Personalized Online Shopping Offer System
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Solution Overview
Problem
Online shopping experiences fail to effectively tailor offers to individual users' interests, resulting in many irrelevant promotions being received, as existing systems do not utilize user-specific data to personalize the shopping experience.
Innovation Solution
A system that creates a user profile combining merchant and financial institution data to match users with relevant offers based on their purchase history, browsing habits, and preferences, allowing for personalized offers to be presented during online shopping sessions, either as an overlay on existing websites or through a dedicated interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If public offers are distributed to all users, then the merchant can reach a wide audience, but most users receive irrelevant offers they are not interested in
Solution Approach 1:
The system performs preliminary actions by collecting user data (purchase history, browsing behavior, preferences) before the shopping experience, creating a user profile that enables personalized offer distribution. This preliminary data collection and profile creation allows the system to anticipate user interests and deliver relevant offers proactively, rather than distributing generic offers to all users.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user interactions with offers and the shopping interface. User behavior data (clicks, purchases, time spent) is fed back into the profile updating process, allowing the system to refine offer personalization dynamically. This closed-loop feedback ensures offers remain relevant as user preferences evolve.
2Ease of operation
If traditional online shopping interfaces are used, then the system is simple to operate, but the shopping experience is not personalized to individual users
Solution Approach 1:
The shopping interface dynamically adapts its content and structure based on the user's profile and real-time behavior. Offers, product recommendations, and interface elements are automatically adjusted to match user preferences without requiring manual configuration. This dynamic personalization maintains ease of operation while significantly improving adaptability to individual users.
Solution Approach 2:
The system performs self-service by automatically creating and updating user profiles based on observed behavior, and autonomously selecting and presenting relevant offers. The interface adapts itself without user intervention, using implicit feedback from browsing and purchasing patterns to personalize the experience, thereby maintaining simplicity while achieving high personalization.
3Loss of information
If merchants collect and process user data for personalization, then offer relevance improves, but system complexity increases
Solution Approach 1:
The system employs a universal user profile structure that can accommodate multiple data sources (purchase history, browsing behavior, preferences, demographic information) and serve multiple functions (offer personalization, product recommendations, interface adaptation). This multi-functional profile approach consolidates complexity into a single manageable framework rather than requiring separate systems for each function.
Solution Approach 2:
The user profile acts as an intermediary layer between raw user data and the offer generation system. Instead of directly processing complex raw data for each offer, the system first transforms diverse user information into a standardized profile structure, which then serves as the basis for personalized offer selection. This intermediary abstraction simplifies the overall system architecture.
Data Source
AI summary
Embodiments of the invention are directed to a system, method, or computer program product for providing a modified online shopping experience to users. The modified online shopping experience presents offers to a user when the user is viewing a merchant online. The offers may be specifically tailored to the interests of the user viewing the merchant online. In this way, the system determines offers that the user may be interested in based on merchant data and financial institution data associated with the user. The tailored offers may be based on merchant data, such as previously accepted offers, loyalty accounts or the like and/or financial institution data such as products purchased, transaction history, and the like. The tailored offers that match the interest of a user may then be presented on the interface, website, or the like that the user is viewing when viewing a merchant online.