This article was originally published on the Google Play Medium channel.
Mobile games have evolved rapidly in recent years. Player expectations have increased along with this evolution. Players now demand games with rich and compelling stories that run smoothly regardless of their device. At the same time, as game studios realized the opportunities in going mobile, there is an ever-increasing number of mobile game titles competing for players' attention. In this increasingly competitive market, the ability to iterate and improve player experiences faster than rival titles is vital. Therefore, having the right tools and knowing how to apply them is a key skill for any game developer.
For many years, Firebase has provided game developers with the tools they need to build, release, and operate successful games. More than 2.5 million apps and games actively use Firebase every month, including global game studios such as Gameloft, Pomelo Games, and Halfbrick Studios.
This article explores four scenarios that game developers deal with on a daily basis and shows how Firebase provides the tools and insights to help you stay ahead of rivals.
It’s easier to provide better experiences to players when you understand how they interact with your game. Knowing how much time players spend in the game, what activities they prefer, and how often they come back enables you to personalize the game experience to suit their behavior and preferences. For example, different groups of players have different monetization preferences. Some may choose to spend money to improve their experience, while others may prefer to see ads and trade their time and attention instead. Understanding these differences – even details such as what people prefer to buy and what they do before making their first in-app purchase – is crucial to optimizing player experiences.
Firebase offers robust integration with Google Analytics. This integration helps shed light on who your players are and how they’re engaging with your game by providing insight into in-app events and user properties. This short video will show you how to log custom events and interpret the data to understand your players better.
Through the audiences feature, Firebase’s Google Analytics integration enables you to segment your user base in ways that are important to your business. You can use these custom audiences to filter reports to understand how different players engage with your game and identify patterns of behavior within the audience. Then, you can use this information to send targeted notifications and personalize the game behavior for player profiles. Take a look at this video to see how to set up audiences for your game.
Push notifications can be used for everything from reminders about opening a chest to letting everybody know the biggest update of the year is available. As push notifications allow real-time communication between a game company and its players, they’re often a critical part of a retention and re-engagement strategy. Most successful games have players worldwide, which means choosing just one language and time of day to communicate with them all is not ideal. After all, you don’t want to annoy players by sending them a push notification at 2 a.m.
Firebase Cloud Messaging lets game developers send targeted messages and notifications, which can be customized to suit your brand and align with player preferences. This video shows how you can send push notifications to players with devices set to a particular language and schedule delivery at an appropriate local time. Firebase Cloud Messaging also lets you target by game versions, geographies, your custom audiences, and many more variables. After sending the push notifications, you can see how many were delivered, how many were opened, and, if you choose to set it up, how many conversions happened.
While many players will gladly invest money in the games they enjoy, other players prefer ads in exchange for the gameplay. Many games employ a hybrid monetization strategy, meaning they include both ads and in-app purchases. As a general rule, the more ad impressions, the greater the revenue generated with ads. However, many other factors need to be considered to optimize and balance user experience and revenue. Differences between ad formats, their sizes, and how often they’re shown without disrupting the game experience are important factors.
Once you decide on these factors, how can you determine if the selected formats are the best ones? Is the frequency right or too much, and is the frequency negatively impacting retention? These questions can be answered quantitatively with Firebase A/B testing. A/B testing can be used with Firebase Remote Config to experiment with different combinations of ad types and frequencies to find the best option. To set up an experiment, all you need to do is define a goal, like increase total revenue, and identify secondary metrics such as D1 and D7 retention.
This video walks you through the process of setting up an experiment to test different ad formats so you can identify the best choice for a game. These parameters will vary from game to game, so it is always good to test the options available.
Pomelo Games, one of the top game studios in Uruguay, used Firebase Remote Config and Firebase A/B Testing to test the effect of showing interstitial ads to their entire player base versus a specific segment. Then, they used Google Analytics to measure the impact on revenue and retention. They also used Firebase Crashlytics to keep an eye on their game vitals. After two weeks of testing, the Pomelo team discovered that interstitial ads led to an average 25% increase in AdMob revenue and, surprisingly, a 35% increase in in-app purchases too. In both tests, there was almost no effect on retention. (Check out the full case study.)
Releasing new game features can be nerve-wracking because you may worry about how players will receive the new features. Will players enjoy the new feature you worked hard on? Will the new feature increase engagement and session time? One way to gain confidence that new features will positively impact your key metrics is by slowly rolling them out and seeing how they perform with a subset of your players before wider release.
In addition to gradually rolling out new features, it's also important to continually experiment with new content or in-game mechanics to optimize the player experience. However, constantly iterating your game can be a time-consuming and tedious process if you don't have the right tools.
Firebase Remote Config lets you dynamically configure your game and confidently roll out new features so you can deliver highly personalized experiences to your players without publishing an app update. This video shows you how to set up and tweak Remote Config parameters and instrument feature flagging.
Conclusion
Firebase is a powerful platform. It’s a great fit for game companies that want to enhance how they optimize experiences to delight their players and improve engagement and monetization. To get started with Firebase, you create your project in the Firebase console. To see more examples of how to use Firebase to supercharge your games business, check our Games with Firebase video series, where we walk through each step of the implementation process and share common use cases.
Last year at Firebase Summit, we introduced you to Predictions, a machine learning product that helps you smartly segment your users based on their predicted future behavior. Without requiring anyone on your app team to have ML expertise, Predictions gives you insight into which segments of users are likely to churn or spend (or complete another conversion event) so you can make informed product decisions and grow your app.
As of today, Predictions makes more than 6 billion predictions per day for our developers and allows them to take meaningful actions by making predictive segments available for targeting in Remote Config, Cloud Messaging, In-App Messaging, and A/B Testing.
This year at Firebase Summit, we announced that Predictions has graduated out of beta and into general availability with a host of new features that we added based on your feedback.
Since Predictions continuously update based on actual user behavior inside your app, we heard from many of you that you wanted to know how stable a prediction was before you integrate it into your app.
To help answer this question, we created a health indicator at the bottom of each predictive segment card that gives you a snapshot of how a certain prediction is performing:
Image 1: Green means it has been performing consistently well over the last two weeks
Image 2: Yellow means it is performing well today but did not meet the quality threshold some time in the past two weeks
Image 3: Red means it is not performing well today and had other performance issues over the last two weeks
It is worth mentioning that actions targeted with Predictions have a fail-safe mechanism, so if a predictive segment is performing poorly, it simply turns inactive. That means, if you are using Remote Config to deliver a set of values to users in that predicted group, Remote Config will gracefully fall back to your default values if the predictive segment decreases in reliability. Any notifications or in-app messages directed at that predictive segment will also not trigger until the predictive segment increases in accuracy.
To help you understand how we assess the quality of a prediction, we are now exposing our evaluation criteria. For every predictive segment, we use a portion of your historical data from the last 28 days that we hold out during the model training phase.
We then compare the results of the prediction to what actually happened. This gives us two ways to score the prediction: how many of the users in the predictive segment actually behaved in the predicted way (we call that true positive rate) and how many users in the predictive segment were incorrectly classified (or in more technical terms, the false positive rate).
You can access this data from the bottom of the prediction card
Tapping on the health indicator exposes these values.
By exposing these two scores to you, you can now make a better determination about which risk profile to choose for your action.
Another common question we received during our beta phase is what went into creating a predictive segment. We now offer a details page that gives you the ingredient list! You can click through and see what data our model makes use of. This includes event frequency, volume, and parameters as well as other data like device language, freshness of app install and more.
The last thing we are excited to announce is that now, you can export your raw predictions data into BigQuery. This will give you access to the raw prediction score, the thresholds we used for each risk profile, as well as the final result. You can use this data to create your own risk profiles or if you supply your own user_id property in analytics, to do sophisticated analysis with your analytics data. For example, you can find out which countries exhibit the highest potential to churn or spend!
We are humbled to have gained your trust over the past year and hope these improvements make it easier for you to make the most out of Predictions in your mobile apps and games. As always, if you have any questions, you can find us on Twitter (@firebase) and on Stack Overflow.
For more information on these updates, check out our docs below!
Predictions risk tolerance and performance
Predictions model inputs and details page
Predictions data export to BigQuery
