Personalized Promotion Display Using Conversion Probability
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Solution Overview
Problem
E-commerce retailers face challenges in providing personalized customer interactions on their websites, as existing methods often rely on limited tracking of user behavior and demographics, leading to generic experiences that may not align with individual users' interests, resulting in reduced sales and customer satisfaction.
Innovation Solution
A system and method that track usage history and static user information to determine the probability of conversion for each promotion, using a combination of user input devices, processing modules, and non-transitory storage modules to display personalized promotions based on calculated probabilities, incorporating maximum entropy models for promotion selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If personalized promotion display is implemented, then customer experience and conversion probability improve, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments users into different profiles based on tracked attributes (demographics, browsing behavior, purchase history, device information) and applies different promotion strategies to each segment. This segmentation allows personalized promotion display without requiring complete customization for every individual user, thus improving conversion probability while managing system complexity through structured user grouping.
Solution Approach 2:
The system performs preliminary tracking and analysis of user attributes, browsing behavior, and purchase history before promotion display. User profiles are pre-built and maintained in the database, allowing the system to quickly retrieve and apply appropriate promotions without real-time complex calculations during the actual promotion display, thereby reducing instantaneous system complexity while maintaining personalization effectiveness.
2Measurement precision
If comprehensive user tracking is implemented, then personalization accuracy improves, but data privacy concerns and security requirements increase
Solution Approach 1:
The system extracts and stores only necessary user attributes and behavioral data in the database for personalization purposes, rather than collecting and retaining all possible user information. By selectively extracting only the data elements needed for promotion personalization (demographics, browsing patterns, purchase history), the system achieves adequate personalization accuracy while minimizing data privacy risks and security requirements.
Solution Approach 2:
The system uses an intermediary database structure that stores processed and aggregated user information rather than raw personal data. User attributes are processed into profile categories and behavioral patterns that enable personalization without exposing sensitive individual information, thus maintaining personalization accuracy while mitigating data privacy concerns through data anonymization and aggregation layers.
Data Source
AI summary
A system and method for providing a customized user experience is presented. A system can include one or more processing modules and one more non-transitory storage modules. The usage history of a user can be tracked at an eCommerce retailer. Static information about the user can also be tracked. Thereafter, using the usage history, the static information, and the available promotions of the retailer, a probability of conversion can be calculated for each promotion. Thereafter, the promotion being displayed to the user can be based on the probability of conversion. Other embodiments are also disclosed herein.