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| − | <td>[https://wiki.smu.edu.sg/ANLY482/ANLY482_AY2016-17_T1_Group1 Our Project Tile is]</td>
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| − | <td></td>
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| − | '''Group01- Team Analytics'''
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| − | * Student 1
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| − | * Student 2
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| − | * Student 3
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
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| − | Associate Professor of Information Systems (Practice)</td>
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| − | <td>[https://wiki.smu.edu.sg/ANLY482/ANLY482_AY2016-17_T1_Group1 Our Project Tile is]</td>
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| − | <td></td>
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| − | '''Group01- Team Analytics'''
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| − | * Student 1
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| − | * Student 2
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| − | * Student 3
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
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| − | Associate Professor of Information Systems (Practice)</td>
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| − | <td>[https://wiki.smu.edu.sg/ANLY482/Analysis_of_User_and_Merchant_Dropoff_for_Sugar_App Analysis of User and Merchant Dropoff for Sugar App]</td>
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| − | <td>Sugar is an interactive city guide that seeks to encourage a culture of exploration in Singapore and helping local small businesses get discovered. Sugar’s merchants are mainly small local businesses in Singapore. It has a large variety, including cafes, small restaurants, bars, hair salons, gyms, gift shops. The benefits for merchants is advertising to users that are in close proximity to them. Users in turn get discounts on products that are in the closest proximity to them.
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| − | As Sugar is a relatively young startup in Singapore, it has not yet attained a critical mass of user and merchant numbers. As such, user growth and user experience is vitally important. To reach this critical mass, Sugar needs to minimize user and merchant attrition, and retain vital segments of both groups. Furthermore, as a two-sided market, Sugar needs the network effect and also find out which group(Users or Merchants) adds more value to their bottomline.
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| − | The objective of our project will be to improve Sugar's bottomline via
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| − | Merchant Analysis <br />
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| − | User Analysis <br />
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| − | Two-Sided Market Analysis <br />
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| − | Geospatial Analysis <br />
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| − | We will be using several techniques such as funnel plots, time series analysis, shortest distance analysis(Geospatial) and regression modeling to get insights and subsequently deriving recommendations for Sugar to increase its revenue and growth.</td>
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| − | <td>
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| − | '''[https://wiki.smu.edu.sg/ANLY482/Analysis_of_User_and_Merchant_Dropoff_for_Sugar_App Group02- Team TurnKEY]'''
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| − | * Kang Long
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| − | * Elizabeth Tan
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| − | * Yi Sheng, Lim
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
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| − | Associate Professor of Information Systems (Practice)</td>
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| − | <td>
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| − | Benjamin Lee
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| − | Founder and Chief Executive Officer of Sugar Technologies Pte Ltd.
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| − | </td>
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| − | </tr>
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| − | <td>[https://wiki.smu.edu.sg/ANLY482/Teppei_Syokudo_-_Improving_Store_Performance Teppei Syokudo - Improving Store Performance]</td>
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| − | <td>Teppei Syokudo is a Japanese Food and Beverage chain, under the umbrella of the famous Teppei Japanese Restaurant. In order to drive store performance through controllable factors, Teppei Syokudo is looking at focusing on staff performance. Most F&B businesses, including Teppei Syokudo, do not set detailed KPIs to evaluate how their staff are performing. If Teppei Syokudo is able to track the performance of their staff through relevant KPIs, they will be able to motivate staff to meet these KPIs, which will in turn boost the business’ bottom line.Teppei Syokudo has identified the following KPIs to assess their staff:
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| − | *Percentage of drinks sales (number of drinks sold / number of meals sold)
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| − | **a measurement of how hard the staff are up-selling
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| − | *Labour Productivity (sales $ / working hours)
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| − | **a measurement of how effective the shop manager is in staffing the shop
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| − | However, the business is uncertain if these are the right KPIs to set. Also, if they are the right ones, they are unsure as to what would be a good target to meet.
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| − | Another factor for driving store performance is through product portfolio mix. Even though the staff may be up-selling and cross-selling, they may not know the right products to cross-sell to increase the probability of the customer making additional purchases. For example, most customers may tend to purchase Drink X together with Don X. In this case, if a customer orders Don X and is about to make payment, the staff can suggest Drink X to the customer, hence prompting a higher probability for the customer to purchase Drink X.
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| − | </td>
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| − | '''Group03- Team APSM'''
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| − | * TAN Jhun Boon
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| − | * YAP Jessie
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| − | * OH Peng Ho
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
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| − | Associate Professor of Information Systems (Practice)</td>
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| − | <td>Itaru Nagao,
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| − | Managing Director
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| − | YCP Management Southeast Asia Pte. Ltd.
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| − | YCP Retailing SEA Pte. Ltd.
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| − | </td>
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| − | </tr>
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| − | <td>[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_Atom Car Park Overspill Study]</td>
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| − | <td>The objective of this project is to assist Media Research Consultants Pte Ltd (MRC) in understanding the current parking situations in 65 different locations in Singapore. These 65 parking locations compromise of 30 retail malls, 15 retails and Food & Beverage (F&B) clusters in landed housing estates, 10 hawker centers, and 10 community clubs. </td>
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| − | <td>
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| − | '''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_Atom Group04- Team ATOM]'''
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| − | * Macus KHOO JunHao
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| − | * YAN ShaoHong Chris
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| − | * YO Wee King
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
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| − | Associate Professor of Information Systems (Practice)</td>
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| − | <td>[http://www.mrconsultants.sg Media Research Consultants Pte Ltd]
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| − | </td>
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| − | </tr>
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| − | <td>[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_AP Social Media Analytics]</td>
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| − | <p>The aim of this project is to provide deeper insight into SGAG's social network across its multiple platforms, namely Instagram and Twitter. </p>
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| − | <p>Our client is the co-founder of the company and he seeks insights that can spur growth in SGAG's follower numbers. Through our analysis and research, we aim to help discover what kind of users are on each platform; the key engagement leaders for each topic; and how wide is the reach of these individuals. </p>
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| − | <p>The final deliverables will aim to: <br>
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| − | # Visualise the social networks of SGAG
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| − | # Identifying the user segments who engage SGAG's content, as filtered by topics and their reach
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| − | </p>
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| − | </td>
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| − | <td>
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| − | '''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_AP Group05- Team AP]'''
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| − | * WANG Shyan Ann
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| − | * NG Tse Siong
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| − | * Sherman YONG Chin Wei
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
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| − | Associate Professor of Information Systems (Practice)</td>
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| − | <td>Karl Mak, Co-founder at SGAG</td>
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| − | </tr>
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| − | <td>[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_SkyTrek Skyscanner Content Analysis]</td>
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| − | <td>
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| − | <p>
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| − | The project aims to help Skyscanner analyse its content sites in order to facilitate more effective planning of news articles. It will help understand the factors that affect content performance.
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| − | </p>
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| − | <p>
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| − | The team will analyse content related data from multiple sources to determine what differentiates good and bad content based on certain performance metrics set by Skyscanner. This will be done through Text Based Cluster Analysis, Exploratory Modelling with logistic regression and Data Visualization using Tableau
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| − | </p>
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| − | <p>
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| − | The deliverables include creating a dashboard with visualizations that will help Skyscanner team to better understand performance of content across different content channels.
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| − | It will be used to validate some of the intuitions they might have about certain content topics/types and to determine the best time to publish them.
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| − | The dashboard will benchmark certain metrics against pageviews as well as additional attributes that Skyscanner does not currently analyse via Google Analytics, such as the impact of title, text length, theme of article and number of images.
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| − | </p>
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| − | <p>
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| − | Data set includes data from Skyscanner websites for the Singapore, Malaysia and Thailand markets.
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| − | </p>
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| − | </td>
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| − | <td>
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| − | '''[https://wiki.smu.edu.sg/ANLY482/AY1516_T2_Team_SkyTrek Group06- Team SkyTrek]'''
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| − | * Aseem PRABHAT
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| − | * Jedaiah TAN Jia Le
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| − | * NGUYEN Viet Huy
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| − | </td>
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| − | <td>[http://sis.smu.edu.sg/faculty/profile/9618/KAM-Tin-Seong Prof. Kam Tin Seong]
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| − | Associate Professor of Information Systems (Practice)</td>
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| − | <td>
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| − | Ms. Antoinette Tan <br>
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| − | Content Manager, APAC<br>
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| − | Skyscanner
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| − | </td>
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| − | </tr>
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| | </table> | | </table> |
