Promotion System Confidence Adjustment for Insufficient Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Merchants face challenges in determining whether feedback to promotion offers is insufficient and how to correct for this insufficiency, as existing methods struggle to analyze and improve promotion data effectively.
Innovation Solution
A method and system that analyze feedback from previous offers, using predictive models to estimate acceptance based on consumer and promotion attributes, determine if additional offers are needed, and adjust the number of additional consumers to target based on confidence thresholds, thereby improving the reliability of promotion data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If merchants send promotion offers to consumers, then business generation is improved, but the reliability of promotion data is insufficient due to inadequate feedback
Solution Approach 1:
The system performs preliminary analysis of feedback data before making promotion decisions. It calculates confidence levels for conversion rates and identifies attributes with insufficient feedback in advance, allowing merchants to proactively address data reliability issues before they impact business decisions
Solution Approach 2:
The system implements a feedback mechanism where consumer responses to promotion offers are collected and analyzed. This feedback is used to calculate confidence levels and identify insufficient data, which then informs subsequent promotion strategies and attribute selection
2Reliability
If merchants analyze all promotion feedback data, then data reliability is improved, but the complexity of analysis increases
Solution Approach 1:
The system segments the analysis by focusing on specific attributes (consumer attributes, promotion attributes, contextual attributes) rather than analyzing all data uniformly. It identifies and prioritizes attributes with insufficient feedback, dividing the complex analysis task into manageable segments
Solution Approach 2:
The system replaces manual data analysis with automated computational methods. It uses algorithms to calculate confidence levels, identify insufficient feedback attributes, and determine additional offers needed, substituting mechanical manual analysis with automated statistical processing
3Reliability
If merchants send additional promotion offers to increase confidence, then confidence in conversion rates is improved, but the loss of time and resources increases
Solution Approach 1:
The system determines the precise number of additional offers needed to achieve a target confidence level, rather than sending excessive offers. It calculates the minimum necessary action (additional offers) to reach the desired confidence threshold, avoiding unnecessary time and resource expenditure
Solution Approach 2:
The system dynamically adjusts promotion parameters (number of additional offers, target confidence levels, attribute selections) based on the analyzed feedback data. It changes these parameters optimally to achieve the desired confidence improvement with minimal time and resource investment
Data Source
AI summary
A promotion system for determining a deficiency in promotion data and correcting for the deficiency is disclosed. Issuing offers from a promotion program results in promotion data being generated. The promotion data may be analyzed to determine an acceptance rate of the offers. The promotion system may compare whether the acceptance rate is above a predetermined threshold, but has a confidence level that is less than a confidence rate threshold. In that event, the promotion system may issue additional offers in order to increase the confidence level associated with the acceptance rate by a predetermined amount.