Mobile insights

0. Import libraries



In [165]:

    
%matplotlib inline 

import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd

pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr_html', True)
import seaborn as sns #sets up styles and gives us more plotting options

1. Settings



In [166]:

    
# Time period 15th Jan - 26th April (arbitrary )

# API credentials
# Email address 705762800217-compute@developer.gserviceaccount.com
# Key IDs 948ee8e2a420ef14a5d5a29bd35104fe2f1e6ed4



In [167]:

    
# open file. It is requested via API explorer using request parameters:

#Account: TMRW Tech Hub
#Property: TMRW
#View: All Web Site Data
#ids: ga:123303369
#start-date: 2017-02-01
#end-date: 2017-04-30

#metrics
#ga:sessions
#ga:sessionsWithEvent
#ga:bounceRate

#dimensions
#ga:deviceCategory

2. Data import & transformation



In [203]:

    
# Open file
input_mob= pd.read_csv('files/TMRW_mob_dev.csv')



In [204]:

    
# rename columns
input_mob.columns=['Device','Sessions','Bounce Rate','Conversions','CR']



In [205]:

    
# group by device
input_mob = input_mob.set_index('Device')

input_mob









    Out[205]:






  
    
      
      Sessions
      Bounce Rate
      Conversions
      CR
    
    
      Device
      
      
      
      
    
  
  
    
      desktop
      4061
      52.647131
      114
      0.028072
    
    
      mobile
      2261
      62.627156
      30
      0.013268
    
    
      tablet
      270
      55.555556
      3
      0.011111

3. Calculate



In [206]:

    
mobile_Sessions = input_mob.loc['mobile','Sessions']
desktop_CR = input_mob.loc['desktop','CR']
mobile_Conversions = input_mob.loc['mobile','Conversions']
mobile_CR = input_mob.loc['mobile','CR']


conv_increase =  "%.0f" % (mobile_Sessions * desktop_CR - mobile_Conversions)
conv_increase = int(conv_increase)

#mobileSessions
#desktopCR
#mobileConversions
mobile_CR









    Out[206]:





0.013268465

4. Validate



In [207]:

    
#if mobile CR is greater, equel or less by more than 20% result is failed

dif = mobile_CR / desktop_CR

if dif > 0.8:
    result = False
    
else:
    result = True
    
result









    Out[207]:





True

5. Visualise



In [214]:

    
output_chart_mob = input_mob.loc['mobile',['Sessions','Conversions']]
output_chart_mob.plot.barh(stacked=True)

labels = output_chart_mob.index
sizes = output_chart_mob

# colours are taken from http://tools.medialab.sciences-po.fr/iwanthue/
colors = ['#b94663','#6fac5d', '#677ad1']
explode = (0, 0, 0)
plt.xlabel('Visits')
plt.title('Only %s ' % ("%.2f" % (mobile_CR*100) +'% of all mobile visits end up completing a conversion' ))

#need to specify analytics time perdiod
print ("Have %s more conversions per month by optmising mobile UX" % conv_increase)

plt.show()









    



Have 33 more conversions per month by optmising mobile UX



In [ ]:



In [ ]:

	Sessions	Bounce Rate	Conversions	CR
Device
desktop	4061	52.647131	114	0.028072
mobile	2261	62.627156	30	0.013268
tablet	270	55.555556	3	0.011111