Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (Hardcover)

Now , a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications.

Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis.

Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes:

• The role of analytics in delivering effective messages on the web
• Understanding the web by understanding its hidden structures
• Being recognized on the web – and watching your own competitors
• Visualizing networks and understanding communities within them
• Measuring sentiment and making recommendations
• Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics

Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R.

Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.

現在，西北大學著名分析學程的領導者Thomas W. Miller提出了一個完全整合的預測分析市場應用的商業和學術元素的教材。Miller針對管理者和學生寫作，解釋了在實際應用中的基本概念、原則和理論。

在Miller的先驅計劃的基礎上，《市場數據科學》全面涵蓋了分割、目標市場、品牌和產品定位、新產品開發、選擇建模、推薦系統、價格研究、零售選址、需求估計、銷售預測、客戶保留和生命價值分析等領域。

在Miller廣受好評的《預測分析建模技術》之後，他整合了以前在網絡分析、網絡科學、信息技術和編程等教材中分散的重要信息和見解。內容包括：

- 在網絡上傳遞有效信息的分析角色
- 通過了解隱藏的結構來理解網絡
- 在網絡上被認可 - 並觀察自己的競爭對手
- 可視化網絡並理解其中的社區
- 測量情感並提出建議
- 利用關鍵數據科學方法：數據庫/數據準備、經典/貝葉斯統計、回歸/分類、機器學習和文本分析

六個完整的案例研究涉及到非常相關的問題，例如：區分合法的電子郵件和垃圾郵件；為訴訟發現識別具有法律相關性的信息；從匿名網絡瀏覽數據中獲取見解等。本教材的大量網絡和網絡問題來自豐富的公共領域數據源，其中許多附帶有Python和/或R的解決方案。

《市場數據科學》將成為所有學生、教師和專業營銷人員的寶貴資源，他們希望利用商業分析來提高營銷業績。