NLP and Statistical Analysis for E-commerce Category Matching
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Solution Overview
Problem
Current systems for identifying matching categories and generating targeted offers in e-commerce lack efficiency in leveraging natural language processing and statistical analysis to accurately classify products and customers, leading to suboptimal offer targeting.
Innovation Solution
The integration of natural language processing and statistical analysis to classify item descriptions into tiers and analyze transaction data, enabling the creation of profiles for merchants and customers, and automatically generating targeted offers based on affinity scores and consumer behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing and statistical analysis are integrated to classify items and analyze transactions, then offer targeting accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex analysis task into distinct modules: a natural language processing component that classifies item descriptions into categories and tiers, and a statistical analysis component that processes transaction data. This segmentation allows each module to specialize in specific functions, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary layer that bridges natural language processing and statistical analysis. This intermediary processes and standardizes data from both sources, enabling them to work together effectively. The intermediary translates unstructured item descriptions into structured categories that can be correlated with transaction patterns, thereby improving targeting accuracy without directly increasing the complexity of core algorithms.
2Measurement precision
If detailed natural language processing is applied to item descriptions, then product classification accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary classification of item descriptions into categories and tiers before the actual offer generation process. By pre-processing and organizing item data into structured formats with defined hierarchies, the system reduces the computational burden during real-time offer targeting, thereby maintaining high classification accuracy while reducing processing time for subsequent operations.
3Adaptability or versatility
If comprehensive transaction data analysis is performed to create customer profiles, then offer personalization is improved, but data processing complexity increases
Solution Approach 1:
The patent applies local quality by creating customer profiles that are tailored to specific segments rather than treating all customers uniformly. The system identifies distinct customer segments based on transaction patterns and applies customized offer strategies to each segment. This approach improves personalization effectiveness by focusing analysis on relevant local characteristics of each customer group rather than attempting to model every individual's complete behavior pattern.
Data Source
AI summary
Combining the natural language processing of product descriptions and statistical analysis of payment data to classify consumers based on products purchased and merchants based on products sold. Systems and methods use natural language processing techniques to interpret the descriptions of item level purchase data to classify products that have been purchased by customers into micro-categories. Statistical deviation methods are applied to the payment data to calculate normalized mean product cost, after removing outliers. After determining the micro-categories of the products purchased and the mean product cost of the purchased products, the system and methods classify consumers and merchants into categories based at least in part on the product micro-categories, mean costs, and relative volume of product types sold by merchants to predict which consumers are likely to purchase from which merchants.