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Showcasing how LTV curves are dependent on Retention Curves
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import pandas as pd
import numpy as np
import random
def build_userbase( n, payer_percentage ):
users = pd.DataFrame( columns=[ "user", "payer", "payment_probability", "payment" ] )
for x in range( 1, n + 1 ):
payer = True if random.randint( 1, 100 ) <= ( payer_percentage * 100 ) else 0
payment_probability = 0
payment = 0
churn_probability = float( random.randint( 1, 10 ) ) / 100
if payer:
payment_probability = float( random.randint( 1, 25 ) ) / 100
payment = float( random.randint( 1, 100 ) )
users = users.append(
{ "user": x, "payer": payer,
"payment_probability": payment_probability, "payment": payment,
"churn_probability": churn_probability, "churned": 0 }, ignore_index=True )
return users
def build_cumulative_revenue( users, days ):
payers = users[ users[ 'payer' ] == 1 ]
daily_revenue = [ 0 ] * ( days + 1 )
daily_cumulative_revenue = [ 0 ] * ( days + 1 )
for x in range( 1, days + 1 ):
daily_revenue[ x ] = 0
daily_cumulative_revenue[ x ] = 0
this_daily_revenue = 0
for index, p in payers.iterrows():
this_payment_probability = float( random.randint( 1, 100 ) ) / 100
this_payment = p[ "payment" ] if this_payment_probability <= p[ "payment_probability" ] else 0
this_daily_revenue += this_payment
daily_revenue[ x ] = this_daily_revenue
daily_cumulative_revenue[ x ] = ( daily_cumulative_revenue[ x - 1 ] + daily_revenue[ x ] ) if x > 1 else daily_revenue[ x ]
return daily_revenue, daily_cumulative_revenue
def build_cumulative_revenue_with_churn( users, days ):
payers = users[ users[ 'payer' ] == 1 ]
daily_revenue = [ 0 ] * ( days + 1 )
daily_cumulative_revenue = [ 0 ] * ( days + 1 )
for x in range( 1, days + 1 ):
daily_revenue[ x ] = 0
daily_cumulative_revenue[ x ] = 0
this_daily_revenue = 0
for index, p in payers.iterrows():
if( not p[ "churned" ] ):
#if they didn't churn out
this_churn_probability = float( random.randint( 1, 100 ) ) / 100
if this_churn_probability > p[ "churn_probability" ]:
#if this isn't their day to churn
this_payment_probability = float( random.randint( 1, 100 ) ) / 100
this_payment = p[ "payment" ] if this_payment_probability <= p[ "payment_probability" ] else 0
this_daily_revenue += this_payment
else:
#they are churning
payers.loc[ index, "churned" ] = True
daily_revenue[ x ] = this_daily_revenue
daily_cumulative_revenue[ x ] = ( daily_cumulative_revenue[ x - 1 ] + daily_revenue[ x ] ) if x > 1 else daily_revenue[ x ]
users.loc[ users[ "payer" ] == True ] = payers
return daily_revenue, daily_cumulative_revenue
def build_DAU_with_churn( users, days ):
DAU = [ 0 ] * ( days + 1 )
churn = [ 0 ] * ( days + 1 )
for x in range( 1, days + 1 ):
for index, u in users.iterrows():
if( not u[ "churned" ] ):
#if the user has not yet churned
this_churn_probability = float( random.randint( 1, 100 ) ) / 100
if this_churn_probability > u[ "churn_probability" ]:
#if this user is not churning on this day
#increment the DAU
DAU[ x ] += 1
else:
churn[ x ] += 1
users.loc[ index, "churned" ] = True
return DAU, churn
#
# Build initial userbase
#
users = build_userbase( 1000, payer_percentage=0.05 )
users[ "churned" ] = users[ "churned" ].astype('bool')
#
# Get daily revenue values
#
dr_users = users
daily_revenue, daily_cumulative_revenue = build_cumulative_revenue( dr_users, 365 )
#
# Get daily revenue values with churn
#
drc_users = users
daily_revenue_with_churn, daily_cumulative_revenue_with_churn = build_cumulative_revenue_with_churn( drc_users, 365 )
#
# Get DAU and Churn
#
dau_users = users
DAU, churn = build_DAU_with_churn( dau_users, 365 )
#
# Print Revenue Graph
#
fig, ax = plt.subplots()
plt.rcParams['figure.figsize'] = [10, 5]
plt.plot( daily_cumulative_revenue, '-g', label='Cumulative Revenue', linewidth=3 )
plt.plot( daily_revenue, '-r', label='Daily Revenue', linewidth=3 )
plt.legend(loc='upper left')
plt.ylabel( 'Revenue' )
fmt = '${x:,.0f}'
tick = ticker.StrMethodFormatter( fmt )
ax.yaxis.set_major_formatter( tick )
plt.xticks( rotation=25 )
fig.suptitle( 'Cumulative and Daily Revenue, No Churn', fontsize=14 )
plt.show()
#
# Print Revenue with Churn Graph
#
fig, ax = plt.subplots()
plt.rcParams['figure.figsize'] = [10, 5]
plt.plot( daily_cumulative_revenue_with_churn, '-g', label='Cumulative Revenue (with Churn)', linewidth=3 )
plt.plot( daily_revenue_with_churn, '-r', label='Daily Revenue (with Churn)', linewidth=3 )
plt.legend(loc='center right')
plt.ylabel( 'Revenue' )
fmt = '${x:,.0f}'
tick = ticker.StrMethodFormatter( fmt )
ax.yaxis.set_major_formatter( tick )
plt.xticks( rotation=25 )
fig.suptitle( 'Cumulative and Daily Revenue with Churn', fontsize=14 )
plt.show()
#
# Print DAU and Churn Graph
#
fig, ax1 = plt.subplots()
plt.rcParams['figure.figsize'] = [10, 5]
ax1.plot( DAU, '-r', label='Cohort DAU', linewidth=3 )
ax1.set_ylabel( 'DAU' )
ax1.plot( churn , '-y', label='Daily Cohort Churn', linewidth=3 )
ax1.legend( loc='upper right' )
fig.suptitle( 'DAU and Daily Churn Values', fontsize=14 )
plt.show()
x = np.arange( 1, 365, 1 )
#
# Print a random LTV graph
#
ltv = np.log( x )
fig, ax1 = plt.subplots()
plt.rcParams['figure.figsize'] = [10, 5]
ax1.set_ylabel( 'LTV' )
ax1.plot( ltv, '-g', label='LTV Curve (Arbitrary)', linewidth=3 )
ax1.legend( loc='center right' )
fig.suptitle( 'An Arbitrary LTV Curve', fontsize=14 )
fmt = '${x:,.0f}'
tick = ticker.StrMethodFormatter( fmt )
ax1.yaxis.set_major_formatter( tick )
plt.show()
#
# Print a random retention graph
#
retention = np.exp( -.05 * x )
fig, ax1 = plt.subplots()
plt.rcParams[ 'figure.figsize' ] = [ 10, 5 ]
ax1.set_ylabel( 'Retention' )
ax1.plot( retention, '-r', label='Retention Curve (Arbitrary)', linewidth=3 )
ax1.legend( loc='center right' )
fig.suptitle( 'An Arbitrary Retention Curve', fontsize=14 )
vals = ax1.get_yticks()
ax1.set_yticklabels(['%1.2f%%' %i for i in vals])
plt.show()
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