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Who should attend
This is an intermediate course, suitable for professionals with 2 years of experience, with an interest or requirement to understand digital marketing and social engagement in the following industries including IT Services, Public Services (e.g. Government Agencies), Healthcare, Finance Institutions (Retail Banking & Insurance), Airlines, Logistics, Transport, Hotel, Tourism, Consumer services, etc.
It is applicable for professionals engaged in the following areas.
Participants would have experience in service industries but not have analytics expertise.
The targeted participants are in:
- Service management
- Service improvement
- Service Quality Assurance
- Service Innovation
- Service Marketing
- Service Industry Customer insight and market research
About the course
Service analytics describes the process of capturing, processing and analyzing the data generated from the execution of a service system to improve, extend, and personalize a service to create value for both providers and customers.
Service analytics also covers process analytics and will be useful for those who are managing processes and quality in service industries.
The methodologies taught in this course are applicable to many industries including IT Services, public services (e.g. Government agencies), Healthcare, Finance Institutions (Retail Banking & Insurance), Airlines, Logistics, Transport, Hotel, Tourism, Consumer services, etc.
An organisation’s innovation potential depends on its capability to process ‘data’ about internal operations and the world outside the organization.
The scope of this analytics-based capability is constantly expanded by the development of new technologies making the generation, collection, and analysis of data much easier.
For many industries, more data and the ability to analyze that data in a scientific manner, provide an opportunity to optimize, innovate, and differentiate for growth and greater efficiency. This capability based on data analytics is emerging as a critical factor to achieve competitive advantage.
This course is part of the Analytics and Intelligence Systems series offered by NUS-ISS.
At the end of the course, the participants will be able to:
- Recognize, access, and utilise different types of service data.
- Understand and apply different sense making and data communication techniques.
- Evaluate, select, and implement specific analytic tools.
- Design and deploy analytical frameworks for ‘service quality’, ‘customer satisfaction’, and ‘service innovation’.
- Apply analytic techniques to the evaluation and performance management.
- Apply analytics to inform strategic decisions and communicate performance.
What Will Be Coverede
- Introduction to Service Analytics
- Customer satisfaction survey
- Service quality framework
- Metrics – GQM method + Data for Service Metrics
- Advances in Analytics for service processes
- Sense making and diagnostic tools
- Quantile regression
- Multinomial logistics regression
- Service process improvement
Chandra has a wide experience in Software Quality Management as a SQA and SEPG involved in identifying, developing, implementing, verifying the adherence, assessing the effectiveness and improving of the organizational standard software processes with reference to the SW CMM & CMMI Dev Models...
Brandon is currently an Associate in Institute of Systems Science, National University of Singapore (NUS-ISS). He completed his study in Computer Science, major in Software Engineering and Artificial Intelligence. Under the NUS full time research scholarship sponsored by The Logistics Institute -...
16 years of experience in marketing insights and analytics across Asia, with special focus on consumer, retailing, brand research and policy development and evaluation. Expertise to integrate data from multiple sources. Extensively worked on segmentation, brand positioning, pricing decision, ROI,...
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.