Promotion Filtering System for E-Commerce Decision Fatigue
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Solution Overview
Problem
In electronic marketplace applications, users face challenges in selecting relevant promotions from numerous options, leading to confusion, increased time spent on purchases, and potentially reduced revenue due to overwhelmed users or irrelevant promotions.
Innovation Solution
A system and method that uses an analysis module to filter and prioritize promotions in real-time, offering only relevant and optimal discounts to users during the listing process, thereby streamlining the purchasing experience and reducing confusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If multiple promotions are offered to users, then revenue potential increases, but user confusion and decision time increase
Solution Approach 1:
The system dynamically changes promotion parameters (selection, filtering, prioritization) based on user characteristics, purchase history, and contextual factors. This allows the system to optimize the promotion set for each user, maximizing revenue potential while minimizing decision time by presenting only relevant options.
Solution Approach 2:
The promotion system segments users into different groups based on their characteristics and behavior patterns. Each segment receives customized promotion selections rather than a universal set, which reduces confusion within each segment while maintaining overall revenue potential across diverse user bases.
2Ease of operation
If promotions are offered to all eligible users, then user engagement increases, but revenue decreases due to irrelevant promotions
Solution Approach 1:
The system applies local quality by customizing promotion offerings to match specific user characteristics, purchase histories, and contextual factors. Each user receives promotions tailored to their local context rather than a uniform promotion set, ensuring relevance and maintaining both engagement and revenue.
Solution Approach 2:
The system uses feedback from user behavior data, purchase history, and engagement patterns to continuously refine promotion selections. This feedback mechanism ensures that promotions remain relevant to each user, preventing engagement decline while avoiding revenue loss from irrelevant offers.
3Adaptability or versatility
If comprehensive promotion options are provided, then user choice increases, but user overload and confusion increase
Solution Approach 1:
The system applies partial action by selectively presenting only a subset of available promotions to each user based on their specific characteristics and needs. This partial presentation maintains user choice for relevant options while avoiding the overload and confusion that would result from presenting all possible promotions.
Data Source
AI summary
A method and a system to promote a publication are provided. In example embodiments, data is received from a user that is used to create a publication for placement on a networked-based system. An indication to promote the publication on the networked-based system is received. A user interface through which the user selects an option to promote the publication on the network-based system is presented. The publication is published on the network-based system, whereby the publication is promoted based on the option selected to promote the publication.