This is a second blog post of this marketing analytics series on a coupon e-commerce website (ponpare.jp). I will review some of the user statistics and purchasing patterns. In the continuing posts, I will cover more in-depth metrics to understand users and their purchasing behaviors.
import os.path
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import matplotlib.cm as cm
import pandas as pd
%matplotlib inline
user_list = pd.read_csv("user_list.csv")
user_list['REG_DATE'] = pd.to_datetime(user_list['REG_DATE'])
user_list['REG_MONTH'] = user_list['REG_DATE'].dt.month
user_list['REG_HOUR'] = user_list['REG_DATE'].dt.hour
User statistics¶
There are a total of 22,873 unique users in this user list with over 95% active users. Percentages of males and female users are similar with slightly more male users. Looking at active and inactive users separately, while genders represented among active users are similar to that of the total list, there are 15% more males among inactive users who have withdrawn their registration.
total_users = user_list['USER_ID_hash'].unique().shape[0]
print '*Total # of users: ', total_users
print '\n*Gender percentages:' # Gender percentages
f = user_list['USER_ID_hash'][user_list['SEX_ID']=='f'].unique().shape[0]
m = user_list['USER_ID_hash'][user_list['SEX_ID']=='m'].unique().shape[0]
print '% of female users: ', round(float(f)/(total_users),4)*100, '%'
print '% of male users: ', round(float(m)/(total_users),4)*100, '% \n'
print '*Registration status:'
users_withdrew = user_list[user_list['WITHDRAW_DATE'].isnull() == False]
users_active = user_list[user_list['WITHDRAW_DATE'].isnull()]
print '% of active users in the User list: ', round(float(users_active.shape[0])/total_users,4)*100, '%'
print '% of inactive users in the User list: ', round(float(users_withdrew.shape[0])/total_users,4)*100, '%\n'
print '*Among active users:'
active_f = users_active['USER_ID_hash'][user_list['SEX_ID']=='f'].shape[0]
active_m = users_active['USER_ID_hash'][user_list['SEX_ID']=='m'].shape[0]
print '% of active female users: ', round(float(active_f)/(users_active.shape[0]),4)*100, '%'
print '% of active male users: ', round(float(active_m)/(users_active.shape[0]),4)*100, '%'
print '\n*Among inactive users:'
inactive_f = users_withdrew['USER_ID_hash'][user_list['SEX_ID']=='f'].shape[0]
inactive_m = users_withdrew['USER_ID_hash'][user_list['SEX_ID']=='m'].shape[0]
print '% of inactive female users: ', round(float(inactive_f)/(users_withdrew.shape[0]),4)*100, '%'
print '% of inactive male users: ', round(float(inactive_m)/(users_withdrew.shape[0]),4)*100, '%'
User Age¶
Next, I look at a few other basic distribution of different user profiles. This is a distribution of users' ages. The single largest user group is between 35 and 40, if we use each bin size as 5.
plt.figure(figsize=(13,6))
sns.distplot(user_list['AGE'],bins = range(0,100,5),kde=False)
plt.xticks(np.arange(min(range(0,100,5)), max(range(0,100,5))+1, 5.0))
plt.xlabel('Age')
plt.ylabel('# of Users')
plt.title('User Age Distribution')
plt.show()
Just because I'm curious, I also look at user age distributions for inactive user groups and active user groups, to see if there are any differences. While the distributions look different, this could be attributable to the smaller size of the inactive user group (5% vs 95%).
plt.figure(figsize=(17,4))
plt.subplot(1, 2, 1)
sns.distplot(users_active['AGE'],bins = range(0,100,5),kde=False)
plt.xticks(np.arange(min(range(0,100,5)), max(range(0,100,5))+1, 5.0))
plt.xlabel('Age')
plt.ylabel('# of Active Users')
plt.title('Active User Age Distribution')
plt.subplot(1, 2, 2)
sns.distplot(users_withdrew['AGE'],bins = range(0,100,5),kde=False)
plt.xticks(np.arange(min(range(0,100,5)), max(range(0,100,5))+1, 5.0))
plt.xlabel('Age')
plt.ylabel('# of Inactive Users')
plt.title('Inactive User Age Distribution')
plt.show()
User Registration¶
It would be interesting to also look at purchasing patterns of each group (active vs. inactive) to see if inactive users purchase differenly or something causes them to cancel their status. For now, I will continue looking at registration characteristics by user.
This histogram below shows that most users signed up in 2011 (60% of users). This makes wonder how this dataset was chosen, and if it was random, what led such a spike in 2011 and whether that would affect further purchasing behaviors.
plt.figure(figsize=(10,6))
sns.distplot(user_list['REG_DATE'].dt.year,bins=3, kde=False)
plt.ticklabel_format(useOffset=False)
plt.xticks(np.arange(min(user_list['REG_DATE'].dt.year), max(user_list['REG_DATE'].dt.year)+1, 1.0))
plt.xlabel('Year')
plt.ylabel('# of Users who registered')
plt.title('Registration hour by user')
plt.show()
The two histograms below shows what time of the year and what time of the day users are signing up for registration.
November and May show the highest percentage of user registration rate. Throughout the day, registration starts to go up in the morning and peaks at 5pm and again after 9pm. The histogram on the right has KDE(kernel density estimate) turned on, so the y-axis scale shows density, rather than number of users.
plt.figure(figsize=(17,4))
#plot user registration by month
plt.subplot(1, 2, 1)
sns.distplot(user_list['REG_DATE'].dt.month,bins=12, kde=False)
plt.xlabel('Month')
plt.ylabel('# of Users who registered')
plt.title('Registration month by user')
plt.xticks(np.arange(min(user_list['REG_DATE'].dt.month), max(user_list['REG_DATE'].dt.month)+1, 1.0))
plt.subplot(1, 2, 2)
sns.distplot(user_list['REG_DATE'].dt.hour, kde=True)
plt.xlabel('Hour')
plt.ylabel('# of Users who registered')
plt.title('Registration hour by user')
plt.xticks(np.arange(min(user_list['REG_DATE'].dt.hour), max(user_list['REG_DATE'].dt.hour)+1, 1.0))
plt.show()
In looking at the below histogram by month and gender, there are no significant differences in gender for each month in registration. There seems to be the largest difference in December when there are more male users signing up than female users.
#plot user registration by month and gender
age = user_list.groupby(['REG_MONTH','SEX_ID']).size().unstack()
age.plot(kind = 'bar', colormap = cm.Accent, figsize=(13,6))
plt.ylabel('# of Users who registered')
plt.title('Registration month by user gender')
plt.show()
The histogram below is by registration hour and user gender. There are no significant gender differences by hour (male>female) with the largest difference in 5pm, which also happens to be the peak time for overall registration.
There are only 5 hours where females register more than males which are mostly late mornings and early afternoons (10am to 1pm).
hour = user_list.groupby(['REG_HOUR','SEX_ID']).size().unstack()
print '# of Hours(female > male): ', hour[hour['f']>hour['m']].shape[0]
print '\nDetails of these 5 hours:'
print hour[hour['f']>hour['m']]
print '\n# of Hours(male > female): ', hour[hour['m']>hour['f']].shape[0]
hour.plot(kind = 'bar', colormap = cm.Accent, figsize=(13,6) )
plt.ylabel('# of Users who registered')
plt.title('Registration hour by user gender')
plt.show()
