Price Optimization via Transaction Log Segmentation
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Solution Overview
Problem
Current promotion and base price optimization methods in brick and mortar retailers rely on backward-looking, aggregate historical data, which fails to account for unanticipated events and individual consumer behavior, leading to inefficient promotion strategies and inaccurate price setting.
Innovation Solution
The implementation of a forward-looking system that collects and analyzes transaction logs to calculate elasticities and generate optimal prices, using generalized linear models and D-optimal designs to test price variations in segmented subpopulations, allowing for real-time adjustment and validation of prices based on external factors like weather.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If backward-looking aggregate historical data is used for price optimization, then data collection is simple, but the accuracy of price setting and promotion strategy is insufficient
Solution Approach 1:
The patent segments the homogeneous aggregate historical data into heterogeneous transaction-level records with multiple dimensions (product, consumer, time, location, external factors). This segmentation enables precise analysis of individual consumer behaviors and unanticipated events, directly improving price setting accuracy while the systematic structure manages the complexity through organized data fields and relationships.
Solution Approach 2:
The system performs preliminary actions by collecting and structuring transaction logs with external factor data (weather, holidays, events) in advance before optimization calculations. This preliminary data preparation enables the forward-looking optimization model to accurately account for unanticipated events and consumer behaviors, improving price setting accuracy while the automated structure handles the complexity of data integration.
2Reliability
If forward-looking systems with multiple data collections are implemented, then price optimization accuracy improves, but system complexity and implementation cost increase
Solution Approach 1:
The patent creates a universal data collection framework that handles multiple data sources (transaction logs, external factors, consumer profiles) through a single integrated system architecture. This multi-functional system improves promotion strategy effectiveness by considering all relevant factors simultaneously, while the standardized structure manages complexity through consistent data processing routines and unified analysis models.
Solution Approach 2:
The system implements feedback mechanisms where transaction outcomes and consumer responses are fed back into the optimization model to continuously refine price and promotion strategies. This feedback loop improves reliability of promotion strategies by learning from actual consumer behaviors and unanticipated events, while the automated feedback processing manages complexity through systematic data iteration and model refinement.
3Loss of information
If transaction logs with external factors are analyzed, then consumer behavior insights improve, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and structuring transaction logs with external factor data (weather, holidays, events) before optimization calculations. This advance preparation ensures no consumer behavior information is lost, as all relevant data is captured and organized in advance, while reducing processing time during actual optimization by having data ready for immediate analysis.
Solution Approach 2:
The patent implements dynamic data processing that adapts to different analysis needs by selectively processing only relevant transaction logs and external factors for each optimization scenario. This dynamic approach maintains complete consumer behavior insights by preserving all data, while reducing processing time by focusing computational resources on the most relevant data subsets based on the specific optimization objectives.
Data Source
AI summary
Systems and methods for optimizing base pricing of products within a physical retailer are provided. Such systems and methods include first collecting transaction logs for products in a set of physical retail spaces. These logs are validated, adjusted and elasticities between the products are computed. The adjustment may be responsive to the day, by retailer and by a host of external factors (e.g., weather). The adjustment may also include a normalization and filtering out of inaccurate log data. Elasticity is calculated by generalized linear models. A set of constraints are then received and used, along with the elasticities.