Intent Analysis Application for E-Commerce Path Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Electronic commerce systems fail to effectively identify and market desirable product attributes to customers, leading to difficulties in generating accurate product recommendations and targeting campaigns.
Innovation Solution
The intent analysis application infers user intent by analyzing navigation paths and contextual information, grouping users based on similar paths and behaviors to provide personalized content and detect anomalies, which can indicate fraudulent or assistive needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electronic commerce systems use traditional product attribute categorization, then product marketing and categorization are straightforward, but the system fails to identify and market desirable product attributes that customers actually want
Solution Approach 1:
The system implements feedback loops where user navigation paths and behaviors are continuously monitored and fed back into the intent analysis application. This feedback mechanism enables the system to learn from actual customer actions rather than relying on predefined categories, allowing it to identify desirable product attributes that customers truly value while maintaining manageable complexity through iterative learning.
Solution Approach 2:
The intent analysis application performs self-service by automatically analyzing navigation paths and inferring user intent without requiring manual input from analysts. The system autonomously groups users based on similar paths and behaviors, identifies desirable product attributes, and generates insights that improve product marketing and recommendations, thereby resolving the contradiction between measurement precision and system complexity.
2Measurement precision
If the system analyzes detailed navigation paths and user behaviors to infer intent, then product recommendations and targeting campaigns become more accurate, but the computational processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-grouping users into intent groupings based on their navigation paths and behaviors. These pre-computed intent groupings are stored and can be quickly referenced when generating product recommendations or targeting campaigns, significantly reducing the computational energy required during actual recommendation generation while maintaining high accuracy.
Solution Approach 2:
The intent analysis application serves multiple functions: it groups users by behavior patterns, identifies desirable product attributes, generates intent groupings for recommendations, and detects fraudulent activities. By consolidating these functions into a single multi-functional system, the patent reduces redundant computational processing and energy consumption compared to having separate systems for each function.
3Reliability
If the system monitors user paths and behaviors to detect anomalies, then fraudulent activities can be identified, but the complexity of path tracking and analysis increases
Solution Approach 1:
The patent introduces an intermediary component - the intent analysis application - that mediates between raw navigation path data and fraud detection requirements. This intermediary groups users by similar paths and behaviors, creating intent groupings that serve as a simplified representation of complex user behaviors. Fraud detection then operates on these grouped patterns rather than individual raw paths, reducing system complexity while maintaining reliable fraud detection capability.
Data Source
AI summary
Disclosed are various embodiments for detecting unexpected behavior. A path associated with a user is tracked. It is determined whether the path corresponds to at least one intent grouping that in turn corresponds to an expected behavior. Unexpected behavior is identified when the path does not correspond to at least one intent grouping.