Retail Promotional Planning System Using Sensory Data Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Retailers lack sophisticated analysis for planning sales and product promotions, often relying on intuition and failing to consider factors like cannibalization, cadence, associated sales, promotional elasticity, forward buying, and the impact of promotional sequences on long-term performance.
Innovation Solution
A computer system and method for generating an electronic retail plan that inputs sensory data, processes it to determine actionable features, models retailer operating states, and applies optimization parameters to select a simulated retail plan, considering factors like cannibalization, cadence, and promotional elasticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If retailers use traditional intuition-based product selection for promotions, then the selection process is simple and quick, but the promotional effectiveness and long-term performance are suboptimal due to ignoring factors like cannibalization, cadence, and promotional elasticity
Solution Approach 1:
The patent introduces a computer system as an intermediary between retail data and promotional decision-making. This system processes complex analytical factors (cannibalization, cadence, elasticity) that retailers cannot easily evaluate manually, translating raw data into actionable promotional insights while maintaining simplicity for the end user
Solution Approach 2:
The patent replaces manual intuition-based product selection with an automated computer system that applies sophisticated analytical models. This substitution eliminates the need for retailers to manually evaluate complex interrelationships between products and promotions, achieving high reliability without requiring expert knowledge from the retailer
2Productivity
If retailers promote the same products year over year (80-90% repetition), then the promotional process is efficient and consistent, but long-term performance deteriorates due to missed opportunities for optimizing product mix and ignoring promotional sequences
Solution Approach 1:
The patent performs preliminary analysis of promotional sequences and their cumulative effects before executing promotions. By evaluating how current promotions interact with past and future promotions, the system prevents long-term performance deterioration while maintaining efficient planning through automated sequence optimization
Solution Approach 2:
The patent introduces dynamic optimization that adapts promotional selections based on historical performance, current inventory conditions, and forecasted demand. This allows the system to vary product mix strategically over time rather than maintaining static 80-90% repetition, improving long-term performance while preserving planning efficiency through automation
3Measurement precision
If retailers consider multiple factors (cannibalization, cadence, associated sales, forward buying) in promotional planning, then the quality of promotional decisions improves, but the time and computational resources required increase significantly
Solution Approach 1:
The patent segments the complex analytical task into distinct computational modules that evaluate different factors (cannibalization, cadence, associated sales, forward buying) separately. This segmentation allows the system to process multiple factors efficiently by dividing the overall analysis into manageable components, reducing total planning time while maintaining high decision accuracy
Solution Approach 2:
The patent uses historical data and simulated scenarios to create virtual models of promotional outcomes before implementing actual promotions. By testing promotional strategies in a simulated environment using copied historical patterns, the system achieves high measurement precision without requiring extensive real-world testing time
Data Source
AI summary
A computer system and computer-implemented method for retail merchandise planning, including promotional product selection, price optimization and planning. According to an embodiment, the computer system for generating an electronic retail plan for a retailer comprises, a data staging module configured to input retail sensory data from one or more computer systems associated with the retailer; a data processing module configured to pre-process the inputted retail sensory data; a data warehouse module configured to store the inputted retail sensory data and the pre-processed retail sensory data; a state model module configured to generate a retailer state model for modeling operation of the retailer based on the retail sensory data; a calibration module configured to calibrate the state model module according to one or more control parameters; and an output module for generating an electronic retail plan for the retailer based on the retailer state model.