How Predictive Analytics is Used in the Mobile Gaming Industry
According to data from Statista, the global mobile gaming industry will be worth more than $10 billion by 2023.
As the industry grows in size and value, mobile gaming companies are increasingly turning to a technique known as predictive analytics. This technical tool can be used to guide marketing strategy and campaigns, product development, resource allocation, and other business decisions. Predictive analytics can also help mobile gaming companies with specific business goals, such as increasing in-game purchases, player retention, and the lifetime value of the average player.
Using sophisticated algorithms and mathematical models, predictive analytics is focused on predicting possible outcomes based on what happened in the past. Businesses collect valuable data and then use predictive analytics to extrapolate the most likely outcomes, and then base decisions on these highly informed predictions.
In the mobile gaming industry, companies will often apply predictive analytics to customer data, which in this case equates to player data. Valuable data includes information on app installation and retention, in-app activities, in-app incentives, app ad monetization, and in-app purchases.
Predictions from analytics models are used to create customer profiles that are then used to determine which users are most likely to install an app, to churn, to make in-game purchases, or to be high-value players. Companies can then target specific digital products and services based on customer profiles. Analytics also is used to design new games and boost revenue for the game developer.
Through predictive analytics, gaming companies are better able to provide customers with exactly what they are looking for, reducing player churn, growing player LTV, and potentially increasing revenue by retaining more customers.
Reducing Churn in Mobile Gaming
As long as businesses have been around, customer retention has been an important focus. Gaming companies’ recruitment of predictive analytics in the battle against customer churn is just its latest evolution. Our internal analysis at Pecan has shown that predictive analytics is a very effective way to extend the length of a customer relationship and identify the risk of turnover for certain types of customers.
People stop playing mobile games for different reasons. A study performed by Clutch found that 33 percent of app users get frustrated when the onboarding process takes more than two minutes. Users onboarded effectively can have an increased lifetime value of more than 500 percent. Other components of a game or the player experience could also contribute to churn. If a gaming company can intervene before someone decides to stop using the app, they can address concerns, improving retention and player LTV.
Why Predictive Analytics Is Becoming More Popular in Mobile Gaming
If users like an app enough, they will spend money on it and in it. Many mobile games now generate revenue through game passes, subscriptions, and other in-game purchases. That said, only about 5 percent of users make in-app purchases.
If a game maker knew how each potential user was going to interact with their app, the company could specifically customize offerings to attract user spending. However, accurately predicting how someone would use various app functions and offerings has been very difficult until now.
A big reason why predictive analytics is becoming popular in the mobile gaming industry is Apple’s introduction of the SKAdNetwork as part of its 14.5 iOS update. Instead of the identifier for advertisers (IDFA) that tracked individual user interactions with an app, the new SKAdNetwork’s 6-bit conversion value only passes aggregated data to an app developer, solely from users’ first 24 hours with the app.
Because the new conversion value only passes information based on a limited time window, app developers are incentivized to use this value to pass along predictive events. Rather than focusing on users’ purchase amounts or progression events during their first interactions with the app, developers can use predictive events based on their wealth of historical in-app data to gain significant insight into the future value of a user-acquisition campaign. Using predictive analytics, developers can better anticipate how much users will spend by the 30-day mark.
Importantly, these metrics can also be used to minimize churn. When applied to churn statistics, game analytics software can be used to identify pain points, such as a poor onboarding experience or lack of personalization. Companies can then address issues quickly before they become widespread issues that alienate more users.
It is important to note that predictive analytics describes how likely it is that an event, like churn, will happen. Developers looking to leverage the power of predictive analytics should select a solution created by experts in the field. It’s also important for the solution to be thoroughly tried and tested to ensure it produces reliable, accurate, explainable predictions.
Engaging and Retaining Gamers with Pecan
Predictive analytics from Pecan allows game makers to predict every phase of the user journey. Our technology helps gaming companies optimize user acquisition, improve monetization, reduce churn, and cross-promote other apps.
Our predictive analytics platform is extremely simple to use. Simply add your data sources, and the platform automatically performs data prep and feature engineering. Our platform also optimizes and trains its predictive models to quickly generate accurate insights, as well as continuously monitoring models to maintain accurate results over time.