Difference between revisions of "ANLY482 AY2016-17 T2 Group21 : PROJECT FINDINGS"
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| + | ! style="background: #ffffff;font-weight: light; padding:0.1em; text-transform:uppercase;letter-spacing:0.1em;font-size:15px; font-family: 'Century Gothic'"| [[ANLY482_AY2016-17_T2_Group21 : PROJECT MIDTERM FINDINGS | <span style="color:#3d3d3d">Mid-Term</span>]] | ||
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| + | ! style="font-size:15px; text-align: center; border-top:solid #ffffff; border-bottom:solid #ffffff" width="150px"| [[Analysis of User and Merchant Dropoff for Sugar App_Finals| <span style="color:#3d3d3d">Finals</span>]] | ||
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| + | ==<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>Executive Summary</center></font></div>== | ||
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| + | <b>User Findings</b> | ||
| + | * 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 | ||
| + | <b>Renevue</b> | ||
| + | * Sales reflect a season pattern which peaks in May | ||
| + | ** 2016 did not follow this pattern, instead reflecting a peak in January | ||
| + | <b>Orders</b> | ||
| + | * 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 | ||
| + | <b>Order Source</b> | ||
| + | * 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 | ||
| + | <b>Products</b> | ||
| + | * Dressabelle’s product mix offering mainly comprises of Dresses which make up 63% of the total products offered | ||
| + | ** Tops are a far second | ||
| + | <b>Products by Categories and Color</b> | ||
| + | * 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 | ||
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==<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>== | ||
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<div>[[File:regbyhour.png|600px]] | <div>[[File:regbyhour.png|600px]] | ||
<b>Findings:</b> | <b>Findings:</b> | ||
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1. Customers influx during lunch hours and after dinner | 1. Customers influx during lunch hours and after dinner | ||
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<div>[[File:revenue.png|600px]] | <div>[[File:revenue.png|600px]] | ||
<b>Findings:</b> | <b>Findings:</b> | ||
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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 | ||
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Revision as of 02:06, 23 February 2017
| 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
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
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)
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
Understanding User Growth
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
Understanding Revenue
Findings: 1. Sales tends to peak in May
2.Sales pattern in 2016 did not follow the usual sales trend
