Data Science for Marketing Analytics

Key Features

• Study new techniques for marketing analytics
• Explore uses of machine learning to power your marketing analyses
• Work through each stage of data analytics with the help of multiple examples and exercises

Book Description

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.

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

Who this book is for

Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.

《市場分析的數據科學》一書的主要特點包括：

- 學習市場分析的新技術
- 探索使用機器學習來提升市場分析能力
- 通過多個實例和練習來進行數據分析的每個階段

該書涵蓋了數據分析的每個階段，從處理原始數據集到對人群進行分割，並基於這些分割來建模不同部分的人群。

該書首先教你如何使用Python庫，如pandas和Matplotlib，從Python中讀取數據，對其進行操作並創建圖表，使用分類和連續變量。然後，你將學習如何將人群分割成不同的組，並使用不同的聚類技術來評估客戶分割。隨著閱讀的進展，你將探索評估和選擇最佳分割方法的方式，並繼續在客戶價值數據上創建線性回歸模型來預測終身價值。在最後幾章中，你將了解回歸技術和評估回歸模型的工具，並探索使用分類算法來預測客戶選擇的方式。最後，你將應用這些技術來創建一個用於建模客戶產品選擇的流失模型。

通過閱讀本書，你將能夠建立自己的市場報告和互動儀表板解決方案。

本書的學習重點包括：

- 使用pandas和Matplotlib在Python中進行數據分析和可視化
- 學習聚類技術，如層次聚類和k-means聚類
- 基於操作數據創建客戶分割
- 使用線性回歸預測客戶終身價值
- 使用分類算法了解客戶選擇
- 優化分類算法以提取最大信息量

本書適合開發人員和市場分析師，他們希望在市場分析工作中使用新的、更複雜的工具。如果你具有Python編程和高中數學的編程經驗，以及一些關於數據庫、Excel、統計學或Tableau的經驗，那麼這本書將對你有所幫助，但這些經驗並非必需。

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 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.

Pranshu Bhatnagar works as a data scientist in the telematics, insurance, and mobile software space. He has previously worked as a quantitative analyst in the FinTech industry and often writes about algorithms, time series analysis in Python, and similar topics. He graduated with honors from the Chennai Mathematical Institute with a degree in Mathematics and Computer Science and has completed certification courses in Machine Learning and Artificial Intelligence from the International Institute of Information Technology, Hyderabad. He is based in Bangalore, India.

Tommy Blanchard在羅切斯特大學獲得博士學位，並在哈佛大學進行博士後培訓。現在，他在Fresenius Medical Care North America領導數據科學團隊。他的團隊進行高級分析並創建預測模型，以解決公司內各種問題。

Debasish Behera在一家大型日本企業銀行擔任數據科學家，他應用機器學習/人工智能來解決複雜問題。他曾參與多個使用案例，包括AML、預測分析、客戶分割、聊天機器人和自然語言處理。他目前居住在新加坡，擁有新加坡管理大學的商業分析碩士學位（MITB）。

Pranshu Bhatnagar在遙感技術、保險和移動軟件領域擔任數據科學家。他曾在金融科技行業擔任量化分析師，並經常撰寫有關Python中的算法、時間序列分析等主題的文章。他以數學和計算機科學學位榮譽畢業於金奈數學研究所，並完成了國際資訊技術學院（Hyderabad）的機器學習和人工智能認證課程。他目前居住在印度班加羅爾。

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

- 數據準備和清理
- 數據探索和可視化
- 非監督學習：客戶分群
- 選擇最佳分群方法
- 使用線性回歸預測客戶收入
- 其他回歸技術和評估工具
- 監督學習：預測客戶流失
- 微調分類算法
- 建模客戶選擇