### Introduction and Schema Diagram¶

• In this project we're working with a modified version of a database called Chinook. The Chinook database contains information about a fictional digital music shop - kind of like a mini-iTunes store.

• The Chinook database contains information about the artists, songs, and albums from the music shop, as well as information on the shop's employees, customers, and the customers purchases. This information is contained in eleven tables.

• We'll run queries and provide solutions to 4 Business Questions.

### Scenario 1 : Selecting Albums to Purchase¶

The Chinook record store has just signed a deal with a new record label, and we've been tasked with selecting the first three albums that will be added to the store, from a list of four.

All four albums are by artists that don't have any tracks in the store right now - we have the artist names, and the genre of music they produce:

Artist Name Genre
Regal Hip-Hop
Red Tone Punk
Meteor and the Girls Pop
Slim Jim Bites Blues

The record label specializes in artists from the USA, and they have given Chinook some money to advertise the new albums in the USA, so we're interested in finding out which genres sell the best in the USA.

### Creating Helper Functions¶

• First, we'll import SQLite, pandas and matplotlib modules, and use the magic command %matplotlib inline to make sure any plots render in the notebook.
• Next, we will create three helper functions:
• A run_query() function, that takes a SQL query as an argument and returns a pandas dataframe of that query.
• A run_command() function that takes a SQL command as an argument and executes it using the sqlite module.
• A show_tables() function that calls the run_query() function to return a list of all tables and views in the database.
• Next, we will run the show_tables() function.
In [1]:
import pandas as pd
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

def run_query(q):
with sqlite3.connect('chinook.db') as conn:

def run_command(c):
with sqlite3.connect('chinook.db') as conn:
conn.isolation_level = None
conn.execute(c)

def show_tables():
q = '''
SELECT
name,
type
FROM sqlite_master
WHERE type IN ("table", "view");'''
return run_query(q)

show_tables()
Out[1]:
name type
0 album table
1 artist table
2 customer table
3 employee table
4 genre table
5 invoice table
6 invoice_line table
7 media_type table
8 playlist table
9 playlist_track table
10 track table

### Finding Best-Selling Genres¶

• We will write a query that returns each genre, with the number of tracks sold in absolute numbers and in percentages.
• And, then create a plot to show this data.
In [2]:
q1 = '''
WITH tracks_sold_usa AS
(
SELECT il.*
FROM invoice_line il
INNER JOIN invoice i ON i.invoice_id = il.invoice_id
INNER JOIN customer c ON c.customer_id = i.customer_id
WHERE c.country = 'USA'
)

SELECT
g.name,
COUNT(tsu.invoice_line_id) 'tracks_sold',
CAST(COUNT(tsu.invoice_line_id) AS FLOAT)/ (SELECT COUNT(*) FROM tracks_sold_usa) 'tracks_sold_perc'
FROM tracks_sold_usa tsu
INNER JOIN track t ON tsu.track_id = t.track_id
INNER JOIN genre g ON g.genre_id = t.genre_id
GROUP BY 1
ORDER BY 2 DESC
LIMIT 10
'''

Genres with Amount of Tracks Sold

In [3]:
run_query(q1)
Out[3]:
name tracks_sold tracks_sold_perc
0 Rock 561 0.533777
1 Alternative & Punk 130 0.123692
2 Metal 124 0.117983
3 R&B/Soul 53 0.050428
4 Blues 36 0.034253
5 Alternative 35 0.033302
6 Latin 22 0.020932
7 Pop 22 0.020932
8 Hip Hop/Rap 20 0.019029
9 Jazz 14 0.013321

Plotting Genre vs Track Percentage

In [4]:
genre_albums_sold = run_query(q1)
genre_albums_sold.set_index("name", drop=True, inplace=True)

genre_albums_sold.plot.barh(
title = "Top Selling Genres in USA",
xlim = (0, 625),
colormap = plt.cm.Accent
)

plt.ylabel('')

for key, value in enumerate(list(genre_albums_sold.index)):
score = genre_albums_sold.loc[value, 'tracks_sold']
label = (genre_albums_sold.loc[value, 'tracks_sold_perc'] * 100).astype(int).astype(str) + '%'
plt.annotate(str(label), (score+10, key - 0.15))

plt.show()

RECOMMENDATION:

The recommended artists according to popularity of their genres (in Descreasing Order) are:

• Red Stone (Punk) (12%)
• Slim Jim Bites (Blues) (3%)
• Meteor and the Girls (Pop) (2%)

However, the total sales of these genres combined is just 17%.
We should instead look out for artists in Rock genre, since it alone sells for 53%

### Scenario 2: Analysing Employee Sales Performance¶

Each customer for the Chinook store gets assigned to a sales support agent within the company when they first make a purchase.

We want to analyze Employee Sales Performance.
For this we will have to analyze the purchases of customers belonging to each employee.

### Finding Top Sales Employees¶

• We will write a query that finds the total dollar amount of sales assigned to each sales support agent within the company.
• We would need to add some extra attributes for the employees (e.g Date of joining, etc). Since they are relevant to the analysis.
In [5]:
q2 = '''
SELECT
e.first_name || " " || e.last_name 'Employee Name',
e.hire_date 'Hiring Date',
SUM(total) 'Sales'
FROM invoice i
INNER JOIN customer c ON c.customer_id = i.customer_id
INNER JOIN employee e ON e.employee_id = c.support_rep_id
GROUP BY c.support_rep_id
'''

Employees with Hiring Dates and Sales

In [6]:
run_query(q2)
Out[6]:
Employee Name Hiring Date Sales
0 Jane Peacock 2017-04-01 00:00:00 1731.51
1 Margaret Park 2017-05-03 00:00:00 1584.00
2 Steve Johnson 2017-10-17 00:00:00 1393.92

Plotting Employees vs Sales Data

In [7]:
employee_sales = run_query(q2)
employee_sales.set_index("Employee Name", drop=True, inplace=True)
employee_sales.plot.barh(
colormap = plt.cm.Accent,
xlim = (0, 2100)
)

for key, value in enumerate(list(employee_sales.index)):
plt.annotate(str(employee_sales.loc[value, 'Sales'].round(2)), (employee_sales.loc[value,'Sales'] +50, key))

plt.ylabel('')
plt.show()

RECOMMENDATION:

Here Mr. Jean Peacock has a margin of around 20% compared to the lowest performer (Mr. Steve Johnson).
However, we may also note that the difference in Hiring Date corresponds to the Sales difference.

### Scenario 3: Analysing Sales By Country¶

We want to analyze Sales By country. In particular, we will calculate:

• total number of customers
• total value of sales
• average value of sales per customer
• average order value

### Table - Total No. of Customers, Sales, Average Sales, Average Orders Per Country¶

• We will write a query that collates data on purchases from different countries
• Whenever a country has only one customer, we'll collect them into an "Other" group.
• The results will be sorted by the total sales from highest to lowest, with the "Other" group at the very bottom.
In [8]:
q3 = '''
WITH country_or_other AS
(
SELECT
CASE
WHEN
(SELECT
COUNT(*)
FROM customer
WHERE country = c.country
) = 1 THEN 'Other'
ELSE c.country
END AS country,
c.customer_id,
il.*
FROM invoice_line il
INNER JOIN invoice i ON i.invoice_id = il.invoice_id
INNER JOIN customer c ON c.customer_id = i.customer_id
)

SELECT
Country,
Customers,
Total_sales,
Average_customer_life,
Average_order
FROM
(
SELECT
country 'Country',
COUNT(DISTINCT customer_id) 'Customers',
SUM(unit_price) 'Total_sales',
SUM(unit_price) / COUNT(DISTINCT customer_id) 'Average_customer_life',
SUM(unit_price) / COUNT(DISTINCT invoice_id) 'Average_order',
CASE
WHEN country = 'Other' THEN 1
ELSE 0
END AS sort
FROM country_or_other
GROUP BY country
ORDER BY sort ASC, total_sales DESC
);
'''

run_query(q3)
Out[8]:
Country Customers Total_sales Average_customer_life Average_order
0 USA 13 1040.49 80.037692 7.942672
1 Canada 8 535.59 66.948750 7.047237
2 Brazil 5 427.68 85.536000 7.011148
3 France 5 389.07 77.814000 7.781400
4 Germany 4 334.62 83.655000 8.161463
5 Czech Republic 2 273.24 136.620000 9.108000
6 United Kingdom 3 245.52 81.840000 8.768571
7 Portugal 2 185.13 92.565000 6.383793
8 India 2 183.15 91.575000 8.721429
9 Other 15 1094.94 72.996000 7.448571

### Plotting Sales By Country¶

• For each dimension, we will create a visualization which demonstrates the data collated in previous step.
In [22]:
from numpy import arange
import matplotlib

# Required Dataframe
country_sales = run_query(q3)
country_sales.set_index('Country', inplace=True, drop=True)

# Cols is array containing names of columns to be plotted
cols = list(country_sales.columns)

# Required Figure Container
fig = plt.figure(figsize = (18, 12))

# Bar Positions (Left) for Bar Plots
positions = arange(country_sales.shape[0])

# Colors Array for Bar Plots
colors = matplotlib.cm.get_cmap('viridis')
arr1 = np.append(arange(0, 0.9, 0.1), [0.99])
rgba = list(colors(i) for i in arr1)

#################################
#Top Left (Total_sales)(PieChart)

# Column to be plotted copied from Dataframe
customers_in_country = country_sales[cols[1]].copy().rename('')

# Explode is used for Removing a part of Pie a littel bit to highlight
explode = list(0.1 if i == 'Other' else 0 for i in customers_in_country.index)

# Plotting Data
ax1 = customers_in_country.plot(
kind='pie',
colormap = plt.cm.viridis,
startangle = +90,
explode = explode,
title = 'Total Sales per Country'
)
ax1.set_ylabel('')

#########################################################
# Top Right (Pct Sales vs Pct Customers) (Grouped Bar Plot)

# Columns to be Plotted
cust_vs_sales_cols = cols[:2]
cust_vs_sales = country_sales[cust_vs_sales_cols].copy()
cust_vs_sales.index.name = ''

for i in cust_vs_sales_cols:
cust_vs_sales[i] /= cust_vs_sales[i].sum() / 100

# Plotting Data
ax2.bar(
[x - 0.15 for x in positions],
cust_vs_sales['Customers'],
0.4,
#     color = "#FFB7A2"
color='#355F8D'

)
ax2.bar(
[0.25 + x for x in positions],
cust_vs_sales['Total_sales'],
0.4,
#     color = '#96BBE6'
color = '#FDE736'
)

ax2.set_title('$\%$ Sales vs. $\%$ Customers')
ax2.legend(list(cust_vs_sales.columns), loc='upper center')

###########################################################
# Bottom Left (Average Order (Pct Diff From Mean)) (Bar Plot)
avg_cust_order = country_sales['Average_order'].mean()
diff_from_avg = country_sales['Average_order'] - avg_cust_order

ax3.bar(
positions,
diff_from_avg,
color = rgba
)
ax3.axhline(0, c=(0/255, 0/255, 0/255), alpha=0.2)
ax3.set_title('Average Order\n($\%$ Diff From Mean)')

# ##########################################################
# Bottom Right(Customer Lifetime Value (Dollars)) (Bar Plot)
ax4.bar(
positions,
country_sales['Average_customer_life'],
color = rgba
)

##########################################################
# Improving Plot Aesthetics
axes_objects = [ax2, ax3, ax4]
for i in axes_objects:
i.tick_params(top=False, right=False, left=False, bottom=False)
i.set_xticks(arange(cust_vs_sales.shape[0]))
i.set_xticklabels(country_sales.index, rotation = 70)
for k, v in i.spines.items():
if (k == 'right' or k == 'top'):
v.set_visible(False)

plt.savefig('Comparison.png')
plt.show()

### RECOMMENDATION¶

Based on the data, there may be opportunity in the following countries:

Czech Republic
United Kingdom
India

It's worth keeping in mind that because the amount of data from each of these countries is relatively low. Because of this, we should be cautious spending too much money on new marketing campaigns, as the sample size is not large enough to give us high confidence. A better approach would be to run small campaigns in these countries, collecting and analyzing the new customers to make sure that these trends hold with new customers.

### Scenario 4: Analyze purchases of Albums vs Individual Tracks¶

In the Chinook store, customers are not allowed to purchase a whole album, and then add individual tracks to that same purchase. When customers purchase albums they are charged the same price as if they had purchased each of those tracks separately.

Management is considering a change in the purchasing strategy to save money. The new strategy is to purchase only the most popular tracks from each album from record companies, instead of purchasing every track from an album.

We want to analyze what percentage of purchases are individual tracks vs whole albums. It will help management to understand the effect this decision might have on overall revenue.

### Finding Number and Percentage of invoices in Categories (Album vs Tracks)¶

• We will write two queries:
• First, we will categorize each invoice as either an album purchase or not.
• Second, we will calculate the following summary statistics:
• Number of invoices
• Percentage of invoices
In [14]:
q4 = '''
WITH invoice_first_track AS
(
SELECT
invoice_id,
MIN(track_id) 'first_track_id'
FROM invoice_line
GROUP BY 1
)

SELECT
ifs.*,
CASE
WHEN
(
SELECT t.track_id FROM track t
WHERE t.album_id = (
SELECT t2.album_id FROM track t2
WHERE t2.track_id = ifs.first_track_id
)

EXCEPT

SELECT il2.track_id FROM invoice_line il2
WHERE il2.invoice_id = ifs.invoice_id
) IS NULL
AND
(
SELECT il2.track_id FROM invoice_line il2
WHERE il2.invoice_id = ifs.invoice_id

EXCEPT

SELECT t.track_id FROM track t
WHERE t.album_id = (
SELECT t2.album_id FROM track t2
WHERE t2.track_id = ifs.first_track_id
)
) IS NULL
THEN "yes"
ELSE "no"
END AS "album_purchase"
FROM invoice_first_track ifs
'''

run_query(q4)
Out[14]:
invoice_id first_track_id album_purchase
0 1 1158 yes
1 2 201 no
2 3 2516 no
3 4 748 no
4 5 1986 yes
5 6 30 no
6 7 42 no
7 8 81 no
8 9 196 no
9 10 2663 no
10 11 610 no
11 12 92 no
12 13 2553 no
13 14 541 no
14 15 807 no
15 16 16 no
16 17 55 no
17 18 1027 no
18 19 105 no
19 20 60 no
20 21 13 no
21 22 383 no
22 23 1 yes
23 24 1146 yes
24 25 32 no
25 26 85 no
26 27 1280 no
27 28 36 no
28 29 479 no
29 30 33 no
... ... ... ...
584 585 49 no
585 586 20 no
586 587 94 no
587 588 18 no
588 589 2271 yes
589 590 1362 yes
590 591 162 no
591 592 465 no
592 593 57 no
593 594 476 no
594 595 46 no
595 596 12 no
596 597 46 no
597 598 1000 no
598 599 19 no
599 600 1006 no
600 601 14 no
601 602 164 no
602 603 481 no
603 604 1755 yes
604 605 1128 no
605 606 2003 yes
606 607 30 no
607 608 3060 no
608 609 1636 no
609 610 814 no
610 611 57 no
611 612 2204 yes
612 613 1126 no
613 614 2650 no

614 rows × 3 columns

Statistics based on above data

In [15]:
q6 = '''
WITH invoice_first_track AS
(
SELECT
invoice_id,
MIN(track_id) 'first_track_id'
FROM invoice_line
GROUP BY 1
)

SELECT
album_purchase,
COUNT(invoice_id) number_of_invoices,
CAST(COUNT(invoice_id) AS FLOAT)*100 / (SELECT COUNT(*) FROM invoice) 'percent'
FROM
(
SELECT
ifs.*,
CASE
WHEN
(
SELECT t1.track_id from track t1
WHERE t1.album_id = (
SELECT t2.album_id FROM track t2
WHERE t2.track_id = ifs.first_track_id
)
EXCEPT

SELECT il2.track_id FROM invoice_line il2
WHERE il2.invoice_id = ifs.invoice_id
) IS NULL
AND
(
SELECT il2.track_id FROM invoice_line il2
WHERE il2.invoice_id = ifs.invoice_id

EXCEPT

SELECT t1.track_id from track t1
WHERE t1.album_id = (
SELECT t2.album_id FROM track t2
WHERE t2.track_id = ifs.first_track_id
)
) IS NULL
THEN 'yes'
ELSE 'no'
END AS 'album_purchase'
FROM invoice_first_track ifs
)
GROUP BY 1
'''

run_query(q6)
Out[15]:
album_purchase number_of_invoices percent
0 no 500 81.433225
1 yes 114 18.566775

### RECOMMENDATION¶

Here, we can we observe that there are around 18.5% purchases are being done as complete albums.

So we may recommend against going for only individual tracks. Since, around 1/5th of the revenue is being generated by Albums. However, more analysis needs to be done in order to provide a sound recommendation.