Demand Forecasting with Virtual Promotions and Real-Time Signals
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Solution Overview
Problem
Current methods for managing sales pipelines are inefficient and lack accuracy due to manual steps and inadequate demand forecasting.
Innovation Solution
A system and method for managing sales pipelines that includes generating virtual promotions based on historical data, calculating probabilities of consumer purchases, determining real-time demand, and summing predicted and real-time demands to optimize inventory and sales strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual steps are used in sales pipeline management, then ease of operation is maintained, but productivity and accuracy deteriorate
Solution Approach 1:
The system automatically performs demand forecasting, inventory optimization, and sales pipeline management tasks without requiring manual intervention. The computer program product executes algorithms that self-service the sales pipeline by processing historical data, calculating predicted demand, and generating real-time demand forecasts autonomously
Solution Approach 2:
Manual mechanical operations in sales pipeline management are replaced with automated computational systems. The patent substitutes human-operated manual processes with computer-based algorithms that calculate demand probabilities, process user search data, and generate forecasts through electronic computation rather than physical manual steps
2Measurement precision
If traditional demand forecasting methods are used, then device complexity is low, but measurement precision deteriorates
Solution Approach 1:
The patent introduces identification pairs as an intermediary mechanism between user search data and demand forecasting. These identification pairs serve as a bridge that connects search behavior data to product demand predictions, enabling more accurate forecasting through structured data mediation without requiring direct complex analysis of raw search data
Solution Approach 2:
The forecasting system segments demand prediction into two distinct components: predicted demand (based on historical data and probability calculations) and real-time demand (based on current user search data and identification pairs). This segmentation allows each component to be calculated and optimized separately, improving overall measurement precision while managing system complexity through modular architecture
Data Source
AI summary
Provided herein are systems, methods and computer readable media for managing a sales pipeline, and in some embodiments, generating demand based on real time demand and predicted demand. An example method comprises generating a virtual promotion, wherein the virtual promotion comprises a combination of a category or sub-category, a location, and a price range, calculating a probability that a particular consumer would buy the virtual offer in a predetermined time period, wherein the probability is generated at least based on historical data related to the particular consumer and one or more related consumers, determining an estimated number of units to be sold for the virtual offer as a function of at least the probability, the estimated number of units representing a predicted demand, calculating a real time demand, wherein the real time demand is generated based on a plurality of generated identification pairs for the predetermined time period, and determining, using a processor, total demand by summing the predicted demand and the real time demand.