How G-Loot is using data to "level up" online gaming.
G-Loot is in a unique position to see how gamers behave across different games, and even platforms. Here's how the team is using this to create a safer and more unique esports experience.
With a rise in on-demand entertainment and more people from different walks of life playing games, in-person tournaments for a few top players are no longer a good enough solution for the esport industry. Players want to pick up a game for a few hours after work or school, with their friends, kids, or even grandkids, and leave with the satisfaction of winning.
G-Loot is a player-first esports start-up that aims to give more people access to competitive gaming and make it possible for anyone, anywhere in the world, to win money in the games they already love. This fun and social "esports for everyone" concept is realized through an online platform offering 24/7 esports challenges, micro-tournaments, and stat tracking.
G-Loot is currently available on both PC and mobile with console in the pipeline. G-Loot for PC already supports over 20 games, including top titles such as Valorant, Rainbow Six Siege, and Hearthstone Battlegrounds. One outcome of this extensive game coverage is a wealth of player data that G-Loot is now using in everything from improved ad targeting to discouraging in-game cheating.
Data-driven UX & marketing
As well as using extensive A/B testing to optimize the user experience of the service, G-Loot uses individual behavior triggers to ensure players are getting the information they need when they need it.
"We use game and player data to ensure the best possible experience for our users. For example, we target users who have entered a challenge but have not yet finished a game round to ensure that they know how to proceed and remind them that they've entered. We also use fully-automated marketing communications that depend on the user's lifecycle, actions, and game preferences. For example, perhaps you're an avid Apex Legends player but don't really fancy other shooters. In that case, we'll make sure to tailor the communication you receive so that you can focus on the thing you love the most." - Erik Olsen, CRM specialist.
Access to information such as number, frequency, and duration of rounds played by each user also allows the team to track fluctuations and changes in a player's behavior and respond accordingly. For example, a user who routinely enters but does not complete rounds of Valorant may be suffering from a lack of distraction-free time to play. In this case, G-Loot could suggest they play Apex Legends, which has a similar player-base but a shorter average round time. Similarly, users whose average session frequency drops off after months of playing a single title may have become bored with that particular game. Cross-game data could be used to recommend another title they would enjoy, while G-Loot's training challenges act as an incentive to get started.
Cheating has long been a problem in both professional esports and casual gaming. Publishers such as Riot and Valve have implemented technical solutions to block third-party tools that give players an unfair advantage. However, other forms of cheating don't require such tools and are therefore immune to these solutions.
Sandbagging: a player deliberately loses matches and lowers their rating to enter competitions against lower-level players.
Stat manipulation: two or more players co-operate, for example, by allowing themselves to be killed, to boost each other's stats.
Server manipulation: players take advantage of quiet periods on specific servers to gain easy wins and kills.
G-Loot's ability to dig into player behavior has proven to be a real advantage for the Anti-Cheating team. Rather than having to rely solely on user reports, the team can investigate suspicious accounts and spot any red flags that suggest foul play. For example, the team can compare the user's kill/death ratio against the average player to see if it falls within expected levels or even if it has changed dramatically in a way that suggests either deliberately losing or winning by manipulation. Comparing the user's IP address to the servers they play can also alert teams to server manipulation or even account sharing between multiple users.
The hope is that this process can become increasingly automated over time, with the team being alerted to behavioral red flags earlier. As anti-cheating procedures are refined (and as more people adopt G-Loot as part of their everyday gaming experience), this could even help to discourage cheating within certain games, as dishonest players miss out on rewards.
In the coming months, G-Loot plans to expand further into mobile gaming and console while refining the PC product. The Business Intelligence team hopes to devote even more time to understanding users' play-styles, skill levels, and cross-game or even cross-platform behaviors. "We'd like to understand how [players] move between and across games, how many switch to any new game post-launch, how much time they devote to each game if they play multiple games, etc." says Engagement Producer, Disha Sharma. These learnings could then be leveraged to customize each user's experience.
"Ultimately, the goal is to make the player experience as good as possible," commented Head of Creative, Niklas Ricaforte. "By looking at player behavior, we come closer to understanding what each player wants from their time with G-Loot."
If you'd like to know more about G-Loot, please contact firstname.lastname@example.org.