Customer Experience Management & CRM - Crossref Bibliographic Metadata

This dataset provides detailed bibliographic metadata records for scholarly publications related to 'Customer Experience Management' (CEM) and 'Customer Relationship Management' (CRM), as retrieved from Crossref.org. This metadata corpus facilitates in-depth exploration of the academic discourse surrounding these interconnected domains. Contextual Overview of Customer Experience Management & CRM: 1. Definition and Context: Customer Relationship Management (CRM) focuses on practices, strategies, and technologies that companies use to manage and analyze customer interactions and data throughout the customer lifecycle, aiming to improve business relationships, assist in customer retention, and drive sales growth. Customer Experience Management (CEM) is a broader strategy that focuses on the perceptions and feelings a customer has as a result of all their interactions with a company over time. Both gained significant traction from the late 1990s, fueled by technology and a shift towards customer-centricity. 2. Strengths and Weaknesses: Strengths include enhanced customer loyalty, increased sales, improved customer service, and better customer insights. Weaknesses often stem from costly and complex system implementations, challenges in achieving a truly unified customer view, potential for data privacy concerns, and organizational resistance to customer-centric cultural shifts. Success requires strategic alignment, robust data governance, and consistent execution across all touchpoints, not just technological deployment. 3. Relevance and Research Potential: CEM and CRM are critical in today's competitive, digitally-driven markets where customer expectations are high. They are central to marketing, sales, and service management. Research avenues include the impact of AI and big data on CRM/CEM effectiveness, measuring and managing omnichannel customer journeys, the role of employee experience in delivering customer experience, personalization at scale, and the ethical implications of customer data utilization, particularly within emerging digital ecosystems. 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 CEM/CRM. 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 CEM/CRM. 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 experience management" OR CRM ...) AND ("strategy" 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 Experience Management & CRM' 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 CEM/CRM 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 CEM/CRM should consult the corresponding datasets in the Raw Extracts Dataverse and the Comparative Indices Dataverse.