Merchandising Interface for Missed Revenue Calculation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current web analysis solutions lack comprehensive merchandising analysis capabilities, particularly in identifying missed revenue opportunities by not adequately comparing product prices with competitors and optimizing sales performance on a granular product catalog level.
Innovation Solution
An experience analytics system that includes a merchandising interface for visualizing product opportunities, category, and brand performance, identifying cheapest competitor products, and calculating missed revenue based on price differences, integrated with competitor pricing data to provide actionable insights for price adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive competitor pricing data collection and analysis is implemented, then missed revenue identification capability is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the merchandising analysis into distinct functional modules: competitor data collection module, price comparison module, missed revenue calculation module, and visualization module. Each module handles a specific aspect of the analysis, making the overall system more manageable and maintainable while achieving comprehensive competitor pricing analysis
Solution Approach 2:
The patent introduces an intermediary analytics engine that sits between raw competitor pricing data and business decisions. This intermediary processes and structures competitor pricing data, compares it with merchant pricing, calculates missed revenue opportunities, and presents actionable insights through a user interface, thereby simplifying the complexity of direct data-to-decision mapping
2Productivity
If granular product catalog level analysis is performed, then sales performance optimization is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing competitor pricing data in a structured format before analysis is needed. Competitor pricing data is continuously monitored and updated in a database, so when merchandising analysis is requested, the system can quickly retrieve and compare data without performing time-consuming data collection at the moment of analysis
Solution Approach 2:
The system implements dynamic data retrieval and filtering capabilities that adapt to user needs. The merchandising interface allows users to dynamically select product categories, price ranges, and time periods for analysis, with the system efficiently querying and processing only the relevant subset of data rather than analyzing the entire product catalog uniformly
3Loss of information
If competitor pricing data is integrated into the merchandising interface, then actionable insights for price adjustments are improved, but information processing complexity increases
Solution Approach 1:
The merchandising interface implements local quality by presenting different types of information at different levels of detail. The interface provides high-level summary views showing overall missed revenue opportunities by category, and allows drill-down to product-level details showing specific competitor price comparisons and recommendations. This localized information presentation reduces complexity by showing only relevant details at each viewing level
Solution Approach 2:
The system incorporates feedback mechanisms where the merchandising interface not only displays competitor pricing data and missed revenue calculations but also provides actionable recommendations and tracks the impact of price adjustments. The interface gives feedback on which products have the highest missed revenue potential and monitors changes in sales performance after price changes are implemented
Data Source
AI summary
Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method to present missed revenue based on competitor data. The program and method provide for receiving indication of a user request to present missed revenue for at least one product made available for purchase by a website; determining a set of competitor products for the at least one product, each competitor product in the set of competitor products having a competitor product price; identifying, based on the determining, a first competitor product within the set of competitor products with a lowest competitor product price; calculating a missed revenue for the at least one product based at least in part on a difference between the product price and the lowest competitor product price for a given time period; and causing display of the missed revenue in association with the at least one product.