Customer Response Model for Promotional Forecasting
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Solution Overview
Problem
Retailers face challenges in making effective promotional decisions due to a lack of objective scientific data, often relying on historical perceptions and intuition, leading to disappointing sales and unsold inventory, as they fail to understand the true drivers of customer buying decisions which can be influenced by various unforeseen factors.
Innovation Solution
A computer-implemented method for modeling customer response using data from customer purchases, providing expected values for customer traffic, product selection, and quantity, and solving for these parameters to create a customer response model that predicts the effectiveness of promotional programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If retailers rely on historical perceptions and intuition for promotional decisions, then the decision-making process is simple and quick, but the accuracy of predicting customer response and sales outcome deteriorates
Solution Approach 1:
The patent introduces a mathematical modeling system as an intermediary between historical data and promotional decisions. This system uses statistical models and algorithms to process raw sales data and generate predictive insights, acting as a mediator that transforms intuitive guesswork into data-driven recommendations without requiring retailers to directly analyze complex datasets themselves
Solution Approach 2:
The patent replaces the mechanical system of human intuition and historical perception with an automated computational modeling system. The mathematical models automatically process sales data, identify patterns, and generate predictions, substituting human cognitive processes with algorithmic analysis that can handle complex multivariate relationships beyond human capability
2Productivity
If retailers implement promotional programs without scientific data, then the implementation process is simple and fast, but the sales outcome and revenue generation deteriorate
Solution Approach 1:
The patent implements preliminary action by collecting and analyzing sales data before promotional decisions are made. The modeling system processes historical sales data, identifies customer response patterns, and generates predictive models in advance, allowing retailers to make informed decisions before committing to promotional programs rather than reacting after the fact
Solution Approach 2:
The patent incorporates feedback mechanisms where the modeling system continuously processes sales data from promotional programs, compares actual outcomes with predictions, and refines models based on observed customer responses. This feedback loop enables the system to learn from past promotions and improve future predictions, turning time investment in data collection into long-term productivity gains
3Measurement precision
If retailers use complex mathematical models to predict customer behavior, then the accuracy of forecasting improves, but the ease of operation and implementation deteriorates
Solution Approach 1:
The patent implements self-service by designing the modeling system to automatically perform data collection, processing, analysis, and interpretation without requiring user expertise in statistics or mathematics. The system serves itself by autonomously generating predictions and recommendations, allowing retailers to access sophisticated forecasting capabilities through simple interfaces without needing to understand the underlying complex models
Data Source
AI summary
A computer system models customer response using observable data. The observable data includes transaction, product, price, and promotion. The computer system receives data observable from customer responses. A set of factors including customer traffic within a store, selecting a product, and quantity of selected product is defined as expected values, each in terms of a set of parameters related to customer buying decision. A likelihood function is defined for each of the set of factors. The parameters are solved using the observable data and associated likelihood function. The customer response model is time series of unit sales defined by a product combination of the expected value of customer traffic and the expected value of selecting a product and the expected value of quantity of selected product. A linear relationship is given between different products which includes a constant of proportionality that determines affinity and cannibalization relationships between the products.