ML Promotion Analytics System for Demand Forecasting
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Solution Overview
Problem
Conventional methods for creating promotions are time-consuming, inconsistent, and lack a systematic or statistically selective approach, often relying on ad-hoc decision-making and external help, which hinders optimization.
Innovation Solution
A computer-implemented method and system using machine learning models to analyze historical data and optimize promotion parameters, forecasting output analytics such as demand and price elasticity, and determining optimal promotional material configurations and layouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional ad-hoc methods are used to create promotions, then personnel can make decisions based on their judgement, but the process becomes time-consuming and inconsistent
Solution Approach 1:
The system enables self-service by automatically generating promotion parameters and analytics using machine learning models. The optimization model autonomously processes historical data and forecasts outcomes without requiring external help or manual adjustment, allowing the system to serve itself in creating optimized promotions.
Solution Approach 2:
The patent replaces the mechanical system of manual ad-hoc decision-making with an automated machine learning-based system. The optimization model and forecasting model substitute human judgement with algorithmic processing, eliminating the time-consuming and inconsistent nature of conventional approaches.
2Adaptability or versatility
If conventional ad-hoc methods are used to create promotions, then personnel can decide promotion aspects, but the approach lacks systematic optimization and statistical selectivity
Solution Approach 1:
The system incorporates feedback mechanisms by using historical promotion data and transaction history to train machine learning models. The forecasting model provides feedback on predicted outcomes, allowing the optimization model to adjust promotion parameters systematically based on statistical patterns rather than ad-hoc decisions.
Solution Approach 2:
The optimization model systematically changes promotion parameters such as discount levels, duration, and product selection based on statistical analysis of historical data. This replaces the lack of systematic parameter adjustment in conventional methods with data-driven parameter optimization.
3Ease of manufacture
If conventional methods rely on outside help and suppliers, then promotion creation can be accomplished, but the interests may not be aligned and optimization is hindered
Solution Approach 1:
The system eliminates the need for outside help by providing self-service capabilities through automated machine learning models. The system independently performs data analysis, optimization, and forecasting functions that previously required external suppliers, ensuring aligned interests through unified automated decision-making.
Data Source
AI summary
There is provided a method and system for generating an output analytic for a promotion. The method includes determining, using an optimization machine learning model trained or instantiated with an optimization training set, at least one determined parameter for the promotion which optimizes at least one of received input parameters, the optimization training set comprising received historical data; forecasting, using a promotion forecasting machine learning model trained or instantiated with an forecasting training set, at least one output analytic of the promotion, the prediction training set comprising the received historical data, the at least one received input parameter and the at least one determined parameter; and outputting the at least one output analytic to the user.