Marketing Measurement and Analytics : An Introduction

This book is a comprehensive guide to aligning your marketing strategies with business objectives while embracing the latest innovations in data and AI-driven analytics. You'll cover topics such as: aligning measurement to business goals by distinguishing between business and marketing KPIs and...

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Opis bibliograficzny
1. autor: Kihlström, Greg, 19..-
Format: Livre numérique
Język:Anglais
Wydane: Boston, MA : Mercury Learning and Information 2024.
Paris : Cyberlibris
Dostęp online:Accès Université d'Orléans et IFPM
Komentarz: Couverture. https://static2.cyberlibris.com/books_upload/300pix/9781501520426.jpg
Cyberlibris (ScholarVox) corpus sciences économiques et gestion
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Marketing Measurement and Analytics, An Introduction, Greg Kihlström, Boston, MA, Mercury Learning and Information, 2024, 1 vol. (270 p.), 978-15-0152-314-4
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520 |a This book is a comprehensive guide to aligning your marketing strategies with business objectives while embracing the latest innovations in data and AI-driven analytics. You'll cover topics such as: aligning measurement to business goals by distinguishing between business and marketing KPIs and choosing the right metrics to guide your strategy; building adaptable systems that link organizational goals to actionable marketing tactics; exploring AI-based tools, predictive analytics, and generative AI to refine your data strategies; and analyzing results, run multi-channel tests, and create a culture of experimentation and optimization. Through a unique blend of expert insights, practical frameworks, and a recurring case study, this book brings theoretical concepts to life, making them actionable and relatable for any organization. Features: Integrates cutting-edge AI technologies into your measurement processes; Uses a recurring case study to demonstrate real-world applications of measurement concepts; Analyzes results and runs multi-channel tests to create a culture of experimentation and optimization 
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