Application Recommendation Engine for Online Sellers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Power sellers in online commerce face challenges in discovering and utilizing applications that could enhance their sales processes, as they often rely on internet searches and lack awareness of available tools that meet their specific needs.
Innovation Solution
A recommendation engine system that profiles sellers based on their sales metrics and item types, compares user profiles to recommend relevant applications, and assesses the impact of these applications on sales performance, suggesting the most suitable tools to improve sales effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sellers search the Internet manually for applications, then they can discover some tools, but they cannot efficiently find applications that meet their specific needs and may remain unaware of suitable tools
Solution Approach 1:
The patent introduces an intermediary system (application recommendation engine) that connects sellers with relevant applications. This intermediary automatically analyzes seller profiles, monitors application performance data, and recommends suitable applications, eliminating the need for sellers to manually search while ensuring they receive targeted recommendations based on their specific needs and performance metrics.
2Adaptability or versatility
If sellers use generic Internet search methods, then they can find some applications, but they cannot identify which applications will best meet their specific sales needs
Solution Approach 1:
The system performs preliminary actions by creating detailed seller profiles in advance that capture specific sales metrics, item categories, and performance characteristics. It also pre-analyzes application performance data and compatibility information. When a seller needs an application, the system quickly matches them with suitable options based on this pre-prepared information, ensuring high accuracy in matching applications to specific seller needs.
3Productivity
If sellers rely on self-directed searching, then they maintain control over their search process, but they lack access to curated recommendations based on proven performance data
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring application performance data, seller metrics, and transaction outcomes. This feedback loop allows the system to learn from actual results and refine its recommendations over time, providing sellers with curated application suggestions that are based on proven performance data and continuously improved through real-world results.
Data Source
AI summary
A computed-implemented method and system for recommending business applications on a network-based marketplace are described. A user's listings, representing items for sale on the marketplace, are harvested to calculate segmentation data and metrics that form a user profile. The user profile is compared with other similar users who have subscribed to various applications, and the impact those applications have had on the metrics of the similar users is calculated in order to determine what impact the applications will have on the user in question. The impact, combined with user preferences, is used to suggest appropriate applications, which are displayed to the user within the marketplace. If the user selects one of the applications, the application is added to the user's profile and relevant listings are updated with the new application.