Cross-Platform Loyalty System with Probabilistic Rewards
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Solution Overview
Problem
Existing computing systems struggle to comprehensively analyze consumer engagement across multiple platforms, including social media, content platforms, gaming, and retail, leading to incomplete understanding of brand loyalty and consumer behavior, which limits effective engagement and loyalty programs.
Innovation Solution
A cross-platform loyalty and rewards system that utilizes probabilistic methods to award rare and unique items based on user interactions across various platforms, incorporating demographics, purchase history, social media interactions, and geolocation, with a loot server to track and authenticate these items, promoting brand loyalty and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing computing systems analyze consumer engagement only within a single business platform, then the system complexity remains manageable, but the comprehensiveness of consumer behavior understanding is insufficient
Solution Approach 1:
The system segments consumer engagement data into distinct categories from different platforms (social media interactions, content consumption, purchasing behavior, etc.) while maintaining a unified loyalty score. This allows comprehensive analysis without overwhelming complexity by organizing data into manageable segments that can be processed independently yet contribute to the overall picture.
Solution Approach 2:
The loyalty score serves multiple functions simultaneously: it aggregates data from diverse platforms, normalizes different types of consumer interactions, and provides a unified metric for engagement assessment. This multi-functionality allows the system to handle complexity by creating a universal language that translates various platform-specific metrics into a common framework.
2Loss of information
If the system incorporates multiple cross-platform metrics for loyalty assessment, then user engagement analysis becomes more comprehensive, but the measurement and detection difficulty increases
Solution Approach 1:
The system transforms diverse cross-platform metrics into a standardized parameter system where different types of consumer interactions (social media engagement, content views, purchases) are all converted into comparable loyalty score contributions. This parameter transformation simplifies measurement by establishing consistent weighting and normalization rules across platforms.
Solution Approach 2:
The loyalty score acts as an intermediary that mediates between various cross-platform metrics and the final engagement assessment. It translates and harmonizes data from different platforms into a unified scale, reducing measurement difficulty by providing a common reference framework that simplifies comparison and aggregation.
3Productivity
If the system provides detailed cross-platform consumer behavior insights, then targeted advertising effectiveness improves, but the information processing requirements increase
Solution Approach 1:
The system extracts and isolates the most predictive factors from the vast amount of cross-platform consumer data to create the loyalty score. By identifying and separating only the critical engagement indicators rather than processing all available data uniformly, the system reduces information processing requirements while maintaining advertising effectiveness.
Solution Approach 2:
The system applies partial analysis by focusing computational resources on the most impactful cross-platform metrics rather than equally processing all available data. This selective approach processes only the necessary information depth required for effective targeting, avoiding excessive processing of less relevant data.
Data Source
AI summary
Systems, apparatuses, and methods are described for determining a consumer's engagement with a brand of the business by tracking the consumer's activities in multiple platforms, such as social media platforms, content platforms, gaming platforms, other retailers, streaming video providers, service providers, etc. Method are described for probabilistically granting users variations of items that are otherwise being acquired. The granting may be random, but probabilities may be boosted based on the consumer's activities in the platforms.