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

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==Motivation & Business Problem==
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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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INTENT
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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>==
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Improve the transparency of information useful in identifying a seller’s performance to customers and sellers.
 
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PROBLEM
 
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- Customers aren’t able to identify which are the best sellers to purchase products from.
 
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- 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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INTENT<br/>
 
INTENT<br/>
 
Our project aims to help employees make better informed decisions on the right ordering amount of ingredients for each branch in the future.<br/><br/>
 
Our project aims to help employees make better informed decisions on the right ordering amount of ingredients for each branch in the future.<br/><br/>
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- Different stores have different constraints on stocking of ingredients<br/>
 
- Different stores have different constraints on stocking of ingredients<br/>
 
- Do not understand how much ingredients required during promotional periods<br/>
 
- Do not understand how much ingredients required during promotional periods<br/>
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== Project Objectives ==  
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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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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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Our team will focus on correlations between different factors such as promotions, sales, shipment etc. and how these affect sales during different seasons of the year.
 
Our team will focus on correlations between different factors such as promotions, sales, shipment etc. and how these affect sales during different seasons of the year.
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<br>
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== Constraints ==
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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>==
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Project Supervisor: Prof Kam Tin Seong, Associate Professor of Information Systems; Senior Advisor, SIS (Programme in Analytics) <br/>
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Production ready: Run data pipeline within 3 hours with 16gb RAM
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Project Sponsor: Kyle Huang and Joshua Wong, Project Management Office, KOI THÉ Singapore Pte. Ltd
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== Project Details ==
 
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==System Architecture ==
 
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[[Image: LazadaSA.png | center]]
 
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==Predictive Variables(Seller Attributes)==
 
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Shipping Time
 
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Pricing
 
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Return Rate
 
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Seller Initiated Cancellation Rate
 
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Seller Category ( e.g. home & living , fashion, multi-category sellers)
 
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Size of Seller
 
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Seller’s Years of experience on Lazada
 
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==Response Variables (Seller Performance Metrics)==
 
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Total purchases made per sales item
 
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Product Popularity Ratio (PPR) = Total Purchase / Distinct Count of products
 
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==Data Source==
 
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Sensitive Data (Not to be revealed)
 
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==Methodology==
 
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[[Image: LazadaMethodology.png|center|1000x150px]]
 
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== Data Collection ==
 
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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.
 
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== Data Exploration and Cleaning==
 
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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==
 
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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.
 
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==Data Visualization==
 
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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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Revision as of 22:13, 2 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


PROJECT BACKGROUND


PROJECT MOTIVATION

INTENT
Our project aims to help employees make better informed decisions on the right ordering amount of ingredients for each branch in the future.

PROBLEM
- Difficulty predicting the right amount of ingredients to order
- Different stores have different constraints on stocking of ingredients
- Do not understand how much ingredients required during promotional periods

PROJECT OBJECTIVES

Our team will focus on correlations between different factors such as promotions, sales, shipment etc. and how these affect sales during different seasons of the year.

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


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