Semantic queries unlock next-level capabilities with powerful AI and LLM functionality. Kinn searches for feedback across Discord, Steam, YouTube, Reddit, and Twitch for the most comprehensive view possible.
Understanding your player on a personal and meaningful level can feel overwhelming. There are multiple communities to keep up with, thousands of comments to read, and an endless list of streams to watch. Even with a full team of community managers, producers, and UX researchers it’s impossible to keep up with the conversations among your players.
Without proper sentiment analysis and tracking from the tools that matter, your team is making game development decisions without data to back it up.
Historically, queries have been mostly generated using a boolean methodology. They were simple keywords or incredibly short phrases that would match an exact instance of that word.
A classic example is “bug.” A simple boolean query will retrieve any piece of content that contains that exact word. But it might miss similar, closely related terms. So to solve that, oftentimes teams will bundle it as “bug OR broken OR crash,” in an attempt to capture a more comprehensive picture.
This process has been a staple of marketing and community analysis for decades, but it’s starting to show its age—especially in the era of AI.
Semantic queries leverage the power of large language models and AI to comprehend a query the same way humans do. This means that you can look for things by asking a more broad question or statement.
Following the previous example of “bug”, you might instead say “players complaining about stability or performance issues.” This will return a much more thorough and accurate result that more closely aligns with what you were probably looking for.
With semantic queries, you can get a clearer picture of your game's performance by breaking down the concerns into various subjects. If you wanted a better understanding of player sentiment, you might write multiple queries exploring crashes, stability, graphics performance, lag, ping, or other more unique elements focusing on player complaints.
Additionally, some semantic querying tools will allow you to broaden or sharpen the scope of the query. If you write a simple query such as “players complaining about graphics and performance.” The results might return a wide variety of keywords such as DLSS, ray tracing, FPS, and other related terms. But if you specifically want to ignore a target word, you can do so to refine the query to exactly what you are looking for.
It’s often easy to get caught up in queries and feel like you’re just digging around in an endless field, hoping to find something that stands out. But it’s key to remember that the goal is to build a better game, and the best way to do that is to explore what players love most, and what they are most frustrated by.
Players often have strong opinions about various parts of the games they play, but generally, they all love when studios are clearly listening to their feedback and responding to their needs.
This is where queries can really shine. If you’re spotting a trend or feel like there is a pattern going on in your community, a semantic query about that topic can give you concrete details on who and how many people are discussing this.
Some great examples we see teams put together are queries about new in-game content, DLC, seasonal releases, or anything that was recently updated in a patch. It’s incredibly easy to get distracted by a few loud voices, so a good query is an excellent way to understand your larger playerbase without giving the loudest voices too much weight.
Game studios and publishers all around the world use Kinn to build state-of-the-art queries on their games. Our powerful, semantic models understand in-game jargon, slang, and cultural nuance in amazing ways.
Kinn is the only tool in gaming to query across all of your platforms at once. With connections to Discord, Steam, Reddit, YouTube, Twitch, and more, you can expect to get the widest range of voices and the most comprehensive comments from all of your players. Queries will look both internally to your Discord users, but also discover mentions from streamers, YouTubers, and their fans.
Our semantic queries go even further by translating all non-English content to whatever local language you prefer. Using the latest translation technologies, you can query everything from your local language, and retrieve results from anywhere in the world. As gaming becomes more international, understanding your audience in international markets becomes increasingly important for even the smallest of teams.
Queries in Kinn also span massive date ranges and can be filtered dynamically by patch or game version. Kinn logs dates when new versions of your game are released, and gives you quick methods to view data within a specific game version. A great use case is querying seasonal content, balance patches, or battle passes. You can quickly gauge player responses to changes in your game mechanics that might have been modified from season to season.
Kinn makes critical decision-making a breeze for game studios. With our comprehensive topics, queries, and community companion tools, you can listen and respond to enormous volumes of players with ease.
Kinn comes built-in with powerful integrations to the most important tools in gaming.
Enhance all of your touchpoints with better feedback analysis, sentiment scoring, and multi-lingual insights from every tool at once.
We’ll help you scan the horizon and find the most important bugs, suggestions, and pieces of feedback from millions of players across the globe.
Get a demo of Kinn today and start transforming community insights into confident decisions.
