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{ "item_title" : "Introduction to Algorithmic Marketing", "item_author" : [" Ilya Katsov "], "item_description" : "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 AmericasIt is a must-read for both data scientists and marketing officers―even better if they read it together.―Andrey Sebrant, Director of Strategic Marketing, YandexThe 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 DynamicsTable of ContentsChapter 1 - IntroductionThe Subject of Algorithmic MarketingThe Definition of Algorithmic MarketingHistorical Backgrounds and ContextProgrammatic ServicesWho Should Read This Book?SummaryChapter 2 - Review of Predictive ModelingDescriptive, Predictive, and Prescriptive AnalyticsEconomic OptimizationMachine LearningSupervised LearningRepresentation LearningMore Specialized ModelsSummaryChapter 3 - Promotions and AdvertisementsEnvironmentBusiness ObjectivesTargeting PipelineResponse Modeling and MeasurementBuilding Blocks: Targeting and LTV ModelsDesigning and Running CampaignsResource AllocationOnline AdvertisementsMeasuring the EffectivenessArchitecture of Targeting SystemsSummaryChapter 4 - SearchEnvironmentBusiness ObjectivesBuilding Blocks: Matching and RankingMixing Relevance SignalsSemantic AnalysisSearch Methods for MerchandisingRelevance TuningArchitecture of Merchandising Search ServicesSummaryChapter 5 - RecommendationsEnvironmentBusiness ObjectivesQuality EvaluationOverview of Recommendation MethodsContent-based FilteringIntroduction to Collaborative FilteringNeighborhood-based Collaborative FilteringModel-based Collaborative FilteringHybrid MethodsContextual RecommendationsNon-Personalized RecommendationsMultiple Objective OptimizationArchitecture of Recommender SystemsSummaryChapter 6 - Pricing and AssortmentEnvironmentThe Impact of PricingPrice and ValuePrice and DemandBasic Price StructuresDemand PredictionPrice OptimizationResource AllocationAssortment OptimizationArchitecture of Price Management SystemsSummary", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/0/69/298/904/0692989048_b.jpg", "price_data" : { "retail_price" : "50.00", "online_price" : "50.00", "our_price" : "50.00", "club_price" : "50.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Introduction to Algorithmic Marketing|Ilya Katsov

Introduction to Algorithmic Marketing : Artificial Intelligence for Marketing Operations

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Overview

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

This item is Non-Returnable

Details

  • ISBN-13: 9780692989043
  • ISBN-10: 0692989048
  • Publisher: Grid Dynamics
  • Publish Date: December 2017
  • Dimensions: 9 x 6 x 1.5 inches
  • Shipping Weight: 2.39 pounds
  • Page Count: 508

Related Categories

    1. Books
    2. Computers
    3. Artificial Intelligence - General
    1. Books
    2. Business & Economics
    3. Marketing - Direct
    1. Books
    2. Business & Economics
    3. Operations Research

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