User Identification System Using Segmentation Algorithms
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Solution Overview
Problem
Retailers face challenges in identifying and targeting value-conscious users who frequently purchase value-sensitive products online or in physical stores, as these users often browse and purchase both discounted and non-discounted items, making it difficult to accumulate and analyze data effectively.
Innovation Solution
A system and method that track user activities and behaviors to determine value-conscious users by using conditional probabilities and algorithms, analyzing features such as purchasing history, browsing patterns, and product affinities to identify users who frequently buy value-sensitive products, and tailor recommendations and promotions accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system tracks and analyzes all user activities and purchasing histories to identify value-conscious users, then the accuracy of user identification improves, but the computational resources and data processing complexity increase
Solution Approach 1:
The patent segments users into distinct groups (value-conscious users vs. non-value-conscious users) based on their purchasing behavior patterns. By dividing the user base into segments with different characteristics, the system can apply targeted analysis methods to each segment, reducing overall computational complexity while maintaining identification accuracy.
Solution Approach 2:
The system changes parameters by establishing threshold values for purchasing behavior metrics (e.g., frequency of buying discounted items, proportion of discounted vs. full-price purchases). By converting continuous behavioral data into discrete parameter thresholds, the system simplifies the identification process while preserving accuracy in distinguishing value-conscious users.
2Productivity
If the system provides personalized promotions to value-conscious users, then user satisfaction and conversion rates improve, but the marketing automation complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-identifying and segmenting value-conscious users before marketing campaigns are launched. By pre-processing user data and creating ready-to-target user segments, the system simplifies subsequent marketing automation processes while maintaining high conversion rates through personalized promotions.
3Reliability
If the system analyzes shopping histories and patterns for all users, then the reliability of identifying value-conscious users improves, but the time and computational resources required increase
Solution Approach 1:
The patent extracts and focuses analysis only on the most relevant features from user shopping histories (e.g., purchase frequency of discounted items, price sensitivity indicators). By extracting only the critical features needed for classification rather than analyzing all available data, the system maintains high reliability in user identification while significantly reducing analysis time and computational resources.
Data Source
AI summary
A method can include retrieving product information from a website database to identify a first product as a value-sensitive product identified with at least a value price tag. The method can include determining second users who are not value conscious about the first product. The method can also include preparing first and second recommendations and promotions for the first product, wherein the first recommendation comprises one or more value-sensitive products. The method additionally can include transmitting machine readable instructions to display the first recommendations and promotions for the first product for viewing by the first user. The method also can include transmitting machine readable instructions to display the second recommendations and promotions for the first product for viewing by the second user. Other embodiments are disclosed herein.