Customer Segmentation - Crossref Bibliographic Metadata

This dataset provides detailed bibliographic metadata records for scholarly publications related to 'Customer Segmentation' (including Market Segmentation), as retrieved from Crossref.org. This metadata corpus facilitates in-depth exploration of the academic discourse surrounding this fundamental marketing strategy. Contextual Overview of Customer Segmentation: 1. Definition and Context: Customer Segmentation is the process of dividing a broad consumer or business market, normally consisting of existing and potential customers, into sub-groups of consumers (known as segments) based on shared characteristics. Its purpose is to enable more targeted and effective marketing strategies. A cornerstone of marketing theory and practice for decades, its application has become increasingly sophisticated with the advent of data analytics and digital marketing channels. 2. Strengths and Weaknesses: Strengths include improved marketing ROI through targeted messaging, enhanced customer understanding and satisfaction, better product development, and increased competitiveness. Weaknesses can involve the cost and complexity of data collection and analysis, difficulty in identifying meaningful and actionable segments, risk of over-segmentation or stereotyping, and challenges in implementing differentiated strategies across segments. The stability and relevance of segments can also change over time. 3. Relevance and Research Potential: Customer Segmentation remains highly relevant for personalization, targeted advertising, and value proposition design in both B2C and B2B markets. It is a foundational concept in marketing strategy and consumer behavior research. Current research opportunities include AI-driven and dynamic segmentation, behavioral segmentation based on digital footprints, ethical considerations in data-driven segmentation (e.g., fairness, privacy), and the integration of segmentation with customer journey mapping and experience design across omnichannel environments. Dataset Structure and Content: The dataset consists of one or more archives. Each archive contains a series of approximately 850 monthly folders (e.g., spanning from January 1950 to January 2025), reflecting a granular month-by-month process of metadata retrieval and curation for Customer Segmentation. Within each monthly folder, users will find several JSON files documenting the search and filtering process for that specific month: term_results/: A subfolder containing JSON files for results of initial broad keyword searches related to Customer Segmentation. merged_results.json: Aggregated results from these individual term searches before advanced filtering. filtered_results.json: Results after applying a more specific, complex Boolean query (e.g., (\"customer segmentation\" OR \"market segmentation\") AND (\"marketing\" OR ...)) and exact phrase matching to refine relevance. The exact query used is detailed within this file. final_results.json: This is the primary file of interest for most users. It contains the curated, deduplicated (by DOI) list of unique publication metadata records deemed most relevant to 'Customer Segmentation' for that specific month. Includes fields like Title, Authors, DOI, Publication Date, Source Title, Abstract (if available from Crossref). statistics_results.json: Summary statistics of the search and filtering process for the month. This granular monthly structure allows researchers to trace the evolution of academic discourse on Customer Segmentation and identify relevant publications with high temporal precision. For an overview of the general retrieval methodology, refer to the parent Dataverse description (Management Tool Bibliographic Metadata (Crossref)). Users interested in aggregated publication counts or trend analysis for Customer Segmentation should consult the corresponding datasets in the Raw Extracts Dataverse and the Comparative Indices Dataverse.