E-commerce Sales Performance Analysis via Visit Mix and Leakage Metrics
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Solution Overview
Problem
E-commerce businesses face challenges in comparing and optimizing the sales performance of their web pages due to variations in user interactions and lack of effective tools to quantify factors contributing to sales differences over time or between pages.
Innovation Solution
An E-commerce performance evaluation system that analyzes browsing history data to determine numerical metrics for each link or group of links, highlighting specific factors affecting sales through visit mix, consummation ratio, and leakage, allowing businesses to optimize web page layouts for improved sales performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sales performance tracking is used, then basic sales data can be monitored, but factors contributing to sales differences cannot be quantified
Solution Approach 1:
The patent segments the sales performance measurement into multiple distinct components: visit mix (proportion of visits to different page types), consummation ratio (conversion rate), and leakage (lost opportunities). By breaking down the overall sales metric into these separable factors, the system can quantify and analyze each contributor independently, resolving the inability to identify specific factors affecting sales differences.
2Measurement precision
If detailed browsing history analysis is implemented, then factors affecting sales can be identified, but system complexity increases
Solution Approach 1:
The patent extracts only the essential and most impactful factors from the comprehensive browsing history data: visit mix, consummation ratio, and leakage. Rather than analyzing all possible browsing behaviors, the system selectively isolates these three key metrics that directly influence sales performance. This extraction approach maintains measurement precision while avoiding the complexity of processing and analyzing the entire browsing history dataset.
3Ease of operation
If multiple metrics are calculated for each link, then actionable insights are provided, but data processing requirements increase
Solution Approach 1:
The patent applies local quality by calculating the three metrics (visit mix, consummation ratio, leakage) specifically for each link or group of links based on their local characteristics and performance. Rather than applying a uniform analysis across the entire website, the system tailors the measurement to each link's context, providing actionable insights localized to specific pages and links. This approach enables targeted optimization while processing only the necessary data for each local context.
Data Source
AI summary
A method for comparing sales performance of two web pages includes receiving usage data associated with each web page. The usage data includes next-click visit mix information and sales consummation information. A metric is determined based on a visit mix associated with a link category at a second web page, and further based on a difference between a consummation ratio of the link category at the second web page and a consummation ratio of the link category at a first web page.