eCommerce Query-Product Graph for Ad Campaign Evaluation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing eCommerce platforms lack effective methods for evaluating user buying trends and the effectiveness of advertising campaigns, necessitating improved tools for analyzing user-query and product relationships.
Innovation Solution
A graphical user interface (GUI) with an interactive product-query graph is implemented, displaying product and user-query relationships, allowing users to input queries or product identifiers, and providing statistics and advertising performance indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional eCommerce analytics methods are used, then implementation is simple, but the ability to evaluate user buying trends and advertising effectiveness is insufficient
Solution Approach 1:
The patent introduces a Product-Query Graph as an intermediary data structure that mediates between raw eCommerce data and analytics insights. The graph contains nodes representing products and user queries, with edges representing predictive purchase relationships. This intermediary structure enables accurate evaluation of buying trends and advertising effectiveness without requiring complex analytical algorithms, thus resolving the contradiction between measurement precision and device complexity.
2Loss of information
If detailed user query data is collected, then buying trend analysis improves, but data processing complexity increases
Solution Approach 1:
The patent extracts only the essential elements from user query data—specifically, the relationship between queries and purchase outcomes—and stores them in the Product-Query Graph as predictive relationships. This extraction approach retains the critical information needed for trend analysis while eliminating unnecessary data processing complexity, as the graph structure naturally organizes and queries the extracted relationships efficiently.
3Loss of information
If predictive purchase relationships are visualized, then marketing insights improve, but interface complexity increases
Solution Approach 1:
The patent segments the complex predictive purchase relationship data into discrete, visualizable nodes (products and queries) and edges (predictive relationships) in the Product-Query Graph. This segmentation allows marketing insights to be presented as clear, intuitive visual connections between user queries and purchase outcomes, making the information easy to interpret while maintaining interface simplicity through familiar graph visualization patterns.
Data Source
AI summary
A computer-implemented process includes receiving with an eCommerce platform from a user over a network a first product-search keyword, generating to a display of the user a graphical user interface (GUI) including a first selectable icon showing the first keyword, a first selectable image of a first product and a first connector between the first icon and first image, the first connector indicating that the first product was previously purchased using the eCommerce platform in response to the platform receiving the first keyword, and in response to user selection of the icon, generating to the GUI a set of performance indicators characterizing an advertising campaign associated with at least one of the first keyword and first product within the eCommerce platform.