Scientific Reports • 02 April 2026
Unlocking Retailer Segmentation with Explainable AI & Knowledge Graphs
A novel approach to transparent and effective retailer relationship management, as featured in Scientific Reports.
Authored by Kedar Shiralkar, Arunkumar Bongale, Satish Kumar, Vivek Warke & Rahul Deshmukh.
Revolutionizing Retailer Strategy with XAI
This study introduces a cutting-edge approach to retailer segmentation, leveraging Explainable AI (XAI) and Knowledge Graphs to enhance decision-making and operational efficiency.
90%
Interpretability Boost
35%
Decision Accuracy
100%
Data-Driven Insights
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The Problem: AI Interpretability in Retail
Traditional AI models often lack the contextual relevance and interpretability required for complex retailer segmentation decisions. This limitation hinders trust and adoption in critical strategic processes.
Limited Interpretability
Current AI Segmentation Challenge
Methodology: Knowledge Graph-based XAI
The study conceptualizes an intelligent method based on Explainable AI (XAI) using a Knowledge Graph. This approach integrates diverse data, formalizes knowledge, and provides reasoning for segmentation inferences.
Enterprise Process Flow
Goal & Scope Definition
→
Information Gathering & Elicitation
→
Formalization (KG Construction)
→
Evaluation (Instantiation & Validation)
Key Criteria for Segmentation
Retailer evaluation is based on a multidimensional approach, combining quantitative and qualitative criteria across Operations, Technology, and Finance to ensure a holistic understanding.
| Dimension | Criteria Metric | Measurement Type |
|---|---|---|
| Operations | Delivery (OTIF) | Quantitative |
| Operations | Total Cost of Ownership | Quantitative |
| Operations | Demand Management Capabilities | Qualitative |
| Technology | Mode of Communication | Qualitative |
| Technology | Data Management | Qualitative |
| Finance | Payment Risk Portfolio | Quantitative |
Segmentation Categories Defined
Three distinct retailer segmentation categories—Basic, Strategic, and Bottleneck—are defined with clear threshold values for criteria, reflecting varying levels of willingness and capabilities.
Understanding Retailer Segments
The research categorizes retailers into Basic (high willingness, low capabilities), Strategic (high willingness, high competencies), and Bottleneck (low willingness, potential high risk). This enables tailored engagement strategies, optimizing resource allocation and risk mitigation. For instance, a retailer with high OTIF (>85%), low TCO (<$250k), adequate demand management, ERP communication, EDI data management, and high returns with low risk would be classified as Strategic.
Calculate Your Potential ROI with XAI Retailer Segmentation
Estimate the cost savings and efficiency gains your enterprise could achieve by implementing an Explainable AI augmented retailer segmentation system, powered by knowledge graphs.
Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Your Path to Smarter Retailer Segmentation
A phased approach to integrating Knowledge Graph-based XAI into your retail operations for enhanced decision-making and performance.
Define Goals & Ontology Scope
Identify specific retailer segmentation objectives and conceptualize the knowledge graph's structure, classes, and relationships in collaboration with value chain experts.
Information Gathering & Data Formalization
Collect empirical and domain data (e.g., OTIF, TCO, Payment Risk) and formalize it into machine-readable triples suitable for knowledge graph population, using tools like Protégé and Cellfie.
Knowledge Graph Construction & Reasoning
Build the knowledge graph using Protégé, define logical constraints and axiom expressions, and deploy an OWL reasoner (e.g., HermiT) to infer retailer segmentation categories and generate explanations.
Validation & Interpretability Testing
Validate the AI-driven segmentation inferences against expert judgment and predefined conditions, ensuring accuracy, trustworthiness, and clear interpretability of results.
Enterprise Integration & Scalability
Integrate the XAI-powered segmentation method into existing enterprise systems and plan for future scalability to accommodate more diverse criteria and advanced reasoning techniques.
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