PROJECT TOPIC: Machine Learning for Customer Segmentation Through Bibliometric Approach

Project Body:


In the age of information science and automation, ‘technological revolution’ and ‘machine era’ have gained significant attention from researchers of every quarter of life. Exuberance of artificial computation with machine learning techniques with practical applications and precision has become a perennial issue in every discipline. Optimization of marketing efficiency with assistance of computational applications has improved the accuracy and transparency level to achieve competitive advantages, to enhance organizational efficiency and to gain market advancement. In this bibliometric study, the authors have analyzed the literatures published during the period 2009–2019 having occurrence of relevant terms and major sources of contribution related to the area of ‘customer segmentation’ as an impact of ‘machine learning applications.’ Data scribe has been obtained from Scopus database, total of 1440 numbers of research articles were further analyzed using VOSviewer tool. The study revealed co-occurrence of keywords and bibliometric coupling of major sources of contribution. Findings suggest that the most frequent occurred key terms related to machine Learning have more influence and link strength than the terms related to customer segmentation by analyzing significant sources in terms of citations received and number of contribution made.

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