Difference between revisions of "ANLY482 AY2016-17 T2 Group21 : PROJECT FINDINGS"
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| − | <div>[[File:regbyhour.png|600px]] | + | <div>[[File:regbyhour.png|600px]] |
<b>Findings:</b> | <b>Findings:</b> | ||
1. Customers influx during lunch hours and after dinner | 1. Customers influx during lunch hours and after dinner | ||
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<div style="font-size:18px"><b>Understanding Revenue</b></div> | <div style="font-size:18px"><b>Understanding Revenue</b></div> | ||
| − | <div>[[File:revenue.png|600px]]</div> | + | <div>[[File:revenue.png|600px]] |
| + | <b>Findings:</b> | ||
| + | 1. Sales tends to peak in May | ||
| + | |||
| + | 2.Sales pattern in 2016 did not follow the usual sales trend | ||
| + | </div> | ||
Revision as of 02:06, 23 February 2017
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)
Findings: ⅓ of total users do not make purchases
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
Findings: 1. Sales tends to peak in May
2.Sales pattern in 2016 did not follow the usual sales trend
