Learn how AI can dramatically change your app monetization strategy.
What are the primary AI-powered monetization models currently available for mobile apps?
It’s not outrageous to say that our modern lives depend on smartphones and apps. Android or iOS, apps are our tools for entertainment, work, and everything in between. That’s why there’s a massive demand for them.
But, there’s also huge competition. So, you need to be innovative in how you develop and monetize your app. What better way than to use AI for app monetization?
While there are numerous strategies and models to help make money off of your app, there are three that dovetail particularly well with AI.
In-app Advertisement. In this model, businesses pay you each time you display their ads. There is no cap on how much money an app can make from ads; the amount just keeps going up as more people download and use it. If the app is interesting enough to keep people using it, they may also be ready to watch clips all the way through. Offering a prize at the end of the clip is another technique to keep viewers engaged.
Subscriptions. Unlike running ads, which pay you instantly, subscriptions allow you to earn money over time, and sometimes on a recurrent basis.
By giving people a free trial time with the paid features, it’s easy to get them to sign up for a monthly plan. This gives them a chance to see what your app is all about and decide if paying the monthly fee is worth it. It’s also a good idea to give people choices within the plans, so if someone wants a cheaper deal, they can get it.
In-app purchases. This model is based on the free products or services you provide under a basic plan. Users have to spend money for the premium features and services if they want to acquire an upgrade. So, even if you give away the app’s basic features for free, the money you make from in-app sales will cover your costs.
Let’s explore the role of AI in all of this and how to monetize an app with AI.
How AI optimizes in-app advertising to increase revenue without diminishing user experience
Artificial intelligence (AI) can replace standard in-app ads with native ads that fit in with the app’s content and users’ interests. Showing useful and non-intrusive ads is the way to make money through marketing partnerships and improve the user experience.
This requires several elements:
- Fitting content. Based on user behavior, preferences, and contextual facts, AI algorithms show ads that are more relevant to each user’s interests. By knowing what each person likes, AI can suggest relatable ads, making users more open to engagement and purchases while reducing ad fatigue.
- Smart ad placement. AI automatically figures out where to put native ads in the app’s interface so that they work best. AI figures out the best places to put ads by looking at how people interact with and consume content. This way, ads don’t get in the way and blend seamlessly into the user’s experience.
- Real-time tracking. AI keeps an eye on how well native ads are doing all the time, keeping track of things like click-through rates, sales, and user feedback. Using this information, AI tools improve ad placement strategies to make them more effective and bring in more revenue.
AI enhancing subscription models via personalized offers and dynamic pricing
So, beyond turning in-app ads into a seamless conversion engine, what else can AI do? How does it improve the subscription model?
Targeted offers. AI is powerful because it can look through huge amounts of data to find patterns and trends. Developers of mobile apps may use this feature to better target their ads.
AI makes it more likely that people will click on ads and buy things by showing them ads that are related to their hobbies. This targeted strategy helps app developers make the most money from ads and gives marketers a better return on their investment.
Real-time price analysis. AI makes it possible for pricing models to change based on real-time market demand, user interaction, and the prices of competitors. Algorithms constantly look at data to change subscription prices in a way that maximizes revenue and user satisfaction.
By using this dynamic pricing approach, app developers can draw in users who are prepared to pay a premium and keep price-sensitive clients by giving discounts. So, AI-driven dynamic pricing increases income streams, improves user retention, and supports a long-term subscription model.
How AI uses predictive analytics to forecast revenue and identify the most profitable user segments
AI sifts through mountains of data on app use, including engagement, in-app purchases, and ad profits, to give developers a full picture of revenue sources.
It does so using:
ML algorithms & real-time forecasting. With the help of sophisticated machine learning algorithms, AI can extract meaningful relationships from datasets, helping make accurate revenue projections based on the latest market trends and user activities.
To keep projections accurate over time, AI-driven revenue forecasting constantly adjusts to changing user behaviors and market circumstances. This flexibility lets developers find the best ways to make money and use their resources so that the app can keep growing and making money.
Predictive analytics & segmentation. AI looks closely at how users interact with the app, finding trends in things like how often they use it, how they use its features, and what they’ve bought in the past.
The next step is for AI to prioritize high-value user segments by predicting their lifetime value and spending propensity based on behavior patterns. Again, ML algorithms play a big part here.
Over time, AI breaks down users into segments based on real-time data, changing the parameters of each group in real time to reflect user activities and preferences.
This way, developers know where to spend most of their ad budget and how to tailor the user experience to get the highest ROI.
How can AI improve user engagement and retention to sustain monetization?
We’ve already touched on how AI can help boost engagement and retention rates. But, it’s better to dig a little deeper. Besides predictive analytics, AI uses two other elements to keep users happy and engaged for a long time:
- Smart recommendations. AI algorithms recommend information, goods, and services to app users based on their past actions, feedback, and reviews. App users can find fresh and fascinating content using recommendation algorithms, which in turn makes them happy and devoted app users.
- Sentiment analysis. When app users type, speak, or make facial movements, these AI systems figure out how they feel and what they think. With sentiment analysis, you learn how users perceive your app and make adjustments to the design and features that users find most useful.
AI creates innovative revenue streams for mobile apps beyond ads and subscriptions
So, now you know how AI can enhance the current monetization strategies. But, can it open up more revenue streams for mobile apps? Yes, and here are some:
- Data monetization. Thanks to AI-powered analytics, apps can make money off of user data in a responsible way by giving third parties useful data. This could include data sets that have been anonymous for market research or focused ads, which is a good non-intrusive revenue stream.
- Gamification. Artificial intelligence improves the efficiency of app gamification features. Incorporating these functionalities strategically (like offering rewards for referrals) allows developers to generate extra cash while encouraging user engagement.
- Virtual economy. With the help of AI, app developers can build virtual marketplaces where users can buy digital products, unique content, or experiences. These purchases improve user engagement and provide new ways to make money.
AI can also tailor subscription levels to each user’s preferences, giving them a wide range of features and benefits that meet their needs. This customization makes subscriptions seem more valuable, which makes users more likely to choose more expensive plans.
💡 What are the potential challenges of integrating AI into mobile app monetization? Read on TRIARE blog.
