ANLY482 AY2016-17 T2 Group21 : PROJECT FINDINGS
Mid-Term |
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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
Renevue
- 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
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)
Our team weighted customers according to their contribution towards Dressabelle's revenue.
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
Findings:
1. Huge growth in 2013-14
2. Large growth of non-buyers in 2016, possibility due to offline referral
Findings:
Huge spikes of customers on Sunday and Thursday, which corresponds to weekly collection launches
Findings: 1. Customers influx during lunch hours and after dinner
2. Influx of customers during lunch is prominent during collection launch days