Difference between revisions of "ANLY482 AY2016-17 Term 1"

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<td>[https://wiki.smu.edu.sg/ANLY482/ANLY482_AY2016-17_T1_Group2 Sports Event Demand Prediction]</td>
 
<td>[https://wiki.smu.edu.sg/ANLY482/ANLY482_AY2016-17_T1_Group2 Sports Event Demand Prediction]</td>
 
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At Singapore Pools, the demands of sports matches are anticipated through experience and gut feeling of its Sports Division staffs, which human resources (telephone betting staffs) are then allocated based on the turnout or demand of individual soccer match. Such an approach can prove to be erratic at times, as humans can be very prone to errors and/or other factors, which will negatively impact their decision making capabilities. Any errors or failures in making the right decision can also prove to be costly for Singapore Pools, as they will not be able cope with the demands and these customers will potentially resort to illegal counterparts to place their bets.
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In today’s digitalised world, technology had greatly transformed gambling behaviour. Customers these days are able to check and place bets on sports event without being constrained by the opening hours of Singapore Pools’ physical outlets. Moreover, due to time difference, the more well-known leagues such as English Premium League, are often broadcasted in the middle of the night. This makes it difficult for Singapore Pools to allocate additional resources to assist its phone-betting customers, if the demand for a particular match exceed its expectation.
  
This project aims to build a Sports Event Demand Prediction Model, where Singapore Pools will be able to predict demand for individual sports matches from different leagues based on historic data.
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With such challenges, it can prove costly to Singapore Pools if they are unable to allocate their resources efficiently, and when affected customers potentially turn to illegal counterparts to place their bets. Hence, Singapore Pools hopes to derive insights from their historical data and to better predict the demand for sports matches, so as to optimise its resources.
 
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'''Group02- Team Analytics'''
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'''Group02- Team Modulo'''
 
* CHANG Hua Peng
 
* CHANG Hua Peng
 
* CHUA Feng Ru
 
* CHUA Feng Ru

Revision as of 05:05, 4 September 2016

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List of Projects
Title Analytics Practicum Description Student Member(s) Project Supervisor Sponsor
Our Project Tile is

Group01- Team Analytics

  • Anita
  • Hoe Xiu Ming
  • Sallyana
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice)
Sports Event Demand Prediction

In today’s digitalised world, technology had greatly transformed gambling behaviour. Customers these days are able to check and place bets on sports event without being constrained by the opening hours of Singapore Pools’ physical outlets. Moreover, due to time difference, the more well-known leagues such as English Premium League, are often broadcasted in the middle of the night. This makes it difficult for Singapore Pools to allocate additional resources to assist its phone-betting customers, if the demand for a particular match exceed its expectation.

With such challenges, it can prove costly to Singapore Pools if they are unable to allocate their resources efficiently, and when affected customers potentially turn to illegal counterparts to place their bets. Hence, Singapore Pools hopes to derive insights from their historical data and to better predict the demand for sports matches, so as to optimise its resources.

Group02- Team Modulo

  • CHANG Hua Peng
  • CHUA Feng Ru
  • NGO Kee Kai
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice)

Singapore Pools

Geo-spatial Operational Insights for National Library Board In this age of information, we see an increasing need for people and businesses to have a greater access to space and resources to further their personal and corporate needs. Hence, there is the requisite for the libraries to adequately manage this associated increasing demand. However, there exists this difficulty in measuring the operational readiness of the libraries; unlike typical corporations and organisations, the measure of public demand is not in dollars and cents.

Furthermore, there have been renovations and relocation of existing libraries and unveiling of new libraries to keep up with the times. These constant changes prompt for a reliable system to measure the effectiveness of past policies, as well as an accurate predictive model to conduct what-if analyses for future plans. This project aims to create a user-friendly system which displays geo-spatial information that can provide operational insights for the NLB.

Group03- Qui vivra verra

  • LIU Bowei
  • PONG Chong Xin
  • TEO Hui Min
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) National Library Board (NLB)
Consumer and Insurance Team Insights for Tokio Marine

At Tokio Marine, serving happiness to customers has always been the main focus. As such, we at Team Insured have been tasked to assist Tokio Marine's customer base, specifically for the Life Insurance side of the business. The three main things that Tokio Marine requires our help for to do analysis on would be on Average Product Holdings per Customer, Customer Segmentation and Agent Segmentation. Deeper analysis such as Need Based Analysis has also been requested. These are the three general objectives that are to be pursued for this project, so as to help Tokio Marine understand their customers better.

Group04- Team Insured

  • Joshua Quek
  • Justin Ong
  • Manas Mohapatra
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Tokio Marine Asia
Intelligence and Analytics in F&B

Traditional POS systems are used mainly for completing orders and payments from a terminal. Data collected from these systems are hence limited to sales figures. A successful F&B business should not only analyse sales figures but also other aspects of the business such as efficiency of the current operations, inventory stocking, marketing campaigns and etc.

Analytics Cafe will be working with HoiPOS to provide an interactive dashboard of different visualizations of data collected from HoiPOS systems. The project aims to improve HoiPOS’ data visualizations as well as add more visualizations of data analysis to value-add their POS system to clients. This project also aims to be able to apply descriptive analytics and effective visualizations to gain insights not only on the sales performance but also operations and marketing campaigns.

Group05- Analytics Cafe

  • Chen Shiqi
  • Tan Wei Lin Joanna
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice)
Using Social Network and Text Analytics to Create a Sales Force Performance Dashboard Advanced analytics have permeated almost every aspect of business management, from sales and marketing to finance and operations. Once carried out through intuition and experience, the human resource (HR) function is now undergoing its own revolution in the form of People Analytics. People Analytics provide HR professionals with data-driven methods to evaluate human capital and uncover critical workforce insights, thus diminishing the “guesswork” and uncertainty that comes with talent management.

This project seeks to assist TrustSphere in gaining insight into how efficiently their sales team communicates within the company’s external and internal network. Furthermore we aim to develop a dashboard that helps them monitor these communications and assess the performance of their sales employees.

Group06- Team MST

  • Mabel HENG Yi Teng
  • Siti Hamidah Binte Abdul HAMID
  • Tarika GUPTA
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) Ridwan Ismeer

TrustSphere