Workshop on Customer Management Experience Using Data Analytics

According to an article published in The Straits Times on 28 October 2020, about 70% of the 575 SMEs surveyed in Singapore had not yet adopted data analytic solutions and services, with many of them being familiar only with spreadsheets and databases, suggesting a lack of awareness and understanding of advanced data analytics. In business today, data are considered an asset as tangible as hardware and are at the heart of operations. SMEs therefore need to move away from applicationcentric to data-centric ways of doing business. They need to go beyond the mere collection of information and monitoring to real-time data analysis to extract maximum value and pass the results to
key decisionmakers as quickly as possible.

A World Bank article published on 27 September 2021 reported that the service sector accounted for roughly 55% of GDP in developing countries and around 68% in developed countries. Customer experience management (CXM), which encompasses the processes used to track, oversee, and organize every interaction with customers throughout the engagement cycle, is a critical component of business success. With various information coming from multiple customer touchpoints, it is necessary to deploy appropriate, systematic data analysis tools to enhance CXM to achieve the desired objectives. Successful CXM helps build and develop brands in the minds of customers, ultimately leading to long-term relationships. Better experiences are very effective in increasing sales to new or existing customers. A satisfied customer will always create repeat sales and act as a wordof-mouth advertiser. CXM improves brand loyalty through positive customer interactions, satisfaction, and goodwill. In the long run, these are assets for any company to build future growth. The customer experience can be maximized if data analytics are used appropriately.

Data analytic tools can be either qualitative or quantitative. This workshop will explain how those tools extract and separate useful data from unnecessary information and analyze them to reveal patterns and numbers that can help in making profitable changes. It will also discuss how data analytics predict customer trends and behaviors, increase business productivity, and lead to evidence-backed decisions.

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