Data Science for Marketing Analytics
暫譯: 行銷分析的數據科學

Tommy Blanchard , Debasish Behera , Pranshu Bhatnagar

Data Science for Marketing Analytics

買這商品的人也買了...

相關主題

商品描述

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.

商品描述(中文翻譯)

**主要特點**

- 學習行銷分析的新技術
- 探索機器學習在行銷分析中的應用
- 通過多個範例和練習，逐步了解數據分析的每個階段

**書籍描述**

《行銷分析的數據科學》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群，並根據這些分群對人群的不同部分進行建模。

本書首先教你如何使用 Python 函式庫，如 pandas 和 Matplotlib，從 Python 讀取數據、操作數據並創建圖表，使用類別變數和連續變數。接著，你將學習如何將人群分成不同的群組，並使用不同的聚類技術來評估客戶分群。隨著章節的深入，你將探索評估和選擇最佳分群方法的方式，並創建一個基於客戶價值數據的線性回歸模型來預測客戶的終身價值。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法。最後，你將應用這些技術來創建一個流失模型，以模擬客戶的產品選擇。

在本書結束時，你將能夠建立自己的行銷報告和互動儀表板解決方案。

**你將學到的內容**

- 使用 pandas 和 Matplotlib 在 Python 中分析和可視化數據
- 學習聚類技術，如層次聚類和 k-means 聚類
- 根據操作過的數據創建客戶分群
- 使用線性回歸預測客戶的終身價值
- 使用分類算法理解客戶選擇
- 優化分類算法以提取最大信息

**本書適合誰**

《行銷分析的數據科學》是為尋求在行銷分析中使用新型、更複雜工具的開發人員和行銷分析師而設計的。如果你有 Python 編碼的經驗和高中數學的知識，將會對你有所幫助。對數據庫、Excel、統計或 Tableau 有一些經驗是有用的，但不是必需的。

作者簡介

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.

作者簡介(中文翻譯)

湯米·布蘭查德於羅徹斯特大學獲得博士學位，並在哈佛大學進行博士後訓練。目前，他領導著Fresenius Medical Care North America的數據科學團隊。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。

德巴西什·貝赫拉在一家大型日本企業銀行擔任數據科學家，應用機器學習/人工智慧來解決複雜問題。他曾參與多個用例，包括反洗錢（AML）、預測分析、客戶細分、聊天機器人和自然語言處理。他目前居住在新加坡，並持有新加坡管理大學的商業分析碩士學位（MITB）。

普蘭舒·巴特納加在遠程信息處理、保險和移動軟體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，並經常撰寫有關算法、Python中的時間序列分析及類似主題的文章。他以優異的成績畢業於金奈數學研究所，獲得數學和計算機科學學位，並完成了海得拉巴國際信息技術學院的機器學習和人工智慧認證課程。他目前居住在印度班加羅爾。

目錄大綱

  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

目錄大綱(中文翻譯)


  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

類似商品