Price Optimizer System for Retail Product Pricing Analysis
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Solution Overview
Problem
Retailers face challenges in accurately pricing products to maximize profit and manage cross-sell and substitution effects, as existing methods require manual data preparation and user intervention, and fail to automatically adjust prices based on real-time internal and external information.
Innovation Solution
The Teradata Price Optimizer system automatically creates statistical models within a data warehouse to estimate price elasticity and identify cross-sell and substitution effects, optimizing product prices across a set of products without manual data extraction, using a three-tier architecture that includes a graphical user interface, a JBoss Server, and a Teradata RDBMS, allowing for on-demand pricing adjustments and 'what-if' analyses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data preparation and user intervention are used in pricing analysis, then pricing models can be created, but the process is time-consuming and requires significant user effort
Solution Approach 1:
The system automatically performs data extraction, validation, and model creation without requiring manual user intervention. The pricing optimization system self-services by autonomously accessing data warehouse tables, validating data quality, and generating statistical models, thereby eliminating time-consuming manual preparation while maintaining model accuracy
Solution Approach 2:
The system performs preliminary data validation and extraction operations automatically before model creation. By pre-validating data quality and pre-extracting necessary information from the data warehouse, the system eliminates the need for manual data preparation steps while ensuring the accuracy required for reliable pricing models
2Measurement precision
If statistical models are manually created and analyzed, then pricing insights can be obtained, but the process lacks real-time responsiveness to market changes
Solution Approach 1:
The system continuously monitors market conditions, sales data, and competitive pricing through automated feedback loops. By implementing real-time feedback mechanisms that automatically update statistical models with new data, the system maintains pricing insight accuracy while responding rapidly to market changes without manual reanalysis
Solution Approach 2:
The pricing optimization system transitions from static manual model analysis to dynamic automated modeling. Statistical models are automatically retrained and updated in real-time as new market data becomes available, enabling the system to adapt pricing insights dynamically while maintaining analytical precision
3Loss of information
If comprehensive cross-sell and substitution analysis is performed manually, then product relationships can be identified, but the complexity of the analysis becomes unmanageable
Solution Approach 1:
The system replaces manual analytical processes with automated computational algorithms. Statistical modeling and data mining techniques automatically identify cross-sell and substitution relationships by analyzing purchase pattern data, thereby capturing comprehensive product relationship information while eliminating the unmanageable complexity of manual analysis
Solution Approach 2:
The system creates automated computational copies of manual analysis processes. By implementing algorithmic models that replicate and enhance human analytical capabilities, the system can process complex product relationship data at scale, maintaining complete information capture while reducing analysis complexity through automation
4Ease of operation
If pricing optimization is performed without automated systems, then manual control is maintained, but profit maximization opportunities are missed
Solution Approach 1:
The pricing optimization system performs automated profit maximization analysis while preserving manual oversight capabilities. The system autonomously analyzes pricing opportunities, calculates optimal prices, and identifies profit impacts, thereby capturing maximization opportunities that would be missed with purely manual processes while maintaining ease of operation through user-friendly interfaces and approval workflows
Data Source
AI summary
A data warehouse system and application which analyzes historical sales and product data contained within a data warehouse to determine the best product prices across a set of products for a retailer. The application analyzes historical sales and product data contained in a database to determine an opportunity score for multiple products sold by a retailer, the opportunity score indicating potential benefit to said business from changing the pricing of a product; and analyzes historical sales data contained in the database to determine an ability to change score for each product, the ability to change score indicating potential risk of lost sales for the retailer from changing the pricing of a product. Results of the analyses are displayed in a scatter plot graph with the graph axes being the opportunity scores and ability to change scores, respectively. Each product is represented by a point in the scatter plot, the coordinates of the point being the opportunity score and the ability to change score associated with the represented product. The scatter plot graph can be divided into quadrants, wherein products having favorable opportunity scores and favorable ability to change scores are displayed together in one the four quadrants.