Game Offer Personalization via Player Usage Profiling
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Solution Overview
Problem
Current game technologies lack effective methods to personalize and target real-world offers to players based on their interactions and preferences within the game, leading to inefficient marketing and reduced player engagement.
Innovation Solution
A method that determines a player's usage profile and information, categorizes them based on demographics and psychographic data, and selects relevant real-world offers from third-party providers to present to players, enhancing player engagement and marketing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generic offers are provided to all players, then marketing coverage is broad, but marketing efficiency is low and player engagement is reduced
Solution Approach 1:
The patent segments the player base into distinct categories based on usage profiles, demographics, and psychographic data. Players are divided into groups such as competitive players, social players, casual players, and achievement-oriented players, allowing tailored offers to be presented to each segment rather than using a generic one-size-fits-all approach.
Solution Approach 2:
The system performs preliminary actions by collecting and analyzing player data during gameplay to create usage profiles and categorize players before offers are generated. This advance segmentation and profiling enables the system to have personalized offers ready when players are most receptive, improving marketing efficiency without requiring complex real-time decision-making.
2Reliability
If player data is collected and analyzed to personalize offers, then player engagement and purchase likelihood increase, but system complexity and data processing requirements increase
Solution Approach 1:
The patent creates a multi-functional player profile system that serves multiple purposes: tracking gameplay behavior, determining player categorization, generating personalized offers, and measuring offer effectiveness. This universal profile structure eliminates the need for separate systems for each function, reducing overall complexity while maintaining high offer relevance.
Solution Approach 2:
The system introduces an intermediary layer (the usage profile and categorization system) that sits between raw player data and offer generation. This intermediary processes and structures data into meaningful categories, simplifying the complexity of directly mapping raw data to personalized offers while ensuring high relevance.
3Measurement precision
If multiple player categories are created with specific offers, then marketing targeting precision improves, but the complexity of managing multiple offer categories increases
Solution Approach 1:
The patent uses parameter changes by adjusting offer characteristics based on player category parameters. Each player category has associated parameters (e.g., competitive players receive offers related to in-game advantages, social players receive offers for multiplayer events) that automatically modify the offer presentation without requiring manual management of each individual offer.
Data Source
AI summary
A method of rewarding a game player of a game can target particular types of offers to a player. A usage profile is determined for the player, which can be based on interaction between the player and items or goals in a game. Player information is identified, such as player statistics or categorization of the player. A category is selected for the player, based on the usage profile and the player information for the player. An offer is then selected from a group of offers, wherein the offer is for a real world object, service or event.