ANLY482 AY2016-17 T2 Group21 : PROJECT FINDINGS

From Analytics Practicum
Jump to navigation Jump to search

PROJECTS

HOME

 

ABOUT US

 

PROJECT OVERVIEW

 

PROJECT FINDINGS

 

PROJECT MANAGEMENT

 

DOCUMENTATION

Mid-Term

Executive Summary

Users

  • One third of the registered users do not make purchases
  • Top 25% of most valuable customers gives 72.52% of revenue
  • On average, customers in the top segment orders 12 times more than bottom tier customers
  • 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


Understanding Orders

Orders.png

Findings:

1. Decrease in the average pricing of a product results in the increase in order size and overall revenue earned


Ordersource.png

Findings:

1. Organic and referrals comprise of at least 54% of new customers

2. Email remains effective in generating repeated sales

Limitations:

1. Accuracy of tracking

2. Tracking starts mid 2013


Understanding Products

Products.png

Findings:

1. Dressabelle’s main offering is in the dress category of 63%

2. Tops are a far second

Bycategory.png

Findings:

1. For tops and outerwear, free sizing is significantly more prominent than the other categories

2. In general, sizes S & M are generally more popular than L

Limitations:

Categories are not accurately defined as of now

Color.png

Findings:

1. Basic colors (Blue, White, Navy, Blue & Grey) prove the most popular

2. Black is by far the most popular colour


Merchandising

Proposed metrics

1. Sell Through Rate

Quantity of products sold ( in 2 days ) / Quantity of products in inventory

This gives us the stock efficiency

2. Sales Velocity

Quantity of products sold / Time window( 2 days window )

This gives us the product popularity

Graphh.png


Below is a bubble plot of Bubble Plot Video (By SKU).gif


Bubble Plot Video (By Category).gif