Difference between revisions of "ANLY482 AY2017-18 Group9: Project Overview"

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[[Image:TeamInsured_About_Icon.png|30px|link=ANLY482 AY2017-18 Group9: Project Overview ]] &nbsp;
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[[ANLY482 AY2017-18 Group9: Project Overview|<font color="#F5F5F5" size=2><b>PROJECT OVERVIEW</b></font>]]
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[[ANLY482 AY2017-18 Group9: Project Overview|<font color="#F5F5F5" size=1.5><b>PROJECT OVERVIEW</b></font>]]
  
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[[ANLY482 AY2017-18 Group9: Documentation | <font color="#F5F5F5" size=1.5><b>DOCUMENTATION</b></font>]]
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[[ANLY482_AY2017-18_Term_2 | <font color="#F5F5F5" size=1.5><b>MAIN PAGE</b></font>]]
 
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==Motivation & Business Problem==
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|style="vertical-align:top;width:45%;" | <div style="background:#FFA500; width:220px; padding: 13px; font-weight: bold; line-height: 0.3em; text-align:center; text-indent: 15px">[[ANLY482_AY2017-18_Group9%3A_Project_Overview|<font face = "Open Sans" color="#FFFFFF" size=2><b> DESCRIPTION </b></font>]]
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INTENT
 
Improve the transparency of information useful in identifying a seller’s performance to customers and sellers.
 
PROBLEM
 
- Customers aren’t able to identify which are the best sellers to purchase products from.
 
- The characteristics of sellers that matter to a customer aren’t clearly defined to sellers who want to manage and improve their performance.
 
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== Project Objectives ==
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|style="vertical-align:top;width:45%;" | <div style="background:#40403E; width:220px; padding: 13px; font-weight: bold; line-height: 0.3em; text-align:center; text-indent: 15px">[[ANLY482_AY2017-18_Group9%3A_Project_Overview\ Methodology|<font face = "Open Sans" color="#ffffff" size=2><b> METHODOLOGY </b></font>]]
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We will identify critical features that can allow sellers to measure and manage their performance on Lazada’s platform.
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The aforementioned features will be exposed to the customers to help them identify the better/best sellers to purchase from.
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== Constraints ==
 
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Production ready: Run data pipeline within 3 hours with 16gb RAM
 
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== Project Details ==
 
==System Architecture ==
 
<!--
 
[[Image: LazadaSA.png | center]]
 
-->
 
 
 
 
 
==Predictive Variables(Seller Attributes)==
 
<!--
 
Shipping Time
 
Pricing
 
Return Rate
 
Seller Initiated Cancellation Rate
 
Seller Category ( e.g. home & living , fashion, multi-category sellers)
 
Size of Seller
 
Seller’s Years of experience on Lazada
 
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==Response Variables (Seller Performance Metrics)==
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==<div style="background: #40403E; line-height: 0.3em; font-family:helvetica;  border-left: #FFA500 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>PROJECT BACKGROUND</strong></font></div></div>==
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<p align="justify">With the increasing number of outlets in Singapore, coupled with the extensive menu of drinks at KOI, these resulted in logistics issues in KOI's daily operations. Employees from each of its outlet are required to manually order their required stock for next-day delivery. As such, Koi are facing problems such as their employees might under or over order for the next day. To decide on the amount of required stock for the next-day delivery, employees are required to estimate the next day sales at the same time consider the shop storage limits. Additionally, the employees should be aware of any special promotions that will be held or any events that
Total purchases made per sales item
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is happening around their outlet as it might affect their next-day sales. Overall, it has led to a steep learning curve for the new employees. <br><br>
Product Popularity Ratio (PPR) = Total Purchase / Distinct Count of products
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Secondly, upon discussion with our sponsor, we understand the majority KOI customer base makes up of students and the younger crowd. Henceforth, our team will be making us of public statistics data such as the demographics living near the KOI outlet, number of schools (primary/secondary/polytechnic) around the area, and determine if there are any high correlation between these factors and sales. This analysis could potentially help KOI identify what are the potential next viable location to open their new outlet, and to understand more
-->
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about their customer base in the area. <br><br>
==Data Source==
+
Furthermore, there are many competitors that posed significant threats. Primary competitors include Gong Cha, Share Tea and secondary competitors include local neighborhood drink stalls, Boost or Starbucks. These competitors pose as a threat as customer can choose to purchase drinks from them especially in the event of promotions held. For instance, Starbucks 1-for-1 drink promotion may affect the number of people purchasing KOI on that day in the same area. Competitors promotion are important for KOI to strategize themselves and to improve their overall brand positioning. <br><br>
 +
Lastly, KOI often hold promotional events that help to boost their sales revenue and brand reputation. Currently, there are significant difficulty in determining the effectiveness of such promotional campaigns. Furthermore, it is important for KOI to hold such promotional campaigns at the right time to maximize their sales profit.
 +
</p>
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<br>
  
<!--
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==<div style="background: #40403E; line-height: 0.3em; font-family:helvetica;  border-left: #FFA500 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>PROJECT MOTIVATION</strong></font></div></div>==
Sensitive Data (Not to be revealed)
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<p align="justify">Through our past internship, it gave us an insight of how can we utilize big data to its full potential for better decision-making and solving business problems in most industries. Such industries that we worked on before includes Security, Transport, Storage Solutions and Hospitality and Tourism. We understand the potential value analytics brings to the firm in all industries if harnessed properly. Therefore, being a consumer and not having any exposure in the consumer's industry. We will like to take this opportunity to challenge ourselves to analyse data that is in an unfamiliar domain. </p>
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<br>
  
==Methodology==
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==<div style="background: #40403E; line-height: 0.3em; font-family:helvetica;  border-left: #FFA500 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>PROJECT OBJECTIVES</strong></font></div></div>==
  <!--
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By utilizing the data from their current database, we wish to discover meaningful and informative insights which will allow KOI employees to make better decision in doing stock ordering, and thus remove the steep learning curves for their new employees. Further on, we wish to explore different factors that could impact KOI sales (i.e. demographics of people living in the area, schools around the area).
[[Image: LazadaMethodology.png|center|1000x150px]]
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<br>
-->
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==<div style="background: #40403E; line-height: 0.3em; font-family:helvetica; border-left: #FFA500 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>STAKEHOLDERS</strong></font></div></div>==
 +
<b>Project Supervisor: </b>Prof Kam Tin Seong, Associate Professor of Information Systems; Senior Advisor, SIS (Programme in Analytics) <br/>
 +
<b>Project Sponsor: </b> Kyle Huang and Joshua Wong, Project Management Office, KOI THÉ Singapore Pte. Ltd
 +
<br>
  
  
== Data Collection ==
 
<!--
 
This will be done to form the pipeline of data extraction from Lazada database and Google Analytics. The challenge is to properly pull out quality data from the relevant and updated sources.
 
-->
 
== Data Exploration and Cleaning==
 
<!--
 
Manage exploratory analysis of these data. These analysis will be used to improve on business questions which also affect the exploratory analysis. This process will be done repeatedly with necessary data cleaning and munging until we find business questions which accurately express business needs given the data and exploratory analysis made.
 
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== Data Modelling==
 
<!--
 
After a proper exploratory phase of the analysis, we will train and test machine learning models to to answer predictive and prescriptive business questions. This will include processes such as clustering to segment user behaviours, regression to include impacts of various seller attributes to CX Metrics, etc. Various statistical learning models such as Random Forest and Regularization might also be used to reduce risk of overfitting and increase testing accuracy of models.
 
-->
 
==Data Visualization==
 
<!--
 
These data analysis will be documented visually Jupyter Notebook or interactive dashboard tools which are later demonstrated and presented to business users such as Lazada suppliers and internal teams. Insights presentation techniques such as Storyboarding and Pyramid technique (Barbara Pinto) might also be used to ensure proper presentation to match findings and business needs.
 
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Latest revision as of 21:52, 14 January 2018

Fablogo.png

TeamInsured Home.png   HOME

 

TeamInsured About Icon.png   PROJECT OVERVIEW

 

TeamInsured Findings.png   PROJECT FINDINGS

 

TeamInsured PM.png   PROJECT MANAGEMENT

 

TeamInsured Documentation.png   DOCUMENTATION

 

TeamInsured Documentation.png   MAIN PAGE


PROJECT BACKGROUND

With the increasing number of outlets in Singapore, coupled with the extensive menu of drinks at KOI, these resulted in logistics issues in KOI's daily operations. Employees from each of its outlet are required to manually order their required stock for next-day delivery. As such, Koi are facing problems such as their employees might under or over order for the next day. To decide on the amount of required stock for the next-day delivery, employees are required to estimate the next day sales at the same time consider the shop storage limits. Additionally, the employees should be aware of any special promotions that will be held or any events that is happening around their outlet as it might affect their next-day sales. Overall, it has led to a steep learning curve for the new employees.

Secondly, upon discussion with our sponsor, we understand the majority KOI customer base makes up of students and the younger crowd. Henceforth, our team will be making us of public statistics data such as the demographics living near the KOI outlet, number of schools (primary/secondary/polytechnic) around the area, and determine if there are any high correlation between these factors and sales. This analysis could potentially help KOI identify what are the potential next viable location to open their new outlet, and to understand more about their customer base in the area.

Furthermore, there are many competitors that posed significant threats. Primary competitors include Gong Cha, Share Tea and secondary competitors include local neighborhood drink stalls, Boost or Starbucks. These competitors pose as a threat as customer can choose to purchase drinks from them especially in the event of promotions held. For instance, Starbucks 1-for-1 drink promotion may affect the number of people purchasing KOI on that day in the same area. Competitors promotion are important for KOI to strategize themselves and to improve their overall brand positioning.

Lastly, KOI often hold promotional events that help to boost their sales revenue and brand reputation. Currently, there are significant difficulty in determining the effectiveness of such promotional campaigns. Furthermore, it is important for KOI to hold such promotional campaigns at the right time to maximize their sales profit.


PROJECT MOTIVATION

Through our past internship, it gave us an insight of how can we utilize big data to its full potential for better decision-making and solving business problems in most industries. Such industries that we worked on before includes Security, Transport, Storage Solutions and Hospitality and Tourism. We understand the potential value analytics brings to the firm in all industries if harnessed properly. Therefore, being a consumer and not having any exposure in the consumer's industry. We will like to take this opportunity to challenge ourselves to analyse data that is in an unfamiliar domain.


PROJECT OBJECTIVES

By utilizing the data from their current database, we wish to discover meaningful and informative insights which will allow KOI employees to make better decision in doing stock ordering, and thus remove the steep learning curves for their new employees. Further on, we wish to explore different factors that could impact KOI sales (i.e. demographics of people living in the area, schools around the area).

STAKEHOLDERS

Project Supervisor: Prof Kam Tin Seong, Associate Professor of Information Systems; Senior Advisor, SIS (Programme in Analytics)
Project Sponsor: Kyle Huang and Joshua Wong, Project Management Office, KOI THÉ Singapore Pte. Ltd