How AI is Changing Personalization and Content Production in Gaming

The origins of AI date back to the mid-20th century when scientists began exploring the concept of creating machines that could simulate human intelligence. Early developments in AI focused on tasks like problem-solving and logical reasoning. However, it wasn’t until the 2020s that AI experienced a surge in popularity through language models like ChatGPT and generative image tools like Midjourney.

Way before the so-called AI boom, game developers were using the technology to create immersive environments through procedural generation and NPCs (non-player characters) to improve gameplay depth. Now, the rapid evolution of artificial intelligence has resulted in even more possibilities for the industry, through the analysis of player data, advanced segmentation, and more content generation options.

Analysis of player data

Many companies have embraced AI-powered personalized experiences, tailoring their services to the individual preferences of their users. Google, for instance, refines search results by analyzing searches, translations, and emails. Spotify curates unique playlists based on listening history, while Netflix and Amazon Prime Video suggest shows and movies aligned with users’ past viewing habits.

In general, AI-based recommendations benefit both companies and customers. For companies, they boost customer satisfaction and engagement, leading to higher retention rates and better-informed business decisions. For customers, AI recommendations save time and effort by suggesting personalized options that match their preferences, improving the overall user experience and fostering loyalty.

Gaming has also entered the realm of personalized recommendations through data analysis. The Steam Interactive Recommender, for example, employs AI to deliver personalized and interactive game suggestions, taking into account player patterns and behaviors. The system offers flexibility with tag-based filters, allowing players to fine-tune results based on popularity, niche appeal, and the timelessness of titles.

These tailored recommendations manifest on the store homepage. Meanwhile, the “Explore and Customize” feature directs users to the full Interactive Recommender, enabling them to tweak parameters and save preferences. Notably, any adjustments made will influence the content displayed on the homepage, providing an integration of personalized choices into the gaming experience.

Advanced segmentation

Artificial intelligence also allows for advanced user segmentation. The latter involves categorizing users into distinct groups based on shared characteristics, facilitating tailored experiences. Companies often segment users by language preferences, geographical region, or user persona, enabling product teams to design personalized gaming experiences.

Common user segments are based on the following data:

• Data concerning the player's individual attributes, like age, location, language preferences, job title, or role.
• Customer Data. Information housed in a CRM about the user's association with the company, such as plan type, stage in the customer journey, annual revenue, account owner, or renewal date.
• Details regarding the player’s interactions with the product, encompassing metrics like login frequency, pages visited, features utilized, support tickets generated, and time spent on the site.
• Insights into the user's preferences and attitudes, related to product sentiment and captured through metrics such as Customer Satisfaction (CSAT) or Net Promoter Score (NPS).

In industries like iGaming, segmentation aids marketers in pinpointing user locations for targeted offers and recommending content based on preferences. For instance, if users enjoy King Jackpot slots, they can receive notifications about similar games, enhancing their gaming experience through personalized recommendations.

Content generation

Many companies are leveraging AI to generate text content, particularly when it comes to marketing, player engagement, and customer support endeavors. This AI-generated output serves diverse purposes, including more efficient chatbots as well as engaging and quick-to-produce social media posts, email marketing, game descriptions, reviews, tutorials, and guides.

AI's generative models also facilitate image creation for marketing materials, game development, and user interfaces. DALLE-2, Midjourney, and DreamStudio (to name a few) are tools offering image customization options. In the gaming world, they can be used to personalize avatars and in-game items.