Churn rate, when applied to a customer base, refers to the proportion of contractual customers or subscribers who leave a supplier during a given time period. It is a possible indicator of customer dissatisfaction, cheaper and/or better offers from the competition, more successful sales and/or marketing by the competition, or reasons having to do with the customer life cycle.
Churn is closely related to the concept of average customer life time. For example, an annual churn rate of 25 percent implies an average customer life of four years. An annual churn rate of 33 percent implies an average customer life of three years. The churn rate can be minimized by creating barriers which discourage customers to change suppliers (contractual binding periods, use of proprietary technology, value-added services, unique business models, etc.), or through retention activities such as loyalty programs. It is possible to overstate the churn rate, as when a consumer drops the service but then restarts it within the same year. Thus, a clear distinction needs to be made between "gross churn", the total number of absolute disconnections, and "net churn", the overall loss of subscribers or members. The difference between the two measures is the number of new subscribers or members that have joined during the same period. Suppliers may find that if they offer a loss-leader "introductory special", it can lead to a higher churn rate and subscriber abuse, as some subscribers will sign on, let the service lapse, then sign on again to take continuous advantage of current specials. https://en.wikipedia.org/wiki/Churn_rate
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%%capture
import numpy as np
import pandas as pd
import h2o
from h2o.automl import H2OAutoML
from __future__ import print_function
import pandas_profiling
# Suppress unwatned warnings
import warnings
warnings.filterwarnings('ignore')
import logging
logging.getLogger("requests").setLevel(logging.WARNING)
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# Load our favorite visualization library
import os
import plotly
import plotly.plotly as py
import plotly.figure_factory as ff
import plotly.graph_objs as go
import cufflinks as cf
plotly.offline.init_notebook_mode(connected=True)
# Sign into Plotly with masked, encrypted API key
myPlotlyKey = os.environ['SECRET_ENV_BRETTS_PLOTLY_KEY']
py.sign_in(username='bretto777',api_key=myPlotlyKey)
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accessKey = os.environ['BRETT_AWS_ACCESS_KEY']
s3file = 'https://trifactapro.s3.amazonaws.com/churn.csv?AWSAccessKeyId=' + accessKey + '&Expires=1522155539&Signature=Imj9nbjqsdarjbAGKkjeHB9PwWE%3D'
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# Load some data
churnDF = pd.read_csv(s3file, delimiter=',')
churnDF["Churn"] = churnDF["Churn"].replace(to_replace=False, value='Retain')
churnDF["Churn"] = churnDF["Churn"].replace(to_replace=True, value='Churn')
churnDFs = churnDF.sample(frac=0.07) # Sample for speedy viz
churnDF.head(5)
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In [10]:
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pandas_profiling.ProfileReport(churnDF)
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# separate the calls data for plotting
churnDFs = churnDFs[['Account Length','Day Calls','Eve Calls','CustServ Calls','Churn']]
# Create scatter plot matrix of call data
splom = ff.create_scatterplotmatrix(churnDFs, diag='histogram', index='Churn',
colormap= dict(
Churn = '#9CBEF1',
Retain = '#04367F'
),
colormap_type='cat',
height=560, width=650,
size=4, marker=dict(symbol='circle'))
py.iplot(splom)
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In [13]:
%%capture
#h2o.connect(ip="35.225.239.147")
h2o.init(nthreads=1, max_mem_size="768m")
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# Split data into training and testing frames
from sklearn import cross_validation
from sklearn.model_selection import train_test_split
training, testing = train_test_split(churnDF, train_size=0.8, stratify=churnDF["Churn"], random_state=9)
train = h2o.H2OFrame(python_obj=training).drop("State")
test = h2o.H2OFrame(python_obj=testing).drop("State")
# Set predictor and response variables
y = "Churn"
x = train.columns
x.remove(y)
The Automatic Machine Learning (AutoML) function automates the supervised machine learning model training process. The current version of AutoML trains and cross-validates a Random Forest, an Extremely-Randomized Forest, a random grid of Gradient Boosting Machines (GBMs), a random grid of Deep Neural Nets, and a Stacked Ensemble of all the models.
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# Run AutoML until 11 models are built
autoModel = H2OAutoML(max_models = 20)
autoModel.train(x = x, y = y,
training_frame = train,
validation_frame = test,
leaderboard_frame = test)
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leaders = autoModel.leaderboard
leaders
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importances = h2o.get_model(leaders[2, 0]).varimp(use_pandas=True)
importances = importances.loc[:,['variable','relative_importance']].groupby('variable').mean()
importances.sort_values(by="relative_importance", ascending=False).iplot(kind='bar', colors='#5AC4F2', theme='white')
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In [18]:
import matplotlib.pyplot as plt
plt.figure()
bestModel = h2o.get_model(leaders[2, 0])
plt = bestModel.partial_plot(data=test, cols=["Day Mins","CustServ Calls","Day Charge"])
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Model0 = np.array(h2o.get_model(leaders[0,0]).roc(xval=True))
Model1 = np.array(h2o.get_model(leaders[1,0]).roc(xval=True))
Model2 = np.array(h2o.get_model(leaders[2,0]).roc(xval=True))
Model3 = np.array(h2o.get_model(leaders[3,0]).roc(xval=True))
Model4 = np.array(h2o.get_model(leaders[4,0]).roc(xval=True))
Model5 = np.array(h2o.get_model(leaders[5,0]).roc(xval=True))
Model6 = np.array(h2o.get_model(leaders[6,0]).roc(xval=True))
Model7 = np.array(h2o.get_model(leaders[7,0]).roc(xval=True))
Model8 = np.array(h2o.get_model(leaders[8,0]).roc(xval=True))
Model9 = np.array(h2o.get_model(leaders[9,0]).roc(xval=True))
layout = go.Layout(autosize=False, width=725, height=575, xaxis=dict(title='False Positive Rate', titlefont=dict(family='Arial, sans-serif', size=15, color='grey')),
yaxis=dict(title='True Positive Rate', titlefont=dict(family='Arial, sans-serif', size=15, color='grey')))
traceChanceLine = go.Scatter(x = [0,1], y = [0,1], mode = 'lines+markers', name = 'chance', line = dict(color = ('rgb(136, 140, 150)'), width = 4, dash = 'dash'))
Model0Trace = go.Scatter(x = Model0[0], y = Model0[1], mode = 'lines', name = 'Model 0', line = dict(color = ('rgb(26, 58, 126)'), width = 3))
Model1Trace = go.Scatter(x = Model1[0], y = Model1[1], mode = 'lines', name = 'Model 1', line = dict(color = ('rgb(156, 190, 241))'), width = 1))
Model2Trace = go.Scatter(x = Model2[0], y = Model2[1], mode = 'lines', name = 'Model 2', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
Model3Trace = go.Scatter(x = Model3[0], y = Model3[1], mode = 'lines', name = 'Model 3', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
Model4Trace = go.Scatter(x = Model4[0], y = Model4[1], mode = 'lines', name = 'Model 4', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
Model5Trace = go.Scatter(x = Model5[0], y = Model5[1], mode = 'lines', name = 'Model 5', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
Model6Trace = go.Scatter(x = Model6[0], y = Model6[1], mode = 'lines', name = 'Model 6', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
Model7Trace = go.Scatter(x = Model7[0], y = Model7[1], mode = 'lines', name = 'Model 7', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
Model8Trace = go.Scatter(x = Model8[0], y = Model8[1], mode = 'lines', name = 'Model 8', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
Model9Trace = go.Scatter(x = Model9[0], y = Model9[1], mode = 'lines', name = 'Model 9', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
fig = go.Figure(data=[Model0Trace,Model1Trace,Model2Trace,Model3Trace,Model4Trace,Model5Trace,Model6Trace,Model8Trace,Model9Trace,traceChanceLine], layout=layout)
py.iplot(fig)
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In [20]:
cm = h2o.get_model(leaders[1, 0]).confusion_matrix(xval=True)
cm = cm.table.as_data_frame()
cm
confusionMatrix = ff.create_table(cm)
confusionMatrix.layout.height=300
confusionMatrix.layout.width=800
confusionMatrix.layout.font.size=17
py.iplot(confusionMatrix)
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In [21]:
CorrectPredictChurn = cm.loc[0,'Churn']
CorrectPredictChurnImpact = 75
cm1 = CorrectPredictChurn*CorrectPredictChurnImpact
IncorrectPredictChurn = cm.loc[1,'Churn']
IncorrectPredictChurnImpact = -5
cm2 = IncorrectPredictChurn*IncorrectPredictChurnImpact
IncorrectPredictRetain = cm.loc[0,'Retain']
IncorrectPredictRetainImpact = -150
cm3 = IncorrectPredictRetain*IncorrectPredictRetainImpact
CorrectPredictRetain = cm.loc[0,'Retain']
CorrectPredictRetainImpact = 5
cm4 = IncorrectPredictRetain*CorrectPredictRetainImpact
data_matrix = [['Business Impact', '($) Predicted Churn', '($) Predicted Retain', '($) Total'],
['($) Actual Churn', cm1, cm3, '' ],
['($) Actual Retain', cm2, cm4, ''],
['($) Total', cm1+cm2, cm3+cm4, cm1+cm2+cm3+cm4]]
impactMatrix = ff.create_table(data_matrix, height_constant=20, hoverinfo='weight')
impactMatrix.layout.height=300
impactMatrix.layout.width=800
impactMatrix.layout.font.size=17
py.iplot(impactMatrix)
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print("Total customers evaluated: 2132")
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print("Total value created by the model: $" + str(cm1+cm2+cm3+cm4))
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print("Total value per customer: $" +str(round(((cm1+cm2+cm3+cm4)/2132),3)))
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%%capture
# Save the best model
path = h2o.save_model(model=h2o.get_model(leaders[0, 0]), force=True)
os.rename(h2o.get_model(leaders[0, 0]).model_id, "AutoML-leader")
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LoadedEnsemble = h2o.load_model(path="AutoML-leader")
print(LoadedEnsemble)