Probabilistic Inference for Retail Customer Profile Mapping
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Solution Overview
Problem
E-commerce merchants face challenges in mapping in-store transactions to existing customer profiles, leading to duplicate profiles and artificial sparseness in customer-to-item relationships, which hinders targeted recommendations and customer understanding.
Innovation Solution
The proposed system uses a probabilistic inference framework and factor graphs to associate in-store transactions with customer profiles based on unique identifiers, leveraging credit card information and additional data sources to identify individual customers and households, thereby clustering customers effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If in-store transactions are mapped to customer profiles using traditional methods, then customer understanding and targeted recommendations can be improved, but duplicate profiles and artificial sparseness in customer-to-item relationships occur
Solution Approach 1:
The patent introduces a probabilistic inference framework as an intermediary between in-store transactions and customer profiles. This framework uses factor graphs to model uncertainties and relationships, allowing accurate mapping without creating duplicate profiles. The intermediary processes transaction data through probabilistic reasoning to reliably associate customers with profiles while maintaining data quality.
Solution Approach 2:
The patent changes the parameter of profile association from deterministic matching to probabilistic inference. By using probability distributions and factor graphs, the system can handle uncertainties in customer identification, transforming the mapping process from a binary match/no-match approach to a nuanced probabilistic framework that reduces duplicate profiles while improving customer understanding.
2Reliability
If probabilistic inference framework is used to map in-store transactions to customer profiles, then duplicate profiles are reduced and customer segmentation is enhanced, but system complexity increases
Solution Approach 1:
The patent segments the complex probabilistic inference system into manageable components using factor graphs. Each factor graph represents specific relationships between variables (transactions, customers, profiles), allowing the complex inference problem to be broken down into smaller, more tractable sub-problems that can be solved independently and then combined.
Solution Approach 2:
The factor graph serves as an intermediary structure that simplifies the implementation of probabilistic inference. By introducing this graphical model as a mediator between raw transaction data and customer profiles, the system manages complexity through a structured representation that makes the inference process more computationally feasible and easier to implement.
3Measurement precision
If credit card information and additional data sources are leveraged to identify individual customers, then customer identification accuracy is improved, but data processing requirements and computational resources increase
Solution Approach 1:
The patent applies partial action by selectively using credit card information and additional data sources based on the specific inference needs. The factor graph framework allows the system to incorporate only the necessary data sources required for each particular customer identification scenario, rather than processing all available data uniformly, thus reducing computational overhead while maintaining identification accuracy.
Data Source
AI summary
A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: receiving a query from a front-end device for one or more users mapped to a same payment option; generating, using a machine learning model, a first dataset comprising one or more classifications of one or more online users mapped to the same payment option as either (i) a single user with multiple user profiles or (ii) multiple users of a same household; generating, using a factor graph, a second dataset comprising first information of the one or more online users mapped to second information of one or more instore users; mapping at least one of the one or more online users to at least one of the one or more instore users based on the second dataset. Other embodiments are disclosed.