Data Science for Marketing Analytics: Achieve your marketing goals with the data analytics power of Python
Authors: Tommy Blanchard – Debasish Behera – Pranshu Bhatnagar
ISBN-10: 1789959411
ISBN-13: 9781789959413
Publication Date 出版日期: 2019-03-30
Print Length 页数: 420 pages
Publisher finelybook 出版社: Packt
Book Description
By finelybook
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.
Contents
1: DATA PREPARATION AND CLEANING
2: DATA EXPLORATION AND VISUALIZATION
3: UNSUPERVISED LEARNING: CUSTOMER SEGMENTATION
4: CHOOSING THE BEST SEGMENTATION APPROACH
5: PREDICTING CUSTOMER REVENUE USING LINEAR REGRESSION
6: OTHER REGRESSION TECHNIQUES AND TOOLS FOR EVALUATION
7: SUPERVISED LEARNING: PREDICTING CUSTOMER CHURN
8: FINE-TUNING CLASSIFICATION ALGORITHMS
9: MODELING CUSTOMER CHOICE
What You Will Learn
Analyze and visualize data in Python using pandas and Matplotlib
Study clustering techniques,such as hierarchical and k-means clustering
Create customer segments based on manipulated data
Predict customer lifetime value using linear regression
Use classification algorithms to understand customer choice
Optimize classification algorithms to extract maximal information
Authors
Tommy Blanchard
Tommy Blanchard earned his PhD from the University of Rochester and did his postdoctoral training at Harvard. Now,he leads the data science team at Fresenius Medical Care North America. His team performs advanced analytics and creates predictive models to solve a wide variety of problems across the company.
Debasish Behera
Debasish Behera works as a data scientist for a large Japanese corporate bank,where he applies machine learning/AI to solve complex problems. He has worked on multiple use cases involving AML,predictive analytics,customer segmentation,chat bots,and natural language processing. He currently lives in Singapore and holds a Master’s in Business Analytics (MITB) from the Singapore Management University.
