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

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* Customer influx during lunch hours and after dinner
 
* Customer influx during lunch hours and after dinner
 
** Influx during lunch is prominent during days of collection launch
 
** Influx during lunch is prominent during days of collection launch
<b>Renevue</b>
+
<b>Revenue</b>
 
* Sales reflect a season pattern which peaks in May
 
* Sales reflect a season pattern which peaks in May
 
** 2016 did not follow this pattern, instead reflecting a peak in January
 
** 2016 did not follow this pattern, instead reflecting a peak in January
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* Basic colors, such as Blue, White, Navy, Blue, and Grey, prove to be the most popular
 
* Basic colors, such as Blue, White, Navy, Blue, and Grey, prove to be the most popular
 
* Black is by far the most popular color
 
* Black is by far the most popular color
 
  
 
==<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>Exploratory Data Analysis</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>Exploratory Data Analysis</center></font></div>==

Revision as of 02:07, 23 February 2017

PROJECTS

HOME

 

ABOUT US

 

PROJECT OVERVIEW

 

PROJECT FINDINGS

 

PROJECT MANAGEMENT

 

DOCUMENTATION

Mid-Term

Executive Summary

User Findings

  • One third of the registered users do not make purchases
  • Top 25% of most valuable customers gives 72.52% of revenue
  • Top customers on average orders 12 times more than bottom tier customer
  • Spike in growth of user base in 2013 to 2014
  • Large growth in offline buyers
    • Due to offline referrals
  • Huge spikes of customers on Sunday and Thursday which corresponds to their weekly collection launches
  • Customer influx during lunch hours and after dinner
    • Influx during lunch is prominent during days of collection launch

Revenue

  • Sales reflect a season pattern which peaks in May
    • 2016 did not follow this pattern, instead reflecting a peak in January

Orders

  • Decrease in the average product pricing leads to an increase in customer order size and an overall increase sales revenue generated per order
  • Hypothesis: Dressabelle's customer base is price sensitive

Order Source

  • Organic and referrals are the order mediums for at least 54% of the new customers
  • Email is the most effective medium for generating subsequent orders

Products

  • Dressabelle’s product mix offering mainly comprises of Dresses which make up 63% of the total products offered
    • Tops are a far second

Products by Categories and Color

  • Free sizing is significantly more prominent in Tops and Outerwear than compared to other categories
  • Products of size S and size M are generally purchased more often than products of size L
  • Basic colors, such as Blue, White, Navy, Blue, and Grey, prove to be the most popular
  • Black is by far the most popular color

Exploratory Data Analysis

Understanding User Base

Our team brokedown Dressabelle's user base in three categories:

- Guest Customers (those that purchase without an account)

- Registered Customers (those that purchase with a registered account)

- Registered Users (those that have an account but has never purchased anything)

Userbreakdown.png

Findings: ⅓ of total users do not make purchases


Our team weighted customers according to their contribution towards Dressabelle's revenue.

Customervalue.png

Findings:

1. Top 25% of most valuable customers gives 72.52% of revenue

2. Top customers on average orders 12 times more than bottom tier customer


Understanding User Growth
Userreg.png

Findings:

1. Huge growth in 2013-14

2. Large growth of non-buyers in 2016, possibility due to offline referral

Regbyweek.png

Findings:

Huge spikes of customers on Sunday and Thursday, which corresponds to weekly collection launches


Regbyhour.png

Findings: 1. Customers influx during lunch hours and after dinner

2. Influx of customers during lunch is prominent during collection launch days


Understanding Revenue
Revenue.png

Findings: 1. Sales tends to peak in May

2.Sales pattern in 2016 did not follow the usual sales trend