Data Science for Marketing Analytics

Data Science for Marketing Analytics

By : Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar

Buy this Book

Data Science for Marketing Analytics

By: Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar

Buy this Book

Overview of this book

Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions.

Data Science for Marketing Analytics

Preface

Free Chapter

Data Preparation and Cleaning

Introduction

Data Models and Structured Data

pandas

Data Manipulation

Summary

Data Exploration and Visualization

Introduction

Identifying the Right Attributes

Generating Targeted Insights

Visualizing Data

Summary

Unsupervised Learning: Customer Segmentation

Introduction

Customer Segmentation Methods

Similarity and Data Standardization

k-means Clustering

Summary

Choosing the Best Segmentation Approach

Introduction

Choosing the Number of Clusters

Different Methods of Clustering

Evaluating Clustering

Summary

Predicting Customer Revenue Using Linear Regression

Introduction

Understanding Regression

Feature Engineering for Regression

Performing and Interpreting Linear Regression

Summary

Other Regression Techniques and Tools for Evaluation

Introduction

Evaluating the Accuracy of a Regression Model

Using Regularization for Feature Selection

Tree-Based Regression Models

Summary

Supervised Learning: Predicting Customer Churn

Introduction

Classification Problems

Understanding Logistic Regression

Creating a Data Science Pipeline

Modeling the Data

Summary

Fine-Tuning Classification Algorithms

Introduction

Support Vector Machines

Decision Trees

Random Forest

Preprocessing Data for Machine Learning Models

Model Evaluation

Performance Metrics

Summary

Modeling Customer Choice

Introduction

Understanding Multiclass Classification

Class Imbalanced Data

Summary

Appendix

Chapter 1: Data Preparation and Cleaning

Chapter 2: Data Exploration and Visualization

Chapter 3: Unsupervised Learning: Customer Segmentation

Chapter 4: Choosing the Best Segmentation Approach

Chapter 5: Predicting Customer Revenue Using Linear Regression

Chapter 6: Other Regression Techniques and Tools for Evaluation

Chapter 7: Supervised Learning: Predicting Customer Churn

Chapter 8: Fine-Tuning Classification Algorithms

Chapter 9: Modeling Customer Choice

Customer Reviews

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Chapter 7: Supervised Learning: Predicting Customer Churn

Activity 13: Performing OSE from OSEMN

Import the necessary libraries.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

Read the dataset using pandas read.csv. and look at the first few rows of the DataFrame:

data= pd.read_csv(r'Telco_Churn_Data.csv')
data.head(5)

Check the length and shape of the data.
```
len(data)
data.shape
```
The length should be 4708 and the shape should be (4708, 15).
Check for any missing values present in the data set and use the info method to check missing values in each of the columns.
```
data.isnull().values.any()
```
This will return True, implying that missing values are present.
```
data.info()
```
This gives the following output:
Figure 7.58: Output of data.info
Rename all the columns in a readable format. Convert all the columns names with a space to _, for example, rename Target Code to Target_Code.
```
data.columns=data.columns.str.replace(' ','_')
data.columns
```
Check the descriptive statistics of the data
```
data.describe()
```
Check the descriptive statistics of Categorical variable
```
data.describe(include='object')
```

Change the data type of Target_Code, Condition_of_Current_Handset, and Current_TechSupComplaints columns from continuous to categorical object type:

data['Target_Code']=data.Target_Code.astype('object')
data['Condition_of_Current_Handset']=data.Condition_of_Current_Handset.astype('object')
data['Current_TechSupComplaints']=data.Current_TechSupComplaints.astype('object')
data['Target_Code']=data.Target_Code.astype('int64')
data.describe(include='object')

This gives the following output:

Figure 7.59: Output of describe function for categorical variables

Check the percentage of missing values and then impute the values of both Complaint_Code and Condition_of_Current_Handset with the most occurring values:

round(data.isnull().sum()/len(data)*100,2)

Figure 7.60: Checking percentage of missing values

data.Complaint_Code.value_counts()

Figure 7.61: Checking missing values in Complaint_Code

data.Condition_of_Current_Handset.value_counts()

Figure 7.62: Checking missing values in Condition_of_Current_Handset

data['Complaint_Code']=data['Complaint_Code'].fillna(value='Billing Problem')
data['Condition_of_Current_Handset']=data['Condition_of_Current_Handset'].fillna(value=1)
data['Condition_of_Current_Handset']=data.Condition_of_Current_Handset.astype('object')

Perform data exploration by initially exploring the customer Target_Churn variable:

data['Target_Churn'].value_counts(0)
data['Target_Churn'].value_counts(1)*100
summary_churn = data.groupby('Target_Churn')
summary_churn.mean()

Find the correlation among different variables:
```
corr = data.corr()
plt.figure(figsize=(15,8))
sns.heatmap(corr, 
            xticklabels=corr.columns.values,
            yticklabels=corr.columns.values,annot=True)
corr
```
From the plots, you will observe that Avg_Calls_Weekdays and Avg_Calls are highly correlated, which makes sense since they represent the same thing—average calls. Current_Bill_Amt seems to be correlated with both variables, which is as expected, since the more you talk the higher your bill will be.

Perform univariate and bivariate analysis.

Here's the univariate analysis:

f, axes = plt.subplots(ncols=3, figsize=(15, 6))
sns.distplot(data.Avg_Calls_Weekdays, kde=True,  color="darkgreen", ax=axes[0]).set_title('Avg_Calls_Weekdays')
axes[0].set_ylabel('No of Customers')
sns.distplot(data.Avg_Calls, kde=True,color="darkblue", ax=axes[1]).set_title('Avg_Calls')
axes[1].set_ylabel('No of Customers')
sns.distplot(data.Current_Bill_Amt, kde=True, color="maroon", ax=axes[2]).set_title('Current_Bill_Amt')
axes[2].set_ylabel('No of Customers')

And here's the bivariate analysis:

Code for the plot of Complaint_Code versus Target_Churn, is given here:

plt.figure(figsize=(17,10))
p=sns.countplot(y="Complaint_Code", hue='Target_Churn', data=data,palette="Set2")
legend = p.get_legend()
legend_txt = legend.texts
legend_txt[0].set_text("No Churn")
legend_txt[1].set_text("Churn")
p.set_title('Customer Complaint Code Distribution')

From this plot, you'll observe that call quality and billing problems are the two main reasons for customer churn.

Cod for the plot of Acct_Plan_Subtype versus Target_Churn is given here:

plt.figure(figsize=(15,4))
p=sns.countplot(y="Acct_Plan_Subtype", hue='Target_Churn', data=data,palette="Set2")
legend = p.get_legend()
legend_txt = legend.texts
legend_txt[0].set_text("No Churn")
legend_txt[1].set_text("Churn")
p.set_title('Customer Acct_Plan_Subtype Distribution')

Code for the plot of Current_TechSupComplaints versus Target_Churn is given here:

plt.figure(figsize=(15,4))
p=sns.countplot(y="Current_TechSupComplaints", hue='Target_Churn', data=data,palette="Set2")
legend = p.get_legend()
legend_txt = legend.texts
legend_txt[0].set_text("No Churn")
legend_txt[1].set_text("Churn")
p.set_title('Customer Current_TechSupComplaints Distribution')

Code for the plot of Avg_Days_Delinquent versus Target_Code is given here.

plt.figure(figsize=(15,4))
ax=sns.kdeplot(data.loc[(data['Target_Code'] == 0),'Avg_Days_Delinquent'] , color=sns.color_palette("Set2")[0],shade=True,label='no churn')
ax=sns.kdeplot(data.loc[(data['Target_Code'] == 1),'Avg_Days_Delinquent'] , color=sns.color_palette("Set2")[1],shade=True, label='churn')
ax.set(xlabel='Average No of Days Deliquent/Defaluted from paying', ylabel='Frequency')
plt.title('Average No of Days Deliquent/Defaluted from paying - churn vs no churn')

From this plot, you'll observe that if the average number of days delinquent is more than 16 days, customers start to churn.

Code for the plot of Account_Age versus Target_Code is given here:

plt.figure(figsize=(15,4))
ax=sns.kdeplot(data.loc[(data['Target_Code'] == 0),'Account_Age'] , color=sns.color_palette("Set2")[0],shade=True,label='no churn')
ax=sns.kdeplot(data.loc[(data['Target_Code'] == 1),'Account_Age'] , color=sns.color_palette("Set2")[1],shade=True, label='churn')
ax.set(xlabel='Account_Age', ylabel='Frequency')
plt.title('Account_Age - churn vs no churn')

From this plot, you'll observe that during the initial 15-20 days of opening an account, the amount of customer churn increases; however, after 20 days, the churn rate declines.

Code for the plot of Percent_Increase_MOM vs Target_Code is given here:

plt.figure(figsize=(15,4))
ax=sns.kdeplot(data.loc[(data['Target_Code'] == 0),'Percent_Increase_MOM'] , color=sns.color_palette("Set2")[0],shade=True,label='no churn')
ax=sns.kdeplot(data.loc[(data['Target_Code'] == 1),'Percent_Increase_MOM'] , color=sns.color_palette("Set2")[1],shade=True, label='churn')
ax.set(xlabel='Percent_Increase_MOM', ylabel='Frequency')
plt.title('Percent_Increase_MOM- churn vs no churn')

From this plot, you will note that customers who have Percent_Increase_MOM within a range of −ve% to +ve% have a greater likelihood of churning.

Activity 14: Performing MN of OSEMN

Import the RandomForestClassifier, train_test_split, and numpy library:

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import numpy as np

Encode the columns:

data["Acct_Plan_Subtype"] = data["Acct_Plan_Subtype"].astype('category').cat.codes
data["Complaint_Code"] = data["Complaint_Code"].astype('ca
tegory').cat.codes
data[["Acct_Plan_Subtype","Complaint_Code"]].head()

Split the data into a training and testing set:

target = 'Target_Code'
X = data.drop(['Target_Code','Target_Churn'], axis=1)
y=data[target]
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.15, random_state=123, stratify=y)

Perform feature selection using the random forest classifier:

forest=RandomForestClassifier(n_estimators=500,random_state=1)
forest.fit(X_train,y_train)
importances=forest.feature_importances_
features = data.drop(['Target_Code','Target_Churn'],axis=1).columns
indices = np.argsort(importances)[::-1]
plt.figure(figsize=(15,4))
plt.title("Feature importances using Random Forest")
plt.bar(range(X_train.shape[1]), importances[indices],
       color="r",  align="center")
plt.xticks(range(X_train.shape[1]), features[indices], rotation='vertical',fontsize=15)
plt.xlim([-1, X_train.shape[1]])
plt.show()

Import statsmodels:

import statsmodels.api as sm
top7_features = ['Avg_Days_Delinquent','Percent_Increase_MOM','Avg_Calls_Weekdays','Current_Bill_Amt','Avg_Calls','Complaint_Code','Account_Age']
logReg = sm.Logit(y_train, X_train[top7_features])
logistic_regression = logReg.fit()

Find out the parameters:

logistic_regression.summary
logistic_regression.params

Create a function to compute the cost function:

coef = logistic_regression.params
def y (coef, Avg_Days_Delinquent,Percent_Increase_MOM,Avg_Calls_Weekdays,Current_Bill_Amt,Avg_Calls,Complaint_Code,Account_Age) : 
    final_coef=coef[0]*Avg_Days_Delinquent+ coef[1]*Percent_Increase_MOM+coef[2]*Avg_Calls_Weekdays+coef[3]*Current_Bill_Amt+ coef[4]*Avg_Calls+coef[5]*Complaint_Code+coef[6]*Account_Age
    return final_coef

Input the given attributes of the customer to the function to obtain the output:

Avg_Days_Delinquent:40, Percent_Increase_MOM:5, Avg_Calls_Weekdays:39000, Current_Bill_Amt:12000, Avg_Calls:9000, Complaint_Code:0, Account_Age:17
y1 = y(coef, 40, 5, 39000,12000,9000,0,17)
p = np.exp(y1) / (1+np.exp(y1))
p

Data Science for Marketing Analytics

By : Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar

Data Science for Marketing Analytics

By: Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar

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