Revenue Causality Analyzer for Price Management Attribution
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Solution Overview
Problem
Current systems for revenue causality analysis are inaccurate, unreliable, and inefficient, especially when dealing with multiple factors and large product sets, making it difficult to attribute changes in revenue effectively for price management.
Innovation Solution
A Revenue Causality Analyzer system that selects reference and comparison time periods, receives transaction data, prepares it by correcting errors and filling missing data, and attributes causality effects using equations for price, volume, mix, exchange, cost, dividend, and inventory effects, providing a detailed and accurate breakdown of revenue changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current computer systems are used for revenue causality analysis, then some measure of revenue causality attribution is provided, but the analysis is inaccurate, unreliable, and requires large processing resources
Solution Approach 1:
The patent segments the revenue causality analysis into distinct components: price effect, volume effect, mix effect, exchange effect, and cost effect. Each component is calculated separately using specific formulas, allowing for precise attribution while managing computational complexity through structured decomposition of the analysis process
Solution Approach 2:
The patent changes the analytical parameters by introducing a standardized framework with specific计算公式 for each causality effect. This transforms the analysis from rough estimations to precise calculations by defining exact mathematical relationships between revenue changes and their causal factors, improving accuracy without proportionally increasing complexity
2Ease of operation
If human intuition is used to estimate causal factors, then the analysis is simple to perform, but the results are only rough estimations and inaccurate
Solution Approach 1:
The patent replaces the mechanical process of human intuition and estimation with an automated computer-based calculation system. The system uses defined formulas to automatically compute price effects, volume effects, mix effects, exchange effects, and cost effects, eliminating the need for manual estimation while providing precise, reproducible results
Solution Approach 2:
The system enables self-service automated analysis where the computer automatically performs all causality attribution calculations without requiring human intervention for computation. The framework allows businesses to input their data and receive accurate causality breakdowns autonomously, maintaining simplicity while achieving precision
3Adaptability or versatility
If multiple causal factors are analyzed simultaneously, then comprehensive revenue attribution is achieved, but the complexity of the analysis increases significantly
Solution Approach 1:
The patent divides the comprehensive causality analysis into five distinct segmented components: price effect, volume effect, mix effect, exchange effect, and cost effect. Each segment is calculated independently using its own formula, allowing the system to handle multiple factors simultaneously while managing complexity through structured separation of analytical components
Solution Approach 2:
The patent creates a universal framework that can handle multiple types of causality factors through a standardized multi-functional approach. The same basic structure accommodates different effect types by applying appropriate formulas to each, providing comprehensive coverage without requiring separate complex analysis methods for each factor
Data Source
AI summary
A revenue causality analyzer that provides attribution of causality effects for changes in revenue. The analyzer includes a selector for selecting a reference time period and a comparison time period, a receiver configured to receive transaction data including pricing data and volume data about the products at reference and comparison times, a preparer that includes a missing data exchanger and data error corrector, an attributor for attributing causality effects including a price effect, a volume effect, a mix effect an exchange effect, a cost effect, a dividend effect, a loss effect, and an inventory appreciation effect, by analyzing transaction data through a causality equation; and an output. Price effect sums the product of change in price and volume of the products across the products and currencies. Volume effect multiplies change in volume by the revenue per product sold. Mix effect multiplies percent revenue change by the volume of products sold.