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Marketing Analytics Using Machine Learning Techniques

Gain insights into applied machine learning for marketing analytics. Explore data science techniques, create predictive models with Python libraries, and drive results with data-driven decisions.

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Overview

Content

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In this course, you’ll learn applied machine learning in marketing analytics and cover modern data science techniques such as data exploration, data preprocessing, feature engineering and evaluation. You’ll gain hands-on experience with the Python libraries pandas, Scikit-learn, and seaborn, and learn how to use them to perform data wrangling, data analysis, create predictive models, and visualize your results. This course will introduce you to basic data manipulation techniques. Further, you’ll cover specific topics such as customer revenue prediction using Linear Regression, customer segmentation using the K-Means Algorithm, customer churn prediction using Logistic Regression, and Customer Lifetime Value (CLV) analysis and prediction. Whether you're a marketer or business professional, this course will give you the skills to make data-driven decisions and drive results. You’ll have hands-on experience predicting future revenue, segmenting your customers into different groups, and predicting customer churn

In this course, you’ll learn applied machine learning in marketing analytics and cover modern data science techniques such as da...Show More

WHAT YOU'LL LEARN

A working knowledge of using Python libraries such as pandas, scikit-learn, and seaborn for data analysis, visualization, and building machine learning models

An understanding of marketing analytics concepts and applying them in Python

The ability to create and interpret linear regression models for customer revenue prediction

A working knowledge of the K-Means Algorithm and its applications in customer segmentation

An understanding of logistic regression and its use for customer churn prediction

Proficiency in customer lifetime value (CLV) analysis and prediction

A working knowledge of making data-driven decisions and optimizing marketing strategies

A working knowledge of using Python libraries such as pandas, scikit-learn, and seaborn for data analysis, visualization, and building machine learning models

TAKEAWAY SKILLS

Python

Data Manipulation

Content

38 Lessons4 Quizzes5 Challenges

Introduction

1 Lessons

Get familiar with essential machine learning skills for marketing analytics, including data exploration, model building, and evaluation.

Getting Started

Data Manipulation

3 Lessons

Get started with data exploration, wrangling, and modeling using pandas for marketing analytics.

Data Exploration

Data Wrangling

Data Modeling

Predicting Customer Revenue

8 Lessons

Examine predicting customer revenue using linear regression, dataset exploration, feature engineering, model building, and evaluation.

Introduction to Linear Regression

Explore the Dataset

Feature Engineering

Relationships Between Dependent and Independent Variables

Model Building and Evaluation

Challenge: Revenue Prediction

Solution: Revenue Prediction

Quiz: Revenue Prediction

Customer Segmentation

9 Lessons

Grasp the fundamentals of customer segmentation via clustering techniques, feature engineering, and PCA.

Introduction to Customer Segmentation

Explore the Dataset

Feature Engineering

Customer Segmentation

Interpreting the Segmentation Result

Dimensionality Reduction with PCA

Challenge: Customer Segmentation

Solution: Customer Segmentation

Quiz: Customer Segmentation

Predicting Customer Churn

9 Lessons

Map out the steps for predicting customer churn using data analysis and machine learning.

Introduction to Customer Churn

Explore the Dataset

Feature Engineering: One-Hot Encoding

Feature Engineering: Remove Redundant Features

Churn Prediction with Logistic Regression

Churn Prediction with Decision Tree

Challenge: Customer Churn

Solution: Customer Churn

Quiz: Customer Churn

Predicting Customer Lifetime Value (CLV)

7 Lessons

Follow the process of analyzing and predicting Customer Lifetime Value using data and machine learning.

Introduction to Customer Lifetime Value

Data Exploration: Monthly Revenue Analysis

Data Exploration: Customer Revenue Analysis

Predict Quarterly Sales with the RFM Model

Challenge: Customer Lifetime Value

Solution: Customer Lifetime Value

Quiz: Customer Lifetime Value

Conclusion

1 Lessons

Learn how to improve marketing strategies using machine learning for predictions and segmentation.

Course Summary

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Asish Biswas

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