Abstract
Retailer segmentation is the strategic process for retail firms as it optimizes channel management, thus significantly enhances the business performance. Retailer Segmentation based on diverse criteria such as operational efficiency, financial stability, and technological adoption enable retail firms to adopt custom engagement strategies, allocate resources effectively, and mitigate risks. Besides, organizing diverse criteria into broader dimensions reduces inherent complexity in strategizing the retailer segmentation decisions. Moreover, the use of combination of quantitative and qualitative criteria provides good contextual understanding of a retailer’s characteristics, thereby minimizing the bias in decisions. Industry 4.0 is revolutionizing complex strategic processes such as retailer segmentation using artificial intelligence (AI) through data-driven, automated, and predictive capabilities. However, existing AI models often lack contextual relevance and interpretability, thereby limiting their applicability in complex retailer segmentation tasks. This study intends to address these limitations by conceptualizing an intelligent method based on Explainable AI (XAI), utilizing knowledge graph. Empirical data is collected and used in this study to conceptualize the retailer segmentation method. This method uncovers the hidden knowledge in the data about a retailer’s performance using Knowledge Graph model and infers appropriate segmentation category for a retailer. Further, this method adopts a reasoning technique to generate interpretable explanations for inferences. This study helps practitioners better understand how to use Knowledge Graph models for retailer segmentation, which can be extended and scaled in future research.
Keywords: Explainable AI, Retailer segmentation, Artificial intelligence, Knowledge graph, Retailer relationship management
Subject terms: Engineering, Mathematics and computing
Introduction
The role of retailers is pivotal in strengthening the retail distribution network through a wider market reach, which in turn increases retail firm’s brand value and stimulates sales growth. Therefore, retailer relationship management has gained focused attention by retail organizations in last few years. Nike, a leading worldwide retail brand, has been progressively shifting towards a direct-to-consumer (DTC) distribution strategy. Yet around 58% of the total revenue is still coming from the retail distribution network, which signifies the importance of retailers in the retail value chain1.
According to the report published by Forbes, Nike is working with around 30,000 retailers worldwide. However, the company has deliberately chosen a core group of 40 retailers, called “differentiated retailers”, through which it will strengthen its retail distribution. The company is expecting to get 80% of its revenue from retail distribution through these select partners2. This strategic move of Nike demonstrates how retail firms seek competitive advantage by adopting focused and strategic partnerships with a limited number of high performing retailers. Consequently, retailer segmentation has become a decisive method for retailer relationship management. Moreover, an effective segmentation strategy can lead to the efficient distribution of resources and reduction in operational costs, thus enhancing the overall performance of the distribution network3.
The criteria act as quality checks to assess retailers during retailer segmentation. Therefore, the criteria chosen from diverse domains of the retail value chain should ensure objective evaluation of retailers. Furthermore, the selected criteria should provide measurable, accessible and actionable information to ensure robust retailers segmentation decisions4. Empirical evidence shows that the approach of segregating diverse criteria into a manageable number of dimensions reduces complexity in retailer evaluation, thereby improving segmentation accuracy. This multidimensional approach proved to be providing substantial benefits, with estimated 30% to 50% improvement in customer retention resulting in accelerated and sustainable revenue growth5.
Retailer segmentation has been studied extensively in both academic and industry practice. Many retail firms have been using the Purchase Portfolio Matrix, or Kraljic Matrix to classify their retailers6. Although this method is popular in industry, it has some major drawbacks when it comes to creating good segmentation strategies7. A lot of research has been done to find better ways, like using multi-criteria decision-making methods, to improve upon the Kraljic Matrix. However, these alternatives also have their own problems when it comes to giving accurate and useful segmentation results8.
In recent times, there have been many success stories in adopting artificial intelligence (AI) into commercial operations. Therefore, retail firms find great potential in leveraging AI for highly complex decision-making processes such as retailer segmentation. The ability of AI applications to bring human-like judgements analyzing the data for diverse criteria may enable retail firms to automate retailer evaluation activities, thereby improving efficiency and cost-effectiveness in retailer segmentation decision-making process9. Thus, application of AI for retailer segmentation has transformative potential within the retail sector, enabling a more holistic and data-driven strategy for retail optimization10.
Therefore, this study intends to conceptualize an intelligent method for retailer segmentation using artificial intelligence that employs diverse sets of criteria and provides trustworthy inferences for effective retailer segmentation.
Literature review
The objective of this study is to discover the criteria for retailer segmentation and suitable AI model for the conceptualization of AI-driven retailer segmentation method. Therefore, in this section, the retailer segmentation criteria that were discovered during systematic literature review are discussed. Besides, the different AI models for retailer segmentation and their limitations are also discussed.
Criteria for retailer segmentation
Delivery
Thanaraksakul and Phruksaphanrat11 analyzed 76 scholarly articles during the literature review and concluded that delivery performance of the retailer is crucial for retail business success. Early or Late deliveries leads to inefficiencies and result in increased costs across the retail distribution network12.
Niemi et al.13 in their study provided the metrics on the impact of delivery delays and indicated that even minor disruptions, such as a two-week delay, can reduce sales by approximately 10%, while delays exceeding 45 days may lead to an estimated 40% loss in projected sales. This suggests that the implementation of resilient and timely delivery practices helps mitigate risks associated with retail distribution chain disruptions14. The On Time In Full (OTIF) metric objectively measures the delivery performance of a retailer and hence, it is often considered as a key performance indicator within supply chain management15. This metric is usually measured in terms of percentage and assessed against predefined benchmarks. Equation 1 represents the formula for OTIF metric and is calculated as the ratio of number of deliveries completed both on time and in full and the total number of deliveries16.
 |
1 |
Cost
Total Cost of Ownership (TCO) is one of the important criteria which includes not only the purchase price of goods or services but also the cost associated with broader set of activities required to manage the retailer relationship. Therefore, this criterion plays a significant role in measuring operational performance of the retailer17. Van den Abbeele et al.18 revealed that access to retailers’ cost of ownership information boosts firm’s bargaining power with the retailers, thereby enhancing the retail distribution chain performance. The purchase price typically accounts for only approximately 50% of the total cost of ownership. This indicates that significant cost is spent on retailer relationship management activities, which can be reduced through strategic retailer selection19.
The computation of total cost of ownership is inherently complex and cannot be directly derived from raw data. However, firms can use commercial TCO tools to estimate retailer-associated costs with greater precision20. The total cost of ownership represents the monetary value of the expenses incurred. Therefore, it is usually measured in terms of currency.
Demand management
Demand Management is the strategic process that involves forecasting customer demand and aligning it with key supply chain functions such as production, procurement, and distribution. The demand management aims to reduce the demand variations and enhance the operational flexibility, thereby improving the responsiveness of the supply chain21. The adoption of demand management techniques by retail partners significantly enhances the efficiency of retail distribution, which in turn offers greater business sustainability and profitability22.Thus, demand management is strategically important criterion for retail firms. However, not much work has been done to develop computational methods to measure this criterion quantitively, thereby limiting its applicability in retailer performance evaluation.
Mode of communication
The firm-retailer relationship plays a vital role in retail business success. The quality and reliability of information sharing is important for firm-retailer relationships. Kankam et al.23 revealed that trusted partnership between a firm and retailer can be built effectively by timely sharing of true information. Therefore, the mode of communication plays critical role in firm-retailer interactions.
According to Ambrose et al.24, selecting the right mode of communication is heavily influenced by the stage of relationship development between the firm and the retailer. Verbal communication prior to the formal contracting phase fosters collaboration within the retail chain, leading to more efficient outcomes25. However, adopting modern ICT techniques during operational phase facilitates timely information sharing adhering to quality and privacy standards, thereby enhancing transparency and reducing transaction costs26. Therefore, adoption of electronic mode of communication in retail sector has increased significantly in recent years.
Data management
Wu et al.27 uncovered that the use of disparate information management systems within the retail distribution network cause information quality issues and information sharing delays. This leads to product authenticity and traceability issues, resulting in increased costs due to material wastage. Moreover, inaccurate and untimely information sharing introduces inefficiencies in every stage within the retail distribution network. The adoption of unified information management systems ensures information related to the retail distribution network can be stored, integrated, and accessed with high reliability and efficiency, thus reducing inefficiencies in the retail distribution network.
Retail firms and retailers heavily rely on spreadsheets and physical documents, such as ledgers for data storage and retrieval. However, these data management techniques pose substantial risks such as errors in creation, maintenance, or utilization of data, causing operational and financial inefficiencies28. The next-generation Enterprise Resource Planning (ERP) systems can address these risks by offering unified information management features such as data immutability, transparency, and centralized data governance. Therefore, these systems gained significant attention from retail firms in recent years29–31.
Finance
Increased macroeconomic uncertainty and volatility in global markets heavily burdens retailers financially32. Modern retail distribution networks are complex and interdependent, comprising multiple layers and stakeholders. Therefore, any financial disruption affecting the retailer can quickly propagate throughout the entire system, leading to significant business interruptions33. Both retailers and retail firms often experience power imbalances and interdependencies which can adversely impact their operational, contractual, and financial liabilities34.
Retail firms often use their extra funds to offer trade credit to weak retailers to gain double marginalization benefits. This practice can improve retailers’ short-term working capital, but it affects their long-term cash flow. This makes the retailers financially unsustainable for the long run, leading to business disruptions during challenging times. Therefore, retail firms should focus on achieving the balance between their financial objectives and maintaining long-term, sustainable relationships with the retailers35. Some researchers suggested carrying out payment risk portfolio assessment of the retailers and proposed models to assess retailers’ financial health before offering any supply chain finance solution36,37.
AI in retailer segmentation
In retail sector, adoption of artificial intelligence has significantly enhanced operational efficiency and strategic decision-making due to automation of numerous complex tasks38. AI systems boost decision-making accuracy and mitigate risks by fostering more personalized and effective partnerships between firms and retailers. Therefore, Industry 4.0 is revolutionizing retailer relationship management approaches with AI39. The modern AI systems leverage advanced methodologies such as predictive analytics, machine learning, natural language processing, and Explainable AI (XAI), to address the multifaceted challenges of retailer relationship management40.
Several AI tools have been developed employing supervised machine learning models to address segmentation decision challenges within the retail value chain41–43. However, the inferences from these models are hard to trust for the decision-makers due to their limited interpretability. Retailer segmentation, being a strategic decision-making process, it is crucial to comprehend the rationale behind an AI model’s inferences. XAI addresses this problem by providing reasoning for those inferences. Therefore, XAI has emerged as a prominent area of research in recent years44,45.
The knowledge graph model provides a semantic framework that integrates data from diverse data sources and represents knowledge elements in a structured, machine-readable format46. Knowledge graph provides structured information in the form of interconnected nodes and relationships for machines to discover hidden knowledge and use this knowledge to generate meaningful responses to user queries through intuitive interfaces47. Thus, XAI systems using Knowledge Graph model provide decision-makers with relevant and contextually rich information for informed decision-making48.
Fujitsu has successfully employed Knowledge Graph model in their Explainable AI (XAI) system implementation for applications such as assessing mental health risks in patients and advancing genomic medicine49. Moreover, scholars have attempted to use knowledge graph-based XAI methods in various business domains, that includes fraud detection in financial statements within supplier–customer networks and in decision support systems for supplier recommendation50,51. These application examples demonstrate the popularity of Knowledge Graph based XAI systems to address complex business decision-making challenges with enhanced reliability and interpretability.
Concept of Knowledge graph
The concept of the Knowledge Graph (KG) was introduced by Google in 201252. A Knowledge Graph comprises of data triples to semantically represent domain-specific knowledge in a structured, machine-readable format. Each triple contains three components: a subject, a predicate, and an object. The subject denotes the entity or resource being described, the predicate defines the nature of the relationship, and the object represents the entity or value to which the subject is related.
Figure 1 illustrates the example of the triple for the statement "Tom is a doctor".
Fig. 1.
Triple.
Each triple within a Knowledge Graph is encoded into a continuous low-dimensional vector space. This knowledge graph encoding generates learned representations to discover hidden knowledge53,54. Furthermore, Knowledge Graphs integrate complex structured and unstructured data into an ontology-based framework, enabling logical reasoning over the data to infer new knowledge or to identify inconsistencies within the knowledge base55.
An ontology provides a formal structure to KG models and comprises of concepts, properties, and axioms. Axioms impose constraints on ontology’s structure, thereby representing business rules that facilitate the generation of new knowledge from existing information encoded within the ontology56. World Wide Web Consortium (W3C) introduced Web Ontology Language (OWL) to express Ontologies. OWL employs XML/RDF syntax to define ontologies in a manner that is both machine-readable and suitable for automated reasoning. OWL-based ontologies can be hosted on web servers like standard web documents, enabling interoperability with other ontologies, web services, and intelligent software agents57.
Knowledge graph reasoning
Knowledge Graph reasoning provides human-like intelligence by generating inferences at different levels and providing reasoning for these inferences, thus mimicking the human decision-making capability. KG acts as a repository of human expertise and domain knowledge, while the reasoning mechanism extracts new knowledge from the existing knowledge embedded within the graph. This enables advanced features like knowledge-based questions answering and intelligent recommendation, providing informed decisions58.
Abicht59 evaluated 73 OWL reasoners along with 22 systems incorporating third-party reasoners. The study concluded that the majority of these tools are functional and actively maintained for use in diverse decision-making applications. A list of these usable and maintained OWL reasoners is presented in Table 1.
Table 1.
List of usable and maintained OWL reasoners.
| OWL Reasoner |
|---|
| AllegroGraph |
| Arachne |
| BaseVISor |
| BORN |
| CEL |
| ElepHant |
| ELK |
| Expressive Reasoning Graph Store (IBM) |
| EYE |
| EYE.js |
| HermiT |
| Jcel |
| JFact |
| Konclude |
| LiFR |
| LiRoT |
| Ontop (with Query reasoner) |
| Openllet |
| OWLRL |
| RDFox |
| RDFSharp.Semantics |
| Reasonable |
| TRILL (and TRILLP and TORNADO) |
| Vampire |
| Vlog |
| Whelk |
Tools to build Knowledge graph
Numerous open-source tools are available for constructing and managing knowledge graphs. Some of the popular tools in this list include Onto-Studio, TopBraid Composer (Free Edition), SWOOP, Protégé, and OntoEdit. Among these, Protégé is the most widely adopted open-source knowledge graph development tool which holds approximately 70% of the market share60,61.
In Protégé, an intuitive user interface is provided for interactive editing of knowledge graphs. Protégé also supports various visualization plugins, such as OntoViz, which facilitate the graphical representation of ontological structures. Besides, Protégé has a wide range of extensible plugins, including reasoning engines, querying tools, multimedia integration, and problem-solving modules, thereby enhancing its utility for diverse knowledge engineering applications62,63. Various OWL reasoners, including ELK and HermiT are available in Protégé by default and have a plugin for FACT++ reasoner. All these reasoners are used more often and cited more often in the literature64.
Additionally, Protégé includes the Cellfie plugin, which facilitates the extraction of data from Excel spreadsheets to generate instances and factual assertions for populating ontologies, thereby supporting the construction of knowledge graphs65. Protégé also provides a plugin for SPARQL, a semantic web query language that enables the inference of new information from existing knowledge within the knowledge graph66. These features position Protégé as a comprehensive platform capable of supporting the implementation of explainable artificial intelligence (XAI) methods grounded in knowledge graph technology.
Methodology
Figure 2 outlines the methodology employed to prototype our knowledge graph-based XAI approach for retailer segmentation.
Fig. 2.
Summary of methodology.
Goal and scope definition
This study is intended to conceptualize a knowledge graph-based explainable AI method for retailer segmentation for academic purposes while ensuring its applicability and relevance to industry contexts. Therefore, the scope of the study is limited to developing prototype of this method using Protégé Desktop v5.6.9 software downloaded from https://protege.stanford.edu/software.php#desktop-protege. The study utilized the HermiT reasoner, natively supported by Protégé, to achieve robust and reliable inferences.
Three-member team of value chain experts was formed to get assistance in defining the scope of the study and discover the various elements of XAI based intelligent method for retailer segmentation. The team of value chain experts comprises of two value chain experts with experience > 15 years and one techno-functional expert with experience > 20 years. Focused interviews were carried out with this team at different stages of methodology to seek their advice.
According to value chain experts, grouping criteria into broader dimensions offer abstract and meaningful inferences providing a good holistic view about retailer performance. Therefore, the criteria discussed in the literature review were categorized into broader dimensions, in consultation with the team of value chain experts. Moreover, value chain experts advised to use combination of quantitative and qualitative metrics for segmentation criteria to enable trustworthy contextual understanding of retailer behavior.
The base data for metrics like OTIF and Cost can be obtained from system databases directly. However, converting the Cost metric value from numbers into distinct qualitative categories gives more contextual understanding of retailer characteristics. Similarly, metric like Demand Management Capabilities cannot be directly quantified using a single metric. Instead, they require comprehensive analysis involving multiple indicators such as forecast attainment, fill rate, and inventory turnover. Therefore, the value chain experts advised adopting a qualitative assessment approach for demand management capabilities criteria. For the sake of simplicity in this research study, this evaluation is operationalized using two qualitative categories viz. Adequate and Inadequate.
The literature review advocates that Mode of Communication criterion is inherently a qualitative in nature and, as such, does not necessitate the extraction of numerical data from system databases. Accordingly, in this study, the criterion was evaluated based on the value chain expert’s selection from a predefined set of non-numerical categories, including Phone, Email, ERP, and Voice Meeting.
Moreover, the literature review indicates that Data Management criterion is also qualitative in nature. Therefore, in this study, the criterion is evaluated through the value chain expert’s selection from a predefined set of non-numerical options, that includes Spreadsheet, EDI, ERP, and Physical Ledger. Table 2 illustrates the list of dimensions with retailer segmentation criteria and their measurement type.
Table 2.
List of dimensions with retailer segmentation criteria.
| Dimension name | Description | Criteria name | Measurement type |
|---|---|---|---|
| Operations |
includes criteria that represent operational reliability of the retailer |
Delivery | Quantitative |
| Cost | Quantitative | ||
|
Demand Management Capabilities |
Qualitative | ||
| Technology |
includes criteria related to technology readiness of the retailer |
Mode of Communication | Qualitative |
| Data Management | Qualitative | ||
| Finance | includes criteria related to retailer’s financial risk | Payment Risk Portfolio | Quantitative |
Retailer segmentation category definitions
In practice, retail firms often define multiple categories within their retailer segmentation strategies. However, the objective of this research study is limited to conceptualization of the intelligent method for retailer segmentation using XAI. Therefore, only three segmentation categories, viz. Basic, Strategic, and Bottleneck are considered in this research study in consultation with the team of value chain experts.
According to value chain experts, factors such as retailer’s willingness and capabilities are crucial to define threshold values for different criteria in retailer segmentation. Further, they emphasized that willingness should be prioritized before capabilities for defining the boundary conditions for different segmentation categories because capabilities cannot be leveraged effectively without willingness.
Experts further highlighted that criteria such as Payment Risk portfolio, Delivery and Total cost of ownership reflect retailer’s willingness. For example, the payment risk portfolio criterion indicates the extent to which a retailer is willing to tolerate financial risk. Delivery reflects willingness to adapt to operational challenges to meet business goals whereas Cost (TCO) represents willingness to focus on long-term value creation rather than short-term cost minimization. Thus, these criteria collectively reflect retailer’s behavioral construction. On the other hand, criteria such as demand management capabilities, mode of communication and data management represent retailer’s capabilities because they capture the retailer’s internal resources, systems, and technical competencies that enable the execution of operational and informational activities in the retail chain.
Value chain experts further stated that retailers belonging to Basic segmentation category show high willingness but often lack capabilities to perform activities to improve retail chain efficiency. These retailers have a risk-averse attitude and prefer to work in predictable market environments. In contrast, Bottleneck retailers often lack willingness irrespective of their capabilities and therefore may sometimes incur negative returns or may pose high risk with low returns. On the other hand, strategic retailers exhibit strong willingness and competencies to grow the business. These retailers can generate high returns and have strong competencies to handle market fluctuation risks effectively. Therefore, criteria threshold values for different retailer segmentation categories were defined accordingly.
For quantitative criteria such as OTIF and Cost, the value chain experts relied on their practical judgment and experience. They carefully assessed each retailer’s behavioural patterns through the interpretation of the retailer’s overall payment-risk profile. This informed, experience-based interpretation allowed them to set thresholds that reflect not just numbers, but the underlying business realities behind those numbers.
Table 3 illustrates the criteria threshold values defined for different segmentation categories in consultation with the value chain expert.
Table 3.
Criteria Constraints for retailer segmentation categories.
| Dimension name | Criteria name | Segmentation category | ||
|---|---|---|---|---|
| Strategic | Basic | Bottleneck | ||
| Operations | Delivery | OTIF > 85% | OTIF > 85% | OTIF <= 85% |
| Total Cost of Ownership | TCO < 2,50000 USD | TCO < 2,50000 USD |
TCO> 2,50000 USD |
|
| Demand management capabilities | Adequate | Inadequate | Adequate/Inadequate | |
| Technology | Mode of communication | [E-mail, ERP] | [Phone, Voice Meeting] | Any |
| Data Management | [EDI, ERP] | [Spreadsheet, Ledger] | Any | |
| Finance | Payment risk portfolio | High Returns and Low Risk) or (High Returns and High Risk) | Low Risk and Low Returns | Negative Returns or (High Risk and Low Returns) |
Information gathering and elicitation
Data collection and elicitation for finance dimension
According to value chain experts, Payment practices data provides valuable insights for assessing retailer’s payment risk portfolio. However, this data is typically classified as confidential within retail firms due to compliance needs. Therefore, in this study, the open-sourced Payment practices dataset from Kaggle platform was obtained. This dataset includes attributes such as Retailer Name, Payment period, Average time to pay, % paid invoices within agreed terms and other contractual terms. The dataset has 24225 records and 1008 unique retailers.
Value chain experts indicated that attributes such as Average time to pay and % paid invoices from the payment practices dataset are good indicators to assess retailer’s payment risk. This is because these indicators indirectly influence the profitability or value assets of the retail chain. Therefore, these two indicators were considered for retailer’s payment risk portfolio assessment in this research study. In order to ensure the reliability of the outcome of this study, the open-source payment practices dataset is refined with the help of value chain experts in this study. The payment practices data for 19 retailers is prepared by anonymizing retailer names, improving the values for two payment performance indicators and removing irrelevant attributes.
The team of value chain experts have highlighted that the retailer risk portfolio in the form of Risk vs Returns provides actionable insights for the decision-makers. Shiralkar et al.36 have presented the payment risk portfolio assessment method using Modern portfolio theory. This method provides retailer’s payment risk portfolio in the form of Risk vs Returns with good reliability. Therefore, the risk assessment tool was developed to evaluate retailer’s payment risk portfolio based on this method using Python programming language.
Data collection and elicitation for operations and technology dimensions
Information gathering for Operations dimension involves collecting data for Delivery, TCO and Demand management capabilities criteria.
The questionnaire was designed to gather qualitative and quantitative data for these criteria and comprises of combination of objective and subjective type questions. The questionnaire was then shared with the team of value chain experts to seek their responses so that reliable data can be obtained for this study. The questionnaire responses were then converted into the dataset manually in the form of an excel spreadsheet. This excel spreadsheet has data for 19 retailers and was used for formalization of XAI based intelligent method for retailer segmentation. Table 4 represents a snapshot of output dataset generated during information gathering and elicitation in this study.
Table 4.
Snapshot of output dataset generated during information gathering and elicitation.
| Company | Returns% | Risk% | Cost | OTIF% | Demand mgmt. capabilities |
Mode of comm. | Data mgmt. |
|---|---|---|---|---|---|---|---|
| Retailer 01 | 0.924 | 6.5153 | 21800 | 90.5 | Inadequate | Phone | Spreadsheet |
| Retailer 02 | 31.581 | 75.528 | 16000 | 95 | Adequate | ERP | ERP |
| Retailer 03 | 8.1396 | 19.979 | 35900 | 87.2 | Inadequate | Voice Meeting | Spreadsheet |
| Retailer 04 | -31.362 | 16.624 | 310000 | 39.3 | Inadequate | Ledger | |
| Retailer 05 | -7.097 | 8.337 | 760000 | 55.5 | Inadequate | Voice meeting | Ledger |
| Retailer 06 | -16.878 | 13.285 | 340000 | 27.6 | Inadequate | Phone | EDI |
| Retailer 07 | 2.446 | 3.288 | 237000 | 87 | Inadequate | Phone | Spreadsheet |
Formalization
In the Formalization stage, the knowledge graph was constructed for the retailer segmentation domain. This subsection begins with a brief overview of the fundamental concepts of knowledge graphs, followed by a discussion on how the knowledge graph for retailer segmentation was developed using the information gathered in the preceding section.
Fundamental concept of Knowledge graph and reasoning
A knowledge graph consists of heterogeneous graph Շ, which represent data and Ontology Ο, which represent schema. A knowledge graph also has predicate logic which represents a collection of facts. These components are discussed in detail below.
Heterogeneous graph can be represented as Շ = (Ѵ, Ɛ) where Ѵ is set of concepts/ entities and Ɛ c ⱱxⱱ is the set of relations. Each pair of entity ⱱ1, ⱱ2 ∈ Ѵ and the relation between them 
can be represented using triplets (ⱱ1, ∈, ⱱ2), which are used in predicate logic67.
Ontology can be represente d as
, in which 
represents the set of entity types and ℜ is the set of relation types. Mathematically, ontology can be defined as a function that maps each entity in a heterogeneous graph u ε Ѵ to a particular entity type 
,and pair of entity types 
to possible relation type ℜε67. For example, in retailer segmentation ontology, entity represents objects such as retailer, segment and objects for different dimensions whereas relation type represents connection between these entities.
Predicate logic represents set of all possible pair of entities and the relations that link them, which are called triplets ((ⱱ1,Ꜫ,ⱱ2 ) for all ⱱ1, ⱱ2 Ꜫ Ѵ and 
. Triplet (ⱱ1, Ꜫ, ⱱ2) can also be stated as predicate logic statement rε = (ⱱ1, ⱱ2) with satisfiability value as True of False67. For example, each retailer has assigned to a segment type Basic, can be represented in the form of triplet containing two concepts or entities (Retailer, BasicSegment) and one relation type (hasAssigned). If the satisfiability of this statement is True, then a given retailer instance really belongs to segment type Basic while False means the given retailer does not belong to segment type Basic.
Knowledge graph reasoning infers which of the False triplets are due to missing data in the input data, which are supposed to be True in reality. Therefore, Knowledge graph reasoning capability can be used to answer competency questions effectively. For example, if we would like to know whether a Retailer01 instance of retailer belongs to segment type Basic, the query can be posed as a predicate logic statement hasAssigned (Retailer 01, Basic). The knowledge graph reasoner performs reasoning activity to find out if it is True or False.
Protégé tool is employed for the formalization of retailer segmentation Knowledge graph in this research study. In Protégé, Ontology is created using OWL based semantics and stored in RDF format. OWL ontology has various elements that define the ontology. These elements are discussed below.
Classes
Represents types of things. Examples for class Person, Car, Book etc.
Object properties
Used to create relationship between two classes. Thus, object property relates an instance of a class with instances of other class. Some examples of Object properties are hasParent, owns, teaches.
Data properties
Relates an instance of a class to literal data (e.g. string, number, datetime etc.). For example, hasAge, hasName, Birthdate.
Individuals (Instances): Specify objects of a class. For example, John is an instance of class Person.
Axioms
Statement that defines facts or constraints. There are three types of axioms.
Class Hierarchies: Student is subclass of Person
Property restrictions: hasAge must be an integer
Domain and range properties
Restrictions
Used to define classes based on property value or constraints. Example: A Parent is a person who hasChild some Person. There are three types of restrictions viz. quantifier restrictions, hasValue restrictions and Cardinality restrictions.
Retailer segmentation knowledge graph
The network of triplets was formed for retailer segmentation knowledge graph to represent complex knowledge in structured and machine-readable way. This helps in the abstraction of complex knowledge into core elements of retailer segmentation domain to build efficient and scalable knowledge graph. The network of triplets for retailer segmentation knowledge graph is illustrated in Figure 3.
Fig. 3.
Network of triplets for retailer segmentation knowledge graph.
The knowledge graph consists of different class types to represent complex domain knowledge in machine readable form for inferences with precise reasoning. The definitions for different class types are provided below.
Primitive class
A primitive class is defined only by a name. This class may or may not have data properties but no necessary and sufficient conditions.
Restricted class
A restricted class has data properties or inherits data properties from parent class and has necessary and sufficient conditions (restrictions/ logical expressions).
Inferred class
An inferred class is not explicitly assigned to an individual in your knowledge graph. Instead, it is deduced automatically by a reasoner based on logical definitions (axioms, restrictions, rules).
The retailer segmentation knowledge graph was implemented using Protégé software in this research study. Table 5 shows a list of classes for retailer segmentation knowledge graph implemented in this study.
Table 5.
List of classes with data properties implemented in retailer segmentation knowledge graph.
| Sr. No | Class Name | SubclassOf | Class type | data property | Data property type |
|---|---|---|---|---|---|
| 1 | Retailer | owl:thing | Primitive | hasName | xsd:string |
| 2 | Technology | Owl:thing | Primitive | ||
| 3 | ModeOfCommunication | Technology | Primitive | hasModeOfCommunication | xsd:string |
| 4 | DataManagement | Technology | Primitive | hasDataManagement | xsd:string |
| 5 | Operations | Owl:thing | Primitive | ||
| 6 | Delivery | Operations | Primitive | hasOTIF | xsd:decimal |
| 7 | DemandManagementCapabilities | Operations | Primitive | hasDemandManagement | xsd:string |
| 8 | Cost | Operations | Primitive | hasCost | xsd:decimal |
| 9 | Finance | owl:thing | Primitive | ||
| 10 | PaymentRiskPortfolio | Finance | Primitive | hasRisk | xsd:decimal |
| 11 | PaymentRiskPortfolio | Finance | Primitive | hasReturns | Xsd:decimal |
| 12 | HighRisk | PaymentRiskPortfolio | Restricted | ||
| 13 | LowRisk | PaymentRiskPortfolio | Restricted | ||
| 14 | HighReturns | PaymentRiskPortfolio | Restricted | ||
| 15 | LowReturns | PaymentRiskPortfolio | Restricted | ||
| 16 | NegativeReturns | PaymentRiskPortfolio | Restricted | ||
| 17 | Segment | owl:thing | Primitive | ||
| 18 | Basic | Segment | Inferred | ||
| 19 | Strategic | Segment | Inferred | ||
| 20 | Bottleneck | Segment | Inferred |
Table 6 illustrates the logical constraints or restriction expressions applied for restricted classes of PaymentRiskPortfolio class in Protégé to define retailer segmentation ontology.
Table 6.
Restricted classes with logical constraint expressions in Protégé.
| Class name | Logical constraints expression |
|---|---|
| HighRisk | hasRisk some xsd:decimal [> 25] |
| LowRisk | hasRisk some xsd:decimal [<= 25] |
| HighReturns | hasReturns some xsd:decimal [> 25] |
| LowReturns | hasReturns some xsd:decimal [<= 25 and >0] |
| NegativeReturns | hasReturns some xsd:decimal [<= 0] |
Table 7 represents the axiom expressions implemented for each segmentation category. These segmentation categories are implemented as inferred classes in Protégé. The HermiT reasoner in Protégé infers the segmentation category for each retailer based on the necessary and sufficient conditions defined in axiom expressions for different segmentation category classes.
Table 7.
Inferred classes axiom expressions in Protégé.
| Class name | Constraint and axiom expressions |
|---|---|
| Straegic |
(HighReturns and (HighRisk or LowRisk)) and ((hasDataManagement value “EDI”) or (hasDataManagement value “ERP”)) and ((hasModeOfCommunication value “ERP”) or (hasModeOfCommunication value "E-mail")) and (hasCost some xsd:decimal [<= 250000]) and (hasOTIF some xsd:decimal [> 85]) and (hasDemandManagement value “Adequate”) |
| Basic |
(LowReturns and LowRisk) and (hasCost some xsd:decimal [<= 250000]) and (hasOTIF some xsd:decimal [> 85]) and (hasDemandManagement value “Inadequate”) and ((hasModeOfCommunication value “Phone”) or (hasModeOfCommunication value “Voice Meeting”)) and (hasDataManagement value “Spreadsheet”) or (hasDataManagement value “Ledger”)) |
| Bottleneck |
NegativeReturns or ((HighRisk and LowReturns) and (hasCost some xsd:decimal [> 250000]) and (hasOTIF some xsd:decimal [<= 85])) |
Figure 4 represents the inferred retailer segmentation knowledge graph generated by the HermiT reasoner in Protégé based on the implementation of constraints and axioms for different classes indicated in Tables 6 and 7.
Fig. 4.
Inferred Retailer segmentation knowledge graph.
Evaluation
This marks the final stage in the evolution of the knowledge graph for retailer segmentation. It consists of a two-step process: instantiation and validation testing. These steps are discussed in detail in the following subsections.
Instantiation
The knowledge graph was instantiated using the retailer data prepared in the information gathering and elicitation stage in the form of excel spreadsheet. The Cellfie plugin in Protégé was used to instantiate retailers in the knowledge graph. Custom transformation rule is implemented to generate the necessary axioms for instantiation. The rule applied for retailer instantiation is expressed using the statement below.
Individual: @A*
Types: Retailer
Facts: hasName @B*(xsd:string),
hasReturns @C*(xsd:decimal),
hasRisk @D*(xsd:decimal),
hasCost @E*(xsd:decimal),
hasOTIF @F*(xsd:decimal),
hasDemandManagement @G*(xsd:string),
hasModeOfCommunication @H*(xsd:string),
hasDataManagement @I*(xsd:string)
Validation testing
The inferences provided by the reasoner in the retailer segmentation knowledge graph were validated using built-in Description Logic (DL) query engine in Protégé. For validation testing, reverse testing strategy was adopted to validate whether individuals (Retailers) of the inferred class satisfy constraints or restrictions enforced in the class expressions. Three test cases were planned, one test case for each segmentation category. These test cases are discussed subsequently.
Test case 1
Find retailers belonging to the Basic segmentation category.
For Retailer 07, the HermiT reasoner has inferred Basic segmentation category. Table 8 illustrates why the reasoner has inferred the Basic segmentation category for Retailer 07.
Table 8.
Reasoning data for retailer 07.
| Dimension | Criteria metric | Actual value | class axiom/ logical constraint satisfied |
|---|---|---|---|
| Finance | Risk | 2.446 | LowRisk |
| Returns | 3.288 | LowReturns | |
| Operations | OTIF | 78 | hasOTIF > 85 |
| Cost | 2370000 | hasCost <= 2, 50000 | |
|
Demand Management Capabilities |
Inadequate | hasDemandManagement = “Indequate” | |
| Technology | Mode of Communication | Phone | hasModeOfCommunication=”phone” |
| Data Management | Spreadsheet |
hasDataManagement= “Spreadsheet” |
Reasoning data for Retailer 07 represented in Table 8 interprets that Retailer 07 belongs to Basic segmentation category. This is because data for Retailer 07 satisfy all the necessary and sufficient conditions for Basic segmentation category as defined in Table 7.
Test case 2
Find retailers belonging to the Strategic segmentation category
For Retailer 02, the HermiT reasoner has inferred Strategic segmentation category. Table 9 illustrates why the reasoner has inferred the Strategic segmentation category for Retailer 02.
Table 9.
Reasoning data for retailer 02.
| Dimension | Criteria metric | Actual value | class axiom/ logical constraint satisfied |
|---|---|---|---|
| Finance | Risk | 75.528 | HighRisk |
| Returns | 31.581 | HighReturns | |
| Operations | OTIF | 90.5 | hasOTIF > 85 |
| Cost | 16000 | hasCost <= 2, 50000 | |
|
Demand Management Capabilities |
Adequate | hasDemandManagement = “Adequate” | |
| Technology | Mode of Communication | ERP | hasModeofCommunication = “ERP” |
| Data Management | ERP | hasDataManagement=”ERP” |
Reasoning data for Retailer 02 represented in Table 9 interprets that Retailer 02 belongs to Strategic segmentation category. This is because this data satisfies all the necessary conditions for Strategic segmentation category as defined in Table 7.
Test case 3
Find retailers belonging to the Bottleneck segmentation category
For Retailer 05, the HermiT reasoner has inferred Bottleneck segmentation category. Table 10 illustrates why the reasoner has inferred the Bottleneck segmentation category for Retailer 05.
Table 10.
Reasoning data for retailer 05.
| Dimension | Criteria metric | Actual value | Class axiom/ logical constraint satisfied |
|---|---|---|---|
| Finance | Risk | 8.337 | LowRisk |
| Returns | -7.097 | Negative Returns | |
| Operations | OTIF | 55.5 | hasOTIF < 85 |
| Cost | 760000 | hasCost > 250000 | |
|
Demand Management Capabilities |
Inadequate | Inadequate | |
| Technology | Mode of Communication | Voice Meeting | Any |
| Data Management | Ledger | Any |
Reasoning data for Retailer 05 represented in Table 10 interprets that Retailer 05 belongs to Bottleneck segmentation category. This is because Returns metric has negative value. This means Retailer 05 has met all the necessary and sufficient conditions for the Bottleneck segmentation category as defined in Table 7. Therefore, the reasoner assigns Bottleneck segmentation category for this retailer.
For academic purposes and to simplify the interpretation of Knowledge Graph concept implementation for retailer segmentation by practitioners, we defined a limited number of segmentation categories with strict, non-overlapping boundary conditions in the Knowledge Graph. Consequently, no failure cases were observed in this study. However, we acknowledge that in real-world scenarios, retailer segmentation would likely involve a larger number of categories with overlapping boundary conditions, which could potentially lead to conflicting or failure cases.
Conclusions
In this study, Explainable AI (XAI) method is conceptualized which detects segmentation category of a retailer uncovering hidden relationships between entities involved in retailer segmentation. Thus, this method addresses the literature gap of lack of technique that can infer hidden information in the existing data to support retailer segmentation decision-making. This approach enables practitioners to draw inferences using hidden knowledge across multiple dimensions, enhancing decision-making accuracy. Moreover, the explainability feature of this method brings human-like intelligence in the outcome, thus helps improving trustworthiness of adoption of AI in practice.
The primary goal of this study was to conceptualize the XAI method. Hence, for the sake of simplicity, a limited set of criteria across various dimensions were employed in this study. However, there is a future scope of research to validate this method against more diversified business criteria. Moreover, the reasoning technique used in this method is a symbolic artificial intelligence technique. However, with the advancements in reasoning techniques such as neural network or data embeddings, it is worth assessing this method using these techniques as future scope of research.\
Author contributions
K.S. perform the data collection and data analysis. K.S. and V.W. wrote the main manuscript text and prepared all figures. A.B. , S.K., and R.G. reviewed, edited and supervise the work and manuscript.
Funding
Open access funding provided by Symbiosis International (Deemed University).
Data availability
The datasets generated and/or analyzed during the current study are not publicly available due ongoing research work but are available from the corresponding author on reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due ongoing research work but are available from the corresponding author on reasonable request.