Persona Identification System Using Behavior Mapping Models
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Solution Overview
Problem
Existing e-commerce solutions fail to accurately identify the need and intent of online shoppers, limiting their ability to provide personalized recommendations and effectively implement persuasion techniques.
Innovation Solution
A computer-implemented method and system that extracts session data from e-commerce applications, uses a behavior mapping model to analyze user behavior, segments users based on predefined attributes, and generates persona-centric persuasion techniques to motivate online shoppers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content-based recommendations and collaborative filtering techniques are used to analyze shopping patterns, then product recommendations can be provided to shoppers, but the complete analysis of shoppers' personalities and their actual need and intent cannot be identified
Solution Approach 1:
The patent segments shopper behavior analysis into multiple distinct modules: browsing history analysis, demographic data processing, session data examination, and real-time behavior monitoring. Each module focuses on specific aspects of shopper behavior, allowing comprehensive persona identification without creating a single overly complex system. This modular segmentation enables accurate identification of shopper needs and intent while maintaining manageable system complexity.
Solution Approach 2:
The patent transitions from traditional two-dimensional analysis (browsing history and demographics) to multi-dimensional analysis by incorporating session data, real-time behavior patterns, time spent on pages, and interaction depth. This dimensional expansion allows the system to identify complete shopper personas and actual purchase intent that cannot be captured by conventional methods alone.
2Reliability
If comprehensive analysis of shopper behavior is performed to identify actual need and intent, then accurate persona identification is achieved, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-defining behavior mapping models with established categories and attributes before actual shopper analysis begins. The system pre-processes demographic data and establishes baseline shopping patterns, allowing real-time analysis to focus on identifying deviations and specific needs rather than building analysis frameworks during live interactions. This reduces operational complexity while maintaining high reliability in persona identification.
Solution Approach 2:
The patent introduces behavior mapping models as intermediary layers between raw data collection and persona identification. These models act as mediators that translate complex multi-dimensional shopper data into standardized behavioral categories and attributes. The intermediary models simplify the connection between comprehensive data collection and accurate persona identification, reducing the apparent system complexity while enabling reliable persuasion technique implementation.
3Productivity
If real-time monitoring of complex and dynamic shopper behavior is implemented, then accurate user segmentation is achieved, but the data processing time and computational resources increase
Solution Approach 1:
The patent merges multiple data collection functions into unified processing operations. Instead of separately analyzing browsing history, session data, and real-time interactions, the system combines these data streams into integrated behavior patterns that are processed simultaneously. This merging reduces redundant computational operations and enables efficient real-time user segmentation without proportionally increasing processing time.
Solution Approach 2:
The patent implements self-service mechanisms where the behavior mapping model automatically adapts and refines segmentation criteria based on accumulated data patterns. The system performs self-optimization by identifying emerging shopper personas and adjusting analysis parameters without requiring manual intervention or reconfiguration. This self-service capability maintains high segmentation efficiency while reducing the computational burden of continuous manual model tuning.
Data Source
AI summary
A computer-implemented method and a system for implementing persuasion techniques based on persona of one or more online users is provided. Session data of the online users is extracted from one or more e-commerce applications and is analyzed using a behavior mapping model to identify persona of the one or more users. The one or more users or shoppers are segmented into different categories based on the identified persona of the users. One or more persuasion techniques are generated corresponding to each category of the segmented users. The persona-centric persuasion techniques are implemented in the field of e-commerce to motivate online users to shop.