It’s sub-second-analytics that provide game developers with the most invaluable insights into the performance and behavior of the players engaging with their games.
Having them in sub-second makes it possible to more effectively monitor player behavior and thus make data-driven decisions that should (or hopefully will) improve the overall gaming experience.
It’s crucial for the success of any game… from mobile, to console to desktop.
Engaged players are so much more likely to spend time playing, spend money on in-game purchases, and recommend the game to their friends.
That means that maximizing player engagement is one of, if not the, top priority for game developers.
Defining player engagement
Before we can get started however, it’s important that we define exactly what we mean when we refer to ‘player engagement’.
It can cover a wide variety of metrics, depending on the game, platform or demographic but in general terms usually refers to the level of interest and involvement that players have with a game.
When measuring it, game developers will attempt to track time spent playing, frequency of play, and/or level of investment (i.e. in-game purchases).
Higher player engagement is a key indicator of a successful game and is absolutely vital for player loyalty and retention.
Getting the data in sub-second allows developers to understand which aspects of their game are working well and which areas need improvement, reacting accordingly, rather than report day at the end of the month.
Key engagement metrics for game developers
Time Spent Playing (TSP) — The amount of time players spend in the game.
Session Length — The duration of a single play session.
Session Frequency — Numbers of players logged in, divided by the total no. of login events. This lets game developers see how often players are engaging with their game.
Daily Active Users (DAU) — The number of unique players who play the game on a daily basis.
Monthly Active Users (MAU) — The number of unique players who play the game on a monthly basis.
Retention Rate — The percentage of players who continue to play the game after a specified period of time.
In-Game Purchases — The amount of money players spend on in-game items.
Average Revenue Per User (ARPU) — Total revenue generated in any one period divided by the total no. of unique players.
Average Revenue Per Paying User (ARPPU) — Much like ARPU, ARPPU measures total revenue generated but divides it by the total number of paying users, giving a much more accurate view of a games success/failure in monetization.
Lifetime Value (LTV) — LTV is the total no. of unique players using a game, divided by the total revenue generated by said game which lets us see an ROI metric per player.
Error Logs — As it sounds, counts/measures how many errors are triggered in your games content.
The role of sub-second analytics in maximizing player engagement
It goes without saying that some metrics need to be collected in sub-second, whilst others can wait.
ARPPU doesn’t need to be viewed live (it’s nice when it can be viewed whenever you want on a dashboard but it isn’t business critical).
Something like error logs however, do.
If something is going wrong in your game, affecting player engagement, then you need to know ASAP… not 30 minutes, a day or a month after all your players are gone.
Sub-second analytics does that, enabling game developers to track key engagement metrics as they happen, providing an up-to-date view of player behavior and engagement.
This lets them quickly identify trends and make data-driven decisions to improve the gaming experience.
A good example is if someone notices the game’s retention rate has suddenly dropped.
Sub-second analytics can be used to identify the cause and take action to fix it. It can also show which game features are the most popular and which areas of the game are causing players to drop off.
Armed with that data, game developers can make changes that will lead to increased player engagement.
It’s not just error logs that sub-second analytics are useful for though.
It can also be useful in personalizing a player’s gameplay, encouraging additional engagement and retention.
By using AI and some ML (or even a bit of heuristic programming) to track individual player’s behavior and preferences, game developers can create recommendations for in-game items or suggest new game modes to try.
That level of personalization leads to increased player engagement, with players feeling the game is tailored specifically to their needs and preferences.
And that’s before we even mention the optimization of personalized ads, should the game support in-game advertising.
Open-source tech for better sub-second analytics
So sub-second analytics are important… but how to achieve them?
Here at DoubleCloud we’re huge proponents of open-source technology, and one of the easiest ways to achieve sub-second, sub-second analytics is with our managed ClickHouse service, created for the gaming sector (more details here).
Creating your sub-second analytics solution with open-source tech will have huge benefits for your organization, including:
Flexibility — Open-source technologies allow for customization and integration with existing systems.
Cost-effectiveness — Open-source technologies are often free and have low operational costs.
Large Community Support — Open-source technologies have a large community of developers who contribute to their development and improvement.
It’s a lot easier to implement than you might initially assume as well.
When it comes to open-source tech, best practice (unless you’re a start-up that can make the conscious choice to build from the ground up in open-source) suggests starting small.
Begin with small implementations and scale as needed or is effective (again, unless you’re a start-up in which case building from the get-go in open-source tech is an excellent choice).
Choosing the right open-source tech for you is also important and this is where the benefit of the wider community really comes into its own.
There’ll be plenty of people happy to chat with you, advise and guide on what could work best for your organization and how it could be implemented or integrated into your existing systems.
Come chat with us about this on Slack
Finally, when choosing your different types of tech, it’s important to plan for your growth, so any solution you implement needs to be able to scale.
From pre-launch to post-launch: How to implement sub-second analytics for better gamer engagement
Before implementing anything, it’s important to sit down and scope out the analytics you’ll actually want to collect from your game. What’s important, what’s not, what helps drive the bottom line and, perhaps most importantly, of all of these, which need to be available in sub-second?
Retention rate? TSP? In-game purchases? Player progress (or lack thereof)?
Have you researched similar games on the market?
From that research is it possible to set performance benchmarks you can measure yourself against?
Once you’ve defined your key engagement metrics, your devs will need to choose the right tools and technologies that will let you track those metrics and, as we’ve already pointed out, are those tools scalable? Are they secure? And is there a cost associated with them?
From there, it’s vital that sub-second analytics aren’t an afterthought once the game has been developed, they need to be integrated into the design process from the very beginning for the most optimized results.
That ensures the metrics being tracked are relevant to the goals of the game and the data collected will actually be useful for making informed decisions about how to improve player engagement.
That means, an efficient data pipeline is a vital part of your LiveOps strategy.
Having a reliable (and sub-second) source of data on your players lets you respond much faster to player behaviors, optimizing your game as it grows or granting valuable information for new, better iterations (everyone loves a sequel!)