Citation

Zulkiflee, Imran and Haw, Su Cheng and Ng, Kok Why and Naveen, Palanichamy and Krisnawati, Lucia Dwi (2023) Customer Relationship Management for Better Insights with Descriptive Analytics. Advances in Artificial Intelligence and Machine Learning, 03 (03). pp. 1482-1493. ISSN 2582-9793

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Abstract

The Customer Relationship Management (CRM) dashboard with analytics capabilities may be useful for businesses desire to understand consumer behaviour and gain competitive advantages. This user-friendly dashboard allows organizations to efficiently collect and evaluate important data, including customer interactions, purchase histories, and demographics. By leveraging various visualization techniques, such as histogram, bar charts, line charts, and heatmaps, the CRM dashboard displays statistics, enabling businesses to understand more about customer journeys, patterns, and behavioural trends. The integrated recommender system on the CRM dashboard is also crucial. Based on their historical interactions and buying behaviours, this system recommends tailored products or services to customers, enhancing engagement and ultimately increasing sales. The methodology used in this research is structured based on a standard data analytics procedure. The methods used to gather and evaluate the data are described in the data gathering and analysis. It contains details on the data gathering, the steps used to clean and prepare the data, and the statistical or analytical techniques employed to evaluate the data. As a result, machine learning simulations may be made using the data to demonstrate the effectiveness of the CRM dashboard. The CRM dashboard with analytics capabilities provides a comprehensive solution for businesses wishing to manage and study customer data and interactions owing to its user-friendly interface, visualizations, and recommender system.

Item Type:	Article
Uncontrolled Keywords:	Business intelligence, Data visualization, Customer relationship management dashboard, Descriptive analytics, Recommender system
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 Oct 2023 06:43
Last Modified:	31 Oct 2023 06:43
URII:	http://shdl.mmu.edu.my/id/eprint/11779

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