Game Player Clustering Using Play Data for Targeted Advertising
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Solution Overview
Problem
Marketing agencies struggle to effectively target computer game players with advertisements, as current methods expose games to unspecified audiences, leading to inefficient marketing efforts.
Innovation Solution
A method for clustering game players based on their playing tendencies by analyzing play data, assigning tags to games, calculating weights for these tags, and classifying players into clusters using a clustering algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If game advertisements are exposed indiscriminately to various media used by unspecified audiences, then advertising coverage is maximized, but marketing effectiveness deteriorates
Solution Approach 1:
The patent segments the player population into distinct clusters based on play data analysis. Players are divided into groups with similar gaming behaviors, preferences, and patterns. This segmentation allows marketing agencies to target specific clusters with tailored advertisements rather than broadcasting to all players indiscriminately, thereby improving marketing effectiveness while maintaining adequate coverage through multi-cluster targeting strategies.
2Measurement precision
If play data is collected and analyzed to classify players into clusters, then marketing precision is improved, but system complexity increases
Solution Approach 1:
The patent introduces a server as an intermediary component that handles the complex tasks of data collection, storage, weight computation, and clustering analysis. This centralized server architecture manages the computational complexity of processing play data and generating player clusters, while providing simplified interfaces for marketing agencies to access clustered player information and apply targeted marketing strategies without needing to implement complex analysis systems themselves.
Data Source
AI summary
A method for clustering players according to their tendencies, using play data of the players playing games is disclosed. Tags are assigned to each of the games to express the characteristics of each game, and the weights for the tags are calculated based on the play data for each of the players. Each player is classified into plural clusters according to their tendencies, based on the weights calculated for each tag. By classifying and clustering players according to their play tendencies, game developers can target advertising to players who match the characteristics of their games.