Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing

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 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 The Subject of Algorithmic MarketingThe Definition of Algorithmic MarketingHistorical Backgrounds and ContextProgrammatic ServicesWho Should Read This Book?Summary Chapter 2 - Review of Predictive Modeling Descriptive, Predictive, and Prescriptive AnalyticsEconomic OptimizationMachine LearningSupervised LearningRepresentation LearningMore Specialized ModelsSummary Chapter 3 - Promotions and Advertisements EnvironmentBusiness ObjectivesTargeting PipelineResponse Modeling and MeasurementBuilding Blocks: Targeting and LTV ModelsDesigning and Running CampaignsResource AllocationOnline AdvertisementsMeasuring the EffectivenessArchitecture of Targeting SystemsSummary Chapter 4 - Search EnvironmentBusiness ObjectivesBuilding Blocks: Matching and RankingMixing Relevance SignalsSemantic AnalysisSearch Methods for MerchandisingRelevance TuningArchitecture of Merchandising Search ServicesSummary Chapter 5 - Recommendations EnvironmentBusiness ObjectivesQuality EvaluationOverview of Recommendation MethodsContent-based FilteringIntroduction to Collaborative FilteringNeighborhood-based Collaborative FilteringModel-based Collaborative FilteringHybrid MethodsContextual RecommendationsNon-Personalized RecommendationsMultiple Objective OptimizationArchitecture of Recommender SystemsSummary Chapter 6 - Pricing and Assortment EnvironmentThe Impact of PricingPrice and ValuePrice and DemandBasic Price StructuresDemand PredictionPrice OptimizationResource AllocationAssortment OptimizationArchitecture of Price Management SystemsSummary

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Location: East Hanover, New Jersey

End Time: 2024-10-20T01:58:59.000Z

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