Probabilistic Feedback Attribution for Organic and Solicited Reviews
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Solution Overview
Problem
Businesses face challenges in determining the impact of solicitation on generating reviews and attributing feedback to specific individuals, particularly in online environments where identities are often unclear or fake, leading to difficulties in understanding their online reputation.
Innovation Solution
A probabilistic feedback attribution system that maps feedback items to individuals by analyzing historical interaction data from CRM systems and applying machine learning models to determine probabilistic confidence in attribution, using features such as name, vehicle, and interaction history to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If businesses reach out to customers to solicit feedback and generate reviews, then the quantity of reviews increases, but the ability to determine the impact of solicitation and attribute feedback to specific individuals deteriorates
Solution Approach 1:
The patent introduces an intermediary attribution system that acts as a mediator between feedback items and individuals. This system uses machine learning models to analyze multiple features (name, vehicle, interaction history, temporal patterns) and probabilistically determine the connection between feedback and customers, enabling precise attribution even when direct identification is unavailable.
Solution Approach 2:
The patent replaces manual or direct mechanical identification methods with automated machine learning-based probabilistic attribution. The system substitutes simple matching mechanisms with complex analytical models that process multiple data dimensions to infer customer feedback connections, thereby achieving high measurement precision without requiring direct customer identification.
2Ease of operation
If businesses use traditional feedback collection methods, then the process is simple, but the ability to understand online reputation and attribute feedback to specific individuals is insufficient
Solution Approach 1:
The patent implements preliminary actions by collecting and storing multiple types of data (customer information, interaction history, feedback items) before attribution is needed. The system pre-processes and organizes this data in a structured manner, enabling rapid and accurate probabilistic attribution when feedback needs to be analyzed, thus preventing information loss while maintaining operational simplicity.
Solution Approach 2:
The patent creates a universal feedback attribution system that handles multiple functions: identifying feedback sources, analyzing feedback content, determining attribution probability, and providing actionable insights. This multi-functional approach consolidates various operations into a single system, maintaining ease of operation while preventing information loss through comprehensive data processing.
Data Source
AI summary
Macro-Attribution of feedback to solicitation includes determining historical time series information pertaining to collected feedback items. It further includes determining historical time series information pertaining to feedback solicitation. It further includes generating, based at least in part on the historical time series information pertaining to the collected feedback items and the historical time series information pertaining to the feedback solicitation, a macro-attribution model usable to estimate an expected number of feedback items to be received for a time period. It further includes receiving a set of feedback items for the time period. It further includes using the macro-attribution model to determine, for the feedback items received for the time period, an estimate of an amount of feedback that was generated organically, and an estimate of an amount of feedback that was generated by solicitation.