Difference between revisions of "ANLY482 AY2016-17 T2 Group21 : PROJECT OVERVIEW"

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[[ANLY482_AY2016-17_Term_2 | <font color="#F5F5F5" size=3 face="Century Gothic"><b>PROJECTS</b></font>]]
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[[ANLY482_AY2016-17_T2_Group21 | <font color="#F5F5F5" size=3 face="'Century Gothic'"><b>HOME</b></font>]]
 
[[ANLY482_AY2016-17_T2_Group21 | <font color="#F5F5F5" size=3 face="'Century Gothic'"><b>HOME</b></font>]]
  
 
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[[ANLY482_AY2016-17_T2_Group21 : ABOUT US | <font color="#F5F5F5" size=3 face="'Century Gothic'"><b>ABOUT US</b></font>]]
 
[[ANLY482_AY2016-17_T2_Group21 : ABOUT US | <font color="#F5F5F5" size=3 face="'Century Gothic'"><b>ABOUT US</b></font>]]
  
 
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[[ANLY482_AY2016-17_T2_Group21 : PROJECT OVERVIEW | <font color="#000000" size=3 face="'Century Gothic'"><b>PROJECT OVERVIEW</b></font>]]
 
[[ANLY482_AY2016-17_T2_Group21 : PROJECT OVERVIEW | <font color="#000000" size=3 face="'Century Gothic'"><b>PROJECT OVERVIEW</b></font>]]
  
 
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[[ANLY482_AY2016-17_T2_Group21 : PROJECT FINDINGS | <font color="#F5F5F5" size=3 face="'Century Gothic'"><b>PROJECT FINDINGS</b></font>]]
 
[[ANLY482_AY2016-17_T2_Group21 : PROJECT FINDINGS | <font color="#F5F5F5" size=3 face="'Century Gothic'"><b>PROJECT FINDINGS</b></font>]]
  
 
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[[ANLY482_AY2016-17_T2_Group21 : PROJECT MANAGEMENT | <font color="#F5F5F5" size=3 face="'Century Gothic'"><b>PROJECT MANAGEMENT</b></font>]]
 
[[ANLY482_AY2016-17_T2_Group21 : PROJECT MANAGEMENT | <font color="#F5F5F5" size=3 face="'Century Gothic'"><b>PROJECT MANAGEMENT</b></font>]]
  
 
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[[ANLY482_AY2016-17_T2_Group21 : DOCUMENTATION | <font color="#F5F5F5" size=3 face="'Century Gothic'"><b>DOCUMENTATION</b></font>]]
 
[[ANLY482_AY2016-17_T2_Group21 : DOCUMENTATION | <font color="#F5F5F5" size=3 face="'Century Gothic'"><b>DOCUMENTATION</b></font>]]
 
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==<div style="background: #404041;font-weight: light; padding:0.3em; text-transform:uppercase;letter-spacing:0.1em;font-size:18px; font-family: 'Century Gothic'"><font color=#ffffff><center>Project Overview</center></font></div>==
 
==<div style="background: #404041;font-weight: light; padding:0.3em; text-transform:uppercase;letter-spacing:0.1em;font-size:18px; font-family: 'Century Gothic'"><font color=#ffffff><center>Project Overview</center></font></div>==
 
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4FINGERS is a Singapore-based and headquartered chain of fast casual restaurants that specializes in Korean-styled fried chicken. Since its inception in 2009, it has experienced rapid growth and has gained a cult following amongst Singaporeans. 4FINGERS pride themselves on serving non-processed food with no artificial flavourings, and MSG. Besides Korean-styled fried chicken, 4FINGERS also features many other asian-inspired menu items including burgers, Japanese rice boxes, fried seafood and salads. Influenced by New York City, the interior design of the restaurants feature heavily-graffitied and signs reminiscent of the New York City Subway
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<div align="center">[[File:Dressabelle Landing.png|400px]]</div>
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<b>Dressabelle</b> is a leading online fast-fashion retailer in Singapore. Having established themselves online since 2012, Dressabelle has also expanded to 6 physical stores in malls around Singapore. Dressabelle set themselves apart with not one but two fresh fashion collection every week. Their mission is for women to feel confident about themselves when they dress in their clothes. With affordable pricing they want women to know they don’t have to blow a couple hundred for a dress that doesn’t compromise on style or quality.
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==<div style="background: #404041;font-weight: light; padding:0.3em; text-transform:uppercase;letter-spacing:0.1em;font-size:18px; font-family: 'Century Gothic'"><font color=#ffffff><center>Business Problems</center></font></div>==
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Being in the fast-fashion industry, Dressabelle launches new fashion collection each week, the fast-paced nature of their merchandising, logistics and marketing has given little room to explore how data could aid their decision process.
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For merchandising, know what to purchase, how much to purchase are important business decisions that have to be made on a regular basis. Dressabelle lacks a data-driven feedback loop on which products have done well and which can be done better.
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 +
Part and parcel of marketing is to attract new and old customers, dressabelle faces two main challenges. First, they are unclear how well are their marketing efforts are paying off, current marketing investments into Google and Facebook do not have clear return of investments. Second, it is crucial to identify which customers to target their marketing efforts into, current practice includes promotions to 'lost' customers.
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==<div style="background: #404041;font-weight: light; padding:0.3em; text-transform:uppercase;letter-spacing:0.1em;font-size:18px; font-family: 'Century Gothic'"><font color=#ffffff><center>Motivation</center></font></div>==
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Dressabelle aims to be a leading player in the fashion industry, this require them to kept in the know of the latest fashion trends, streamline their internal operations and as well as understand their customers. The use of analytics for merchandising and marketing will give them a competitive advantage over other players in the market.
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==<div style="background: #404041;font-weight: light; padding:0.3em; text-transform:uppercase;letter-spacing:0.1em;font-size:18px; font-family: 'Century Gothic'"><font color=#ffffff><center>Project Objectives</center></font></div>==
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The aim of this project is to help Dressabelle with understanding sales patterns to ultimately improve sales:
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 +
1. <b>Merchandising decisions</b> - Breaking down product sales by product attributes, allowing merchandiser to purchase more of what performs well.
 +
 
 +
2. <b>Marketing analytics</b> - Identifying cost & profit effective marketing mediums. Customer segmentation for effective targeted marketing.
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==<div style="background: #404041;font-weight: light; padding:0.3em; text-transform:uppercase;letter-spacing:0.1em;font-size:18px; font-family: 'Century Gothic'"><font color=#ffffff><center>Project Scope</center></font></div>==
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<b>Data collection</b> - Gather transactions, web analytics data from Dressabelle
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<b>Data preparation</b> - Cleaning data and anonymising/censoring data
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<b>Preliminary data analysis</b> - Generation of association rules and contextualising it with product information.
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<b>Product breakdown</b> - Understanding sales by product and it's attributes
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<b>Cohort analysis</b> - Grouping customers by various traits such as by date of first purchase, category of purchase and channels of aquisition.
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==<div style="background: #404041;font-weight: light; padding:0.3em; text-transform:uppercase;letter-spacing:0.1em;font-size:18px; font-family: 'Century Gothic'"><font color=#ffffff><center>Stakeholders</center></font></div>==
  
4FINGERS was founded after its creators discovered the best Korean-styled fried chicken in New York’s Koreatown - they knew they had to share it. The recipe has since been refined and perfected, and its fried chicken has been constantly raved about since its inception in ION Orchard mall in 2009. In just two years, 4FINGERS added six more stores scattered all over sunny Singapore. Since then, they have expanded regionally to our neighbours - Malaysia and Indonesia.
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Project Supervisor:
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<b>Prof Kam Tin Seong</b>, Associate Professor of Information Systems; Senior Advisor, SIS
  
With its aggressive expansion locally and regionally, 4FINGERS top-level management realised the value which data analytics is able to bring to the company, in terms of optimising their merchandising, menu offerings, and improving their bottom-line. As such, they hope to continue to derive valuable insights from their data to help them propel their growth even further, locally and regionally.
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Sponsor:
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<b>Gwen Ang</b>, Marketing Manager, Dressabelle

Latest revision as of 01:20, 23 February 2017

PROJECTS

HOME

 

ABOUT US

 

PROJECT OVERVIEW

 

PROJECT FINDINGS

 

PROJECT MANAGEMENT

 

DOCUMENTATION

Project Overview

Dressabelle Landing.png


Dressabelle is a leading online fast-fashion retailer in Singapore. Having established themselves online since 2012, Dressabelle has also expanded to 6 physical stores in malls around Singapore. Dressabelle set themselves apart with not one but two fresh fashion collection every week. Their mission is for women to feel confident about themselves when they dress in their clothes. With affordable pricing they want women to know they don’t have to blow a couple hundred for a dress that doesn’t compromise on style or quality.

Business Problems

Being in the fast-fashion industry, Dressabelle launches new fashion collection each week, the fast-paced nature of their merchandising, logistics and marketing has given little room to explore how data could aid their decision process.

For merchandising, know what to purchase, how much to purchase are important business decisions that have to be made on a regular basis. Dressabelle lacks a data-driven feedback loop on which products have done well and which can be done better.

Part and parcel of marketing is to attract new and old customers, dressabelle faces two main challenges. First, they are unclear how well are their marketing efforts are paying off, current marketing investments into Google and Facebook do not have clear return of investments. Second, it is crucial to identify which customers to target their marketing efforts into, current practice includes promotions to 'lost' customers.

Motivation

Dressabelle aims to be a leading player in the fashion industry, this require them to kept in the know of the latest fashion trends, streamline their internal operations and as well as understand their customers. The use of analytics for merchandising and marketing will give them a competitive advantage over other players in the market.

Project Objectives

The aim of this project is to help Dressabelle with understanding sales patterns to ultimately improve sales:

1. Merchandising decisions - Breaking down product sales by product attributes, allowing merchandiser to purchase more of what performs well.

2. Marketing analytics - Identifying cost & profit effective marketing mediums. Customer segmentation for effective targeted marketing.

Project Scope

Data collection - Gather transactions, web analytics data from Dressabelle

Data preparation - Cleaning data and anonymising/censoring data

Preliminary data analysis - Generation of association rules and contextualising it with product information.

Product breakdown - Understanding sales by product and it's attributes

Cohort analysis - Grouping customers by various traits such as by date of first purchase, category of purchase and channels of aquisition.

Stakeholders

Project Supervisor: Prof Kam Tin Seong, Associate Professor of Information Systems; Senior Advisor, SIS

Sponsor: Gwen Ang, Marketing Manager, Dressabelle