Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations - Hardcover

Katsov, Ilya

Hardcover

ISBN 10: 0692989048 ISBN 13: 9780692989043

Verlag: Grid Dynamics, 2017

Inhaltsangabe

Introduction to Algorithmic Marketing is a comprehensive guide to advanced marketing automation for marketing strategists, data scientists, product managers, and software engineers. It summarizes various techniques tested  by major technology, advertising, and retail companies, and it glues these methods together with economic theory and machine learning. The book covers the main areas of marketing that require programmatic micro-decisioning – targeted promotions and advertisements, eCommerce search, recommendations, pricing, and assortment optimization.

"A comprehensive and indispensable reference for anyone undertaking the transformational journey towards algorithmic marketing."

―Ali Bouhouch, CTO, Sephora Americas

"It is a must-read for both data scientists and marketing officers―even better if they read it together."

―Andrey Sebrant, Director of Strategic Marketing, Yandex

"The book gives the executives, middle managers, and data scientists in your organization a set of concrete, actionable, and incremental recommendations on how to build better insights and decisions, starting today, one step at a time."

―Victoria Livschitz, founder and CTO, Grid Dynamics

 

Table of Contents

Chapter 1 - Introduction

1. The Subject of Algorithmic Marketing
2. The Definition of Algorithmic Marketing
3. Historical Backgrounds and Context
4. Programmatic Services
5. Who Should Read This Book?
6. Summary

Chapter 2 - Review of Predictive Modeling

1. Descriptive, Predictive, and Prescriptive Analytics
2. Economic Optimization
3. Machine Learning
4. Supervised Learning
5. Representation Learning
6. More Specialized Models
7. Summary

Chapter 3 - Promotions and Advertisements

1. Environment
2. Business Objectives
3. Targeting Pipeline
4. Response Modeling and Measurement
5. Building Blocks: Targeting and LTV Models
6. Designing and Running Campaigns
7. Resource Allocation
8. Online Advertisements
9. Measuring the Effectiveness
10. Architecture of Targeting Systems
11. Summary

Chapter 4 - Search

1. Environment
2. Business Objectives
3. Building Blocks: Matching and Ranking
4. Mixing Relevance Signals
5. Semantic Analysis
6. Search Methods for Merchandising
7. Relevance Tuning
8. Architecture of Merchandising Search Services
9. Summary

Chapter 5 - Recommendations

1. Environment
2. Business Objectives
3. Quality Evaluation
4. Overview of Recommendation Methods
5. Content-based Filtering
6. Introduction to Collaborative Filtering
7. Neighborhood-based Collaborative Filtering
8. Model-based Collaborative Filtering
9. Hybrid Methods
10. Contextual Recommendations
11. Non-Personalized Recommendations
12. Multiple Objective Optimization
13. Architecture of Recommender Systems
14. Summary

Chapter 6 - Pricing and Assortment

1. Environment
2. The Impact of Pricing
3. Price and Value
4. Price and Demand
5. Basic Price Structures
6. Demand Prediction
7. Price Optimization
8. Resource Allocation
9. Assortment Optimization
10. Architecture of Price Management Systems
11. Summary

 

Über diesen Titel

Críticas

"At a time when power is shifting to consumers, while brands and retailers are grasping for fleeting moments of attention, everyone is competing on data and the ability to leverage it at scale to target, acquire, and retain customers. This book is a manual for doing just that. Both marketing practitioners and technology providers will find this book very useful in guiding them through the marketing value chain and how to fully digitize it. A comprehensive and indispensable reference for anyone undertaking the transformational journey towards algorithmic marketing."

―Ali Bouhouch, CTO, Sephora Americas

"This book is a live portrait of digital transformation in marketing. It shows how data science becomes an essential part of every marketing activity. The book details how data-driven approaches and smart algorithms result in deep automation of traditionally labor-intensive marketing tasks. Decision-making is getting not only better but much faster, and this is crucial in our ever-accelerating competitive environment. It is a must-read for both data scientists and marketing officers―even better if they read it together."

―Andrey Sebrant, Director of Strategic Marketing, Yandex

"This books delivers a complete end-to-end blueprint on how to fully digitize your company's marketing operations. Starting from a conceptual architecture for the future of digital marketing, it then delves into detailed analysis of best practices in each individual area of marketing operations. The book gives the executives, middle managers, and data scientists in your organization a set of concrete, actionable, and incremental recommendations on how to build better insights and decisions, starting today, one step at a time."

―Victoria Livschitz, founder and CTO, Grid Dynamics

"This book provides a much-needed collection of recipes for marketing practitioners on how to use advanced methods of machine learning and data science to understand customer behavior, personalize product offerings, optimize the incentives, and control the engagement - thus creating a new generation of data-driven analytic platform for marketing systems."

―Kira Makagon, Chief Innovation Officer, RingCentral; serial entrepreneur, founder of RedAril and Octane

"While virtually every business manager today grasps the conceptual importance of data analytics and machine learning, the challenge of implementing actual competitive solutions rooted in data science remains quite daunting. The scarcity of data scientist talent, combined with the difficulty of adapting academic models, generic open-source software and algorithms to industry-specific contexts are among the difficulties confronting digital marketers around the world. This book by Ilya Katsov draws from the deep domain expertise he developed at Grid Dynamics in delivering innovative, yet practical digital marketing solutions to large organizations and helping them successfully compete, remain relevant, and adapt in the new age of data analytics."

―Eric Benhamou, Founder and General Partner, Benhamou Global Ventures; former CEO and Chairman of 3Com and Palm

Reseña del editor

Introduction to Algorithmic Marketing is a comprehensive guide to advanced marketing automation for marketing strategists, data scientists, product managers, and software engineers. It summarizes various techniques tested  by major technology, advertising, and retail companies, and it glues these methods together with economic theory and machine learning. The book covers the main areas of marketing that require programmatic micro-decisioning – targeted promotions and advertisements, eCommerce search, recommendations, pricing, and assortment optimization.

"A comprehensive and indispensable reference for anyone undertaking the transformational journey towards algorithmic marketing."

―Ali Bouhouch, CTO, Sephora Americas

"It is a must-read for both data scientists and marketing officers―even better if they read it together."

―Andrey Sebrant, Director of Strategic Marketing, Yandex

"The book gives the executives, middle managers, and data scientists in your organization a set of concrete, actionable, and incremental recommendations on how to build better insights and decisions, starting today, one step at a time."

―Victoria Livschitz, founder and CTO, Grid Dynamics

 

Table of Contents

Chapter 1 - Introduction

1. The Subject of Algorithmic Marketing
2. The Definition of Algorithmic Marketing
3. Historical Backgrounds and Context
4. Programmatic Services
5. Who Should Read This Book?
6. Summary

Chapter 2 - Review of Predictive Modeling

1. Descriptive, Predictive, and Prescriptive Analytics
2. Economic Optimization
3. Machine Learning
4. Supervised Learning
5. Representation Learning
6. More Specialized Models
7. Summary

Chapter 3 - Promotions and Advertisements

1. Environment
2. Business Objectives
3. Targeting Pipeline
4. Response Modeling and Measurement
5. Building Blocks: Targeting and LTV Models
6. Designing and Running Campaigns
7. Resource Allocation
8. Online Advertisements
9. Measuring the Effectiveness
10. Architecture of Targeting Systems
11. Summary

Chapter 4 - Search

1. Environment
2. Business Objectives
3. Building Blocks: Matching and Ranking
4. Mixing Relevance Signals
5. Semantic Analysis
6. Search Methods for Merchandising
7. Relevance Tuning
8. Architecture of Merchandising Search Services
9. Summary

Chapter 5 - Recommendations

1. Environment
2. Business Objectives
3. Quality Evaluation
4. Overview of Recommendation Methods
5. Content-based Filtering
6. Introduction to Collaborative Filtering
7. Neighborhood-based Collaborative Filtering
8. Model-based Collaborative Filtering
9. Hybrid Methods
10. Contextual Recommendations
11. Non-Personalized Recommendations
12. Multiple Objective Optimization
13. Architecture of Recommender Systems
14. Summary

Chapter 6 - Pricing and Assortment

1. Environment
2. The Impact of Pricing
3. Price and Value
4. Price and Demand
5. Basic Price Structures
6. Demand Prediction
7. Price Optimization
8. Resource Allocation
9. Assortment Optimization
10. Architecture of Price Management Systems
11. Summary

 

Details zur Ausgabe

• Verlag: Grid Dynamics
• Erscheinungsdatum: 2017
• Sprache: Englisch
• ISBN 10: 0692989048
• ISBN 13: 9780692989043
• Einband: Tapa dura
• Anzahl der Seiten: 508
• Kontakt zum Hersteller: Nicht verfügbar