Difference between revisions of "ANLY482 AY2016-17 T2 Group21 : PROJECT FINDINGS"
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Our team weighted customers according to their contribution towards Dressabelle's revenue. | Our team weighted customers according to their contribution towards Dressabelle's revenue. | ||
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| − | <b>Findings:</b> | + | <div style="text-align:center"><b>Findings:</b> |
1. Top 25% of most valuable customers gives 72.52% of revenue | 1. Top 25% of most valuable customers gives 72.52% of revenue | ||
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<div style="font-size:18px"><b>Understanding User Growth</b></div> | <div style="font-size:18px"><b>Understanding User Growth</b></div> | ||
| − | + | [[File:userreg.png|center|840px]] | |
| − | <b>Findings:</b> | + | <div style="text-align:center"><b>Findings:</b> |
1. Huge growth in 2013-14 | 1. Huge growth in 2013-14 | ||
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</div> | </div> | ||
| − | + | [[File:regbyweek.png|center|840px]] | |
| − | <b>Findings:</b> | + | <div style="text-align:center"><b>Findings:</b> |
Huge spikes of customers on Sunday and Thursday, which corresponds to weekly collection launches | Huge spikes of customers on Sunday and Thursday, which corresponds to weekly collection launches | ||
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| − | <b>Findings:</b> | + | <div style="text-align:center"><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> | ||
| − | + | [[File:revenue.png|center|840px]] | |
| − | <b>Findings:</b> | + | <div style="text-align:center"><b>Findings:</b> |
1. Sales tends to peak in May | 1. Sales tends to peak in May | ||
2.Sales pattern in 2016 did not follow the usual sales trend | 2.Sales pattern in 2016 did not follow the usual sales trend | ||
</div> | </div> | ||
Revision as of 02:20, 23 February 2017
| Mid-Term |
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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
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.
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
1. Huge growth in 2013-14
2. Large growth of non-buyers in 2016, possibility due to offline referral
Huge spikes of customers on Sunday and Thursday, which corresponds to weekly collection launches
1. Customers influx during lunch hours and after dinner
2. Influx of customers during lunch is prominent during collection launch days
1. Sales tends to peak in May
2.Sales pattern in 2016 did not follow the usual sales trend
