Deep data provides developers with the ability to gather detailed information on each player at any point in the game, generating endless opportunities to access actionable insights. Here are three essential ways to improve your game with deep data.
The gap between the ‘haves’ and the ‘have-nots’ in the F2P games market has never been greater. DeltaDNA data confirms this: the top 20% of games have a payer fraction of more than 4%, while the lowest have less than 0.5%. And with 13,185 new iOS games released globally each month (on average, according to Priori Data), and 22,905 on Android, competition is tough. Many are turning to deep data analytics to differentiate their games, improve player experience and create a commercial success. But how exactly do you do this?
What deep data provides is the ability to gather detailed information on each player at any point in the game. So, in a First Person Shooter game, knowing kills, damage inflicted and received, weapons used, rate of progress, deaths, potions used, re-spawns, experience points, in-game currency, squads joined… the list goes on.
The opportunities to generate actionable insight are endless. To get started, here are three essential things to do with deep data that will help improve your F2P game.
Retention is a key metric in F2P games, and on average 20%-30% of players leave within the first two minutes of game play. Tracking the First Time User Experience (FTUE) will help you to improve the onboarding process. Small improvements here can make a big difference later on in your game.
A typical FTUE journey in a F2P game
You need to determine which aspects of your onboarding are causing segments of players to leave. Perhaps the controls aren’t clearly explained, or it’s too time-consuming. To do this you need to compare the experience of those players who left with those who stayed.
On your game analytics platform, set a high density of event counters to enable you to track how players are moving through the FTUE.
Next, use funnels to analyze level progression, to instantly tell you which areas of the game are acting as blockers.
Look at retention and monetization statistics at each game level to allow key thresholds to be identified (e.g. most players pay at level 5, or retention peaks at level 3).
Finally, by comparing real-world time to in-game progress, the game can be balanced to ensure that players never lose momentum and churn.
Game developers have always segmented players in one way or another to help with marketing strategies. In F2P, this has led to a number of commonly identified segments which describe shared player behaviors. Whales (big spenders), Novices, Experts, and Grinders have become commonly used terms in game analysis. But while these player segments are useful, if you rely on them alone, you could overlook segments that are unique to your game and the way it’s structured.
Identify unique segments by looking at shared player behaviors in your game
With deep data, you can take a more detailed look at factors like engagement, player performance, aggression, strategy and social interaction, or any aspect of gameplay to create a segment. By identifying your own segments, you can then use player engagement tools to create augmented experiences. For example, this could be implemented through messaging, gifting and making offers to individual segments, or adjusting game parameters when needed. The objective is for the game to both challenge and reward all players.
The first step in defining your own unique player segments is to consider which measures you will use. For example, looking at the rate XP is gained alongside kill/death ratio could identify a segment of highly competent players. You’re then all set to measure their game interaction, introduce engagement mechanics and live testing.
Just as important as identifying segments is to measure both the immediate and long term effects of any campaigns or adjustments made. A/B testing is most widely used by developers to do this.
A/B testing is often used to establish optimum levels for variants, e.g. IAP pricing. It can show whether an offer is being well received, and show the implications for player progression and monetization. To put simply, making a change to the game may cause one group of players to spend more and another to spend less. So, you need to evaluate the results from every angle using a broad variety of game Dashboard tools.
Determine the number of variants you want to set up in your test, and target specific user segments.
A/B testing can be used to test different variables, including in-game messaging and offers
An advanced tool like deltaDNA’s A/B Testing will also give you access to significance testing and analysis dashboards, so you can ascertain the effects of each variant on all aspects of your game.
Access to deep data is essential for determining how players interact with your game and for knowing who your players are. You need this information to engage with your players effectively, otherwise you risk upsetting as many as you encourage. Focus on optimizing the experience for all players and you’ll find commercial success is far more likely to follow.
Post first appeared on the deltaDNA blog.
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