Dynamic Lightweight Personalized Analytics Engine for Real-Time Remittance Insights
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Solution Overview
Problem
Current analytics systems in consumer-focused industries, such as remittance, lack lightweight and fast methodologies for real-time personalized insights, focusing mainly on overall trends and patterns rather than individual behaviors, which hinders the ability to provide customized services and build customer relationships effectively.
Innovation Solution
A system for dynamic lightweight personalized analytics (DLPA) that dynamically adjusts data structures and recommendation priorities based on customer-specific parameters, social media, support, and communication data, using a computer processor to optimize memory usage and computation for generating real-time remittance and financial suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analytics systems process large amounts of customer data to gain insights, then measurement precision and reliability improve, but device complexity and processing time increase
Solution Approach 1:
The patent extracts and processes only the most relevant customer data points and behavioral patterns rather than analyzing all available data. The system identifies and focuses on key metrics such as transaction frequency, amount, and timing patterns, filtering out redundant information to reduce processing complexity while maintaining analytics precision.
Solution Approach 2:
The analytics system is segmented into modular components that process different types of customer data independently. The system divides customer behavior analysis into separate modules for transaction processing, pattern recognition, and insight generation, allowing each module to operate efficiently with reduced complexity.
2Adaptability or versatility
If the system processes more customer data for personalized insights, then adaptability improves, but loss of time increases
Solution Approach 1:
The system performs preliminary processing of customer data by pre-calculating behavioral patterns, transaction histories, and preference profiles. This preliminary action allows the system to quickly generate personalized insights without processing raw data in real-time, reducing processing time while maintaining personalization capability.
Solution Approach 2:
The system dynamically adjusts the amount and type of data processed based on the specific customer profile and analytical requirements. For high-value customers with established patterns, the system uses pre-computed insights; for new customers, it processes more data. This dynamic adaptation balances personalization needs with processing time constraints.
3Reliability
If the system maintains detailed customer profiles and historical data, then reliability improves, but volume of data to process increases
Solution Approach 1:
The system extracts and retains only the essential customer profile attributes and historical patterns necessary for reliable analytics. It filters out redundant detailed data and maintains a condensed representation of customer behavior that preserves reliability while reducing data volume for processing.
Solution Approach 2:
The system periodically reviews and updates customer profiles, discarding outdated or irrelevant historical data while recovering and retaining only the most relevant patterns. This maintains analytics reliability by ensuring data relevance while managing data volume through selective retention.
Data Source
AI summary
An embodiment of the present invention is directed to a feedback-based system and methodology for dynamically selecting communication messages, social media data, support data, etc. (denoted data) in evaluating dynamic lightweight personalized analytics (DLPA). Disclosed embodiments include a process for optimizing the key performance indicators (KPIs) used in measuring success by dynamically selecting the type, size, etc. of data leveraged by DLPA. This facilitates a small memory footprint and optimal computation when making smart, customized suggestions to users.