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Customer Relationship Management(CRM) has always played a crucial role as a market strategy for providing organizations with the quintessential business intelligence for building, managing and developing valuable long-term customer relationships. A number of business enterprises have come to realize the significance of CRM and the application of technical expertise to achieve competitive advantage. This study explores the importance of Customer Segmentation as a core function of CRM as well as the various models for segmenting customers using clustering techniques. The available clustering models for customer segmentation, in general, and the major models of K-Means and Hierarchical Clustering, in particular, are studied and the virtues and vices of the techniques are pointed out. Finally, the possibility of developing a hybrid solution by the combination of the above two techniques, having the ability to outperform the individual models, is discussed.

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Abstract-Several challenges are faced by citizens of urban centers while dealing with day-to-day events, and the absence of a centralized reporting mechanism makes event-reporting and redressal a daunting task. With the push on information technology to adapt to the needs of smart-cities and integrate urban civic services, the use of Open311 architecture presents an interesting solution. In this paper, we present a novel approach that uses an existing Open311 ontology to classify and report non-emergency city-events, as well as to guide the citizen to the points of redressal. The use of linked open data and the semantic model serves to provide contextual meaning and make vast amounts of content hyper-connected and easily-searchable. Such a one-size-fits-all model also ensures reusability and effective visualization and analysis of data across several cities. By integrating urban services across various civic bodies, the proposed approach provides a single endpoint to the citizen, which is imperative for smooth functioning of smart cities.