameworks right now are Vue and React, and it’s easy to see why: they both provide a wonderful developer experience, performance, and a sizable ecosystem to support your development efforts.

 

The popularity of Vue.js, a JavaScript toolkit for building dynamic web apps, has increased significantly, and both big and small enterprises are using it to build many projects. One of the most popular alternatives to Vue.js is React.js, a JavaScript framework for designing user interfaces.

What Exactly Is Vue?

A progressive JavaScript framework called vue.js is used to build single-page applications and user interfaces. The usage of Option API, which enables newcomers to learn more rapidly, makes it feasible for this framework to have a short learning curve, which has become something of a hallmark.

 

Vue.js was developed by Evan You, who previously worked at Google on various intriguing and interactive innovative projects, as a means to take the finest elements of Angular and construct a unique tool around them. Vue JS development services knowing all the benefits get the job done quickly, but you need to know other options like React.

 

The most recent version of Vue, Vue3, is lighter and quicker than earlier versions. It also has better TypeScript support and many other enhancements, including changes to the composition API and the global mounting/configuration API.

Using Vue.js

Vue is becoming more and more well-known, and well-known firms like Adobe, Alibaba, GitLab, WizzAir, Netflix, and even NASA utilize it for their projects. One of the few JavaScript frameworks available today that has achieved this degree of ubiquity is Vue.

What exactly is React?

In 2013, Facebook developed React, a JavaScript toolkit for creating user interfaces. It was first used to create the Facebook website, and since then, it has grown to be one of the most well-liked JavaScript frameworks for creating online applications.

 

Developers may use React to use declarative programming, which is simpler to understand and debug. It also makes use of a virtual DOM, which performs better and is quicker than the conventional DOM.

 

With React’s component-based approach, developers can easily and quickly create robust applications from reusable components. As a result, it works well for both applications that are ready for production and quick prototyping.

Using React

Companies that employ React for their projects include Facebook, Instagram, WhatsApp, PayPal, Yahoo!, Khan Academy, and even Barack Obama’s website. As you can see, React has already cemented its position as the go-to tool for creating high-performance web applications quickly.

React vs. Vue: Differences

Popular JavaScript frameworks for creating dynamic user interfaces include Vue and React. While there are some similarities between the two frameworks, there are also some significant differences:

Templating

While React makes use of JSX, a JavaScript syntax extension, Vue uses HTML-based templates. While JSX offers greater flexibility and control over the rendering process, Vue’s template syntax is generally thought to be more beginner-friendly.

Size

React is often thought seen as being larger and heavier than Vue. Smaller projects may be built more quickly and easily as a result, while bigger projects can benefit from React’s greater scalability and flexibility due to its larger size.

Tate Administration 

It is simple to manage and communicate data across components because of Vue’s integrated state management mechanism, Vuex. React, on the other hand, uses third-party frameworks for state management, such as Redux or MobX.

Growth Curve

Due to its more straightforward API and more comprehensible documentation, Vue is typically regarded as being simpler to learn and use. The learning curve for React is higher, its documentation is less clear and its API is more complicated.

Community Assistance 

With more libraries and resources accessible, React has a more extensive and vibrant community. The developer community for Vue is tiny but expanding, and there are many enthusiastic and committed members.

Tooling

Both frameworks provide a variety of strong debugging and development tools, but React’s tooling is usually regarded as being better developed and reliable.

Conclusion

As you can see, both technologies are valuable resources for creating client-side code for contemporary online applications. Nevertheless, despite their similarities, how each of them is used depends on the scope of your project, your company’s needs, and other particulars of your situation. 

 

You should be able to distinguish clearly between Vue and React after reading this post, which will make it easier for you to choose the appropriate framework. 

 

Categories
Business

Creating a Marketing Trailer Video: 7 Top Video Editing Software to Try

‍Introduction to Making Marketing Trailer Videos

Businesses always need to find creative ways to capture the attention of potential customers and one effective method is through the use of marketing trailer videos. These short, captivating videos are designed to grab the viewer’s attention and provide a quick overview of a product or service. They can be used on websites, social media platforms, email campaigns, and more.

Video editing is a must-have skill nowadays. The process of creating a marketing trailer video involves combining various elements like images, animations, text, and sound effects, and others. It requires a good understanding of storytelling, design principles, and video editing techniques. With the right tools and skills, businesses can create marketing trailer videos that leave a lasting impression on their target audience.

In addition to marketing videos, these skills are highly valuable for content creators across platforms. For instance, if you’re aiming to attract more Twitch viewers, mastering video editing can significantly enhance the quality and appeal of your streams and highlight reels, drawing in a larger audience and boosting engagement.

This article will discuss the top 7 video editing software options available for creating captivating marketing videos

Key Elements of an Effective Marketing Trailer Video

A well-crafted marketing video helps to promote your business. It consists of several key elements that work together to convey the intended message and evoke the desired emotions from viewers. These elements include:

  • Attention-grabbing visuals: High-quality images, animations, and footage are crucial for creating a visually appealing trailer. You should carefully choose them and design to support the message and capture the viewer’s attention within the first few seconds.
  • Clear and concise messaging: The text and voiceovers in a marketing trailer should be brief, direct, and easy to understand. They should convey the main selling points of the product or service without overwhelming the viewer with too much information.
  • Emotionally engaging soundtrack: The right music and sound effects can greatly enhance the emotional impact of a marketing trailer. They can help create anticipation, excitement, or other desired emotions that align with the brand’s identity.
  • Call-to-action: A strong CTA is essential for driving the desired action from viewers, whether it’s visiting a website, signing up for a newsletter, or making a purchase. It should be clear, visible, and persuasive.
  • Consistent branding: The marketing trailer should maintain a consistent look and feel with the brand’s overall visual identity. This includes the use of colors, fonts, and design elements that are easily identifiable with the brand.

Factors to Consider When Choosing a Trailer Maker

When selecting a movie trailer maker to create a marketing trailer, consider the following factors:

  • Ease of use: The software should be user-friendly and easy to navigate, even for beginners.
  • Features and functionality: The trailer maker should offer a wide range of video editing tools and features, such as templates, effects, and transitions to help create a professional-looking marketing trailer video.
  • Compatibility: The software should be compatible with your computer’s operating system and support various file formats for importing and exporting.
  • Price: Compare the costs of different movie trailer makers, considering the features and capabilities they offer. Some software options are available for free, while others require a one-time purchase or a subscription.
  • Customer support: Look for a movie trailer maker that offers reliable customer support through various channels like email, live chat, etc.

Top 7 Video Editing Software for Creating Marketing Trailer Videos

VSDC Video Editor

 

Compatibility: Windows

VSDC Video Editor is a versatile movie trailer maker that offers a wide range of editing tools and features. It supports various video formats and allows users to create marketing trailers with ease. 

The software is designed for both beginners and experienced users, with a user-friendly interface and a comprehensive set of features for creating professional-looking trailers. 

Key features

  • Non-linear video editing
  • Masking, AI filters, color blending, chroma keying
  • Color correction, charts, zoom

Pros

  • Free 
  • Supports all popular formats
  • Good collection of video effects, sounds, and transitions
  • Export in HD and 4K
  • Share directly to social networks

Cons

  • Clunky interface
  • Lacks advanced features like multicam support and motion tracking

Price: Free, $19 for Pro

Sony Vegas Pro

 

Compatibility: Windows

Sony Vegas Pro provides a complete set of tools for creating high-quality marketing trailers. With its advanced features like motion tracking, video stabilization, and color grading, users can create visually stunning and engaging trailers. 

The professional video editor also offers a wide range of pre-built templates, effects, and transitions to help streamline the editing process. Although it is a bit pricier than some other movie trailer makers, its advanced capabilities make it a popular choice among professionals.

Key features

  • Scene detection, keyframing
  • HDR color correction, chroma key
  • Great library of effects, filters, transitions, and titles

Pros

  • Customizable interface
  • Fast render
  • Many export options

Cons

  • A steep learning curve, no tutorials provided
  • It may be expensive for some users

Price: 30-day free trial, $19.99​/month, $399 for a new license

Cyberlink PowerDirector 365

 

Compatibility: Windows, macOS

Cyberlink PowerDirector 365 is PC and Mac video editing software that comes with an intuitive interface and drag-and-drop functionality. It includes a large library of templates for intros and outros, effects, and transitions, as well as support for 4K and 360-degree video editing. 

Key features

  • 3D and 360-degree editing
  • Text overlays, transitions, animations
  • Green screen effect, object tracking, video stabilization

Pros

  • Easy to use
  • Built-in library of graphics, music, and plugins
  • Plenty of video effects
  • Supports 4K

Cons

  • The interface may be overwhelming
  • Unexpected bugs for some users
  • Expensive compared to others

Price: 30-day free trial, $69.99/year

Movie Trailer Maker Online by Movavi

 

Compatibility: web-based

 

This online trailer video maker is web-based that allows users to create videos without the need to install any software. Whether it’s a blockbuster movie trailer or a product video, you create highly attractive and professional video with just a few clicks. 

Whether utilizing a ready-made template or crafting a custom trailer, we seamlessly blend text, voice-overs, titles, music, sound effects, and visuals to create captivating marketing videos. With the aid of text to voice tools, we ensure grammatically correct voice-overs, enhancing the viewer’s experience without incurring significant expenses.

Key features

  • Basic video editing options
  • Transitions, customizable titles, sound effects, and soundtracks

Pros

  • Online and free
  • Fast and quality video editing
  • Upload videos from your computer or use Dropbox
  • Export to YouTube

Cons

  • Lacks advanced features

Price: Free

Corel VideoStudio Pro

 

Compatibility: Windows

Corel VideoStudio Pro is a feature-rich movie trailer maker. You can enjoy switching between hundreds of effects, transitions, titles, and animated AR stickers. There is also a wide range of face effects, color adjustment tools, and 500+ music tracks to explore. 

Key features

  • Basic editing like trimming, cropping, resizing, etc.
  • Multicam and split screen editing
  • Color correction, animated stickers, face effects, lens correction

Pros

  • Drag-and-drop interface
  • Supports popular video formats
  • Vast variety of color adjustment tools
  • Plenty of transitions, text styles, music tracks, and FX effects

Cons

  • Some users experience crash issues if importing large files
  • Blurry rendering for some users

Price: 30-day free trial, a one-time payment for $79.99

Crisp Video

 

Compatibility: web-based

Crisp Video is a video production company that helps businesses create stunning trailers and videos for marketing. The team of experts can help you come up with a concept, storyboard, and script as well as create the finished video by adding all the necessary elements like effects, soundtracks, animations, titles, etc.

Key features

  • Concept creation, script writing
  • Effects, music, transitions, titles

Pros

  • Keep the client’s requirements in mind
  • Smooth and high-quality video production
  • Help increase brand awareness

Cons

  • You can’t work on the project on your own

Price: Custom pricing

Windows Movie Maker

 

Compatibility: Windows

Windows Movie Maker is free and easy-to-use video editing software. It offers basic video editing tools and some effects, transitions, text, music, and overlays to enhance your content. The software also has presets for different types of videos and supports different plugins to extend its functionality. 

Key features

  • Cut, crop, join, and rotate clips
  • A library of titles, text, transitions, effects, and more.

Pros

  • Free
  • Pre-made templates
  • Fast and easy to use
  • Quick share to YouTube, Facebook, and other platforms

Cons

  • Lacks advanced features 
  • No color correction and visual effects adjustment

Price: Free

Wrapping Up

Creating a captivating marketing trailer video is an essential skill for businesses looking to engage their target audience and promote their products or services. By considering the key elements of an effective trailer and selecting the right movie trailer maker, you can create a marketing video that leaves a lasting impression on your viewers.

 

Categories
Marketing & Advertising

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

Introduction

 

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.

Insights

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.

 

Monetization

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.

 

Scale-up

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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Find more powerful 2nd party data

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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.

 

Privacy

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.