Data and artificial intelligence to protect and maintain the online gaming community

Data and artificial intelligence to protect and maintain the online gaming community

Data and artificial intelligence to protect and maintain the online gaming community

How do you play the data and AI card to prevent the gaming community from increasing toxicity and establishing a relationship of trust with each player?

The pandemic has brought unprecedented demand to the video game industry, with many platforms seeing traffic double, triple, or even quadruple in recent years. According to the 2021 edition of the Essentials of Video Games by SELL (Syndicate of Leisure Software Publishers), this sector in France is worth 5.6 billion euros, with an increase of 13.5% in 2 years, with 73 % casual gamers and 58% repeat customers. This drastic change in global gaming practices looks positive, but it is forcing gaming companies to innovate quickly to meet new customer demands and keep players coming back again and again.

This article explores the two main problems that video game companies are currently facing and the solutions to help them attract players, retain them, and build a relationship with them.

1. Addressing community toxicity

Trolls and toxicity are becoming a real and growing problem in the video game industry. Repeated abuse can turn players away from platforms and games, making the dropout rate one of the biggest factors affecting creative studio profits. Some companies even address toxicity early in development or even before launch. This shows that the scale of the play space has far exceeded the ability of most teams to manage such behaviors through reporting or intervening in disruptive interactions. Under these conditions, it is essential that studies integrate analysis into their games from the beginning of the development cycle to better understand what is happening there, and then design the management of toxic interactions, on an ongoing basis. So the question is how they should do it.

To monitor and manage toxic players and interactions, developers need to put in place systems that help them fully understand what’s going on in their game at all times. This requires the ability to collect and analyze data from various sources. The challenge is to tap into multiple sources and different types of data, all of which come at different frequencies. This is where data teams get stuck when it comes to bringing them together to identify critical information and make actionable decisions.

Hence, gaming companies need to be able to monitor toxic events in near real-time for quick resolution or even automated response such as silencing players or sending an immediate system alert about the incident. customer relationship management, in order to avoid any direct negative impact on player loyalty. However, this detection rate should not lead to a high false negative or false positive rate. False negatives, in fact, allow toxic behaviors to spread unhindered, while false positives can mistakenly exclude players who behave beyond reproach. Developing and using a trained machine learning (ML) model to quickly and accurately detect toxicity in a game helps maintain a positive user experience.

To design ML models that address in-game toxicity, companies need a data platform, which reduces data silos and allows them to use all of their data and combine technologies with advanced analytics, such as artificial intelligence ( TO THE). Adopting an open data architecture, such as a data lakehouse, makes it possible to develop such a strategy.

2. Building trust in games in the age of personalization and privacy

Another challenge that gaming companies face is how to monetize their communities in the cookieless era of GDPR we live in.

Game developers need to be able to put themselves in the consumer’s shoes and try to bring them added value. This builds trust, increases players’ willingness to share data when required, and thus generates new monetization opportunities. Players only trust platforms that cultivate a genuine relationship with them and consistently demonstrate their added value – first-rate experiences. If they like a specific type of content, they want recommendations to be sent to them based on that interest or how they play. And since today’s digital games are built around a community, the social networking aspect is key. Users also want to receive and connect around the content their colleagues love.

Once this trust is established, video game companies can then seek to truly personalize the player experience at each stage, leveraging data and ML to create the best tailored experiences modern gamers want. Sega Europe is a great example of a company implementing personalization. It now offers large-scale, individual customization, built on a data lakehouse architecture tailored to collect and act on over 600 types of data across 80+ games, with over 10,000 events per second. This platform produces richer information to provide more than 30 million customers with more relevant and fun advice and experiences. Riot Games, a leading publisher with over 100 million monthly active users, also relies on a data management platform on the lake, specifically to provide gamers with specific content and buying advice, which ensures customer engagement and lowers the rate. of abandonment.

Driven by the impact of the pandemic, the video game industry must find ways to maintain its growth and meet new challenges. By leveraging big data and next-generation artificial intelligence tools, companies in the sector have all the cards in hand to protect their ecosystem from new cyber stalkers, increase player acquisition and engagement, and build player loyalty. Anticipating at all levels, understanding the expectations of each player and knowing how to constantly adapt is the golden rule to conquer market share and offer a safe, fun and personalized experience. Such a win-win balance relies in particular on data quality and predictive intelligence throughout the player’s journey.

Leave a Reply

Your email address will not be published.