In mobile gaming, marketing expenses are strongly correlated with the number of newly acquired customers. Simple rule: the more users you want to retain – more marketing expenses you need to bare. Data science consulting, performance marketing or customer acquisition departments in mobile gaming industry trying to optimise marketing expense, keep existing customers (gamers), and monetize available data. Below we will try to explain how to use machine learning for finance ( LTV, Churn and Fraud ) and also in mobile gaming industry for mobile game monetization.
The matter is not as simple as it seems. You need to analyze huge amount of Big Data sets on your customers, specify the best ones, find those who plan to churn or leave the game and create a personalized offer (for each individually). The problem is that if you manually analyze each user, it will take a lot of time. Mobile gaming companies gather hundreds of thousands of records on users in-app activities. The following question arises: how to deal with this challenges to monetize on such Big Data? and how to use machine learning in mobile gaming?
Mobile Game Monetization using Big Data
In the Era of data-driven organizations, every business tries to collect all available data into their data lakes. In most cases collected data is about our customers and their activity. From mobile games app you could collect data on all customers’ in-game activity. Data point that are collected – progress, likes, interactions, preferences, expenses, demographics, behavior patterns and many other data points. That kind of information is often used to extract insights in gaming industry.
That’s where the magic begins – monetization of the data. Among the huge amount of transactional data, find the most important factors, dependencies, correlations and create intelligent algorithms.
You may also find it interesting – Big Data in Mobile Gaming.
Use Machine Learning for prediction in mobile gaming and game monetization
How to automatically perform tasks such as intelligent analysis of each user? This is done by Artificial Intelligence (AI) algorithms, which automatically extracts patterns in the data and applies them to millions of users.
Nowadays, artificial intelligence algorithms speed up the data monetization process by a quick analysis of millions of customers and finding rules that can be used for prediction. Also, these algorithms are cyclically learning on new data and adapting to current behavior patterns by computers – this process is called machine learning in marketing.
The most commonly used solutions for mobile game monetization are:
Identifying fraud in player acquisition
Mobile gambling companies pay for new players, but unfortunately they also often pay for bots. Smaller advertising companies are sometimes using gaming bots that continuously download your mobile games but will never play or pay. Also bots can take action in the game to pretend that they are live users. To gain only qualitative customers, you must minimize the harm of fraud. The networks are paying a fee to anyone who brings them an install. These smaller advertising groups are sometimes using bots to download games over and over. No one from those bot users will ever play or pay. Identifying these bots using machine learning will help you significantly reduce customer acquisition costs and get only good quality customers.
Mobile game monetization using Customer lifetime value prediction (CLTV or LTV)
The cost of acquiring new customers is huge, and only a small part of these customers will make a purchases inside your app. That is why it is very important to quickly find among all of your users, those who will make a purchase in the future. LTV machine learning models are able automatically predict how much money particular customer will spend in your application. This information will be useful in segmenting new users so that, in combination with customer acquisition cost (CAC), you can choose only those clients that will generate relevant revenue in the future. That solution will save money on advertising among customers who will never buy anything.
Customer churn prediction
Within 30 days after app installation, 90% of users stop using the app. It means that solving customer retention problem will definitely help you optimize marketing expenses and retain users. Churn machine learning models help to predict which customers are risky to stop using your application. Receiving such information in a timely manner allows you to take action to retain them. The highest value will be obtained in connection with the marketing automation software or CRM systems, which will automatically send personalized messages with tailored made offers per each customer.
Recommendation engine using machine learning in mobile gaming
Each customer has his own preferences. Also each person plays differently: some of them like to quickly move to the next round and some play calmly. For each of these players you could personalize the offer and content, it can be another similar game that he will probably want to play or a proposal for additional purchase to reach its goal in the game faster. That will increase customers satisfaction from the game, which will directly contribute to higher income.
Automate and integrate Machine Learning processes in mobile gaming industry
The key to success is to get your users hooked during first playing day’s period. Machine Learning in mobile gaming gives you opportunity to integrate it’s results in business applications. All this solutions with CRM integration not only helps to automatically hook particular customer, but also identify the best ones, create personalized offers, retain the most risky and as a result monetize them.
Visualize insights on business friendly tool – Business Intelligence (BI)
Each above mentioned activity and interaction requires monitoring and control. That is why it is worth monitoring how retention and LTV changes over the time, what group of users are the most risky and how users monetization goes. It is also important to get alerts on such events.
Integration of Machine learning results with original data into Data warehouse will give you unique possibility for multidimensional data analysis. Also, very useful will be flexible self-service Business Intelligence (BI) tools such as Tableau, Power BI, QlikView, Domo or Looker. Using those tools, you can easily make any dashboard in few minutes, set up alerts on preferred events or be emailed when something unusual is happening. Programming knowledge here is not necessary, those tools are fully self-service. Insights can be easily shared with your colleagues inside company or embedded into your internal applications.