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Digital Marketing Analytics: Tools and Techniques

Learn how to leverage leading tools and approaches to digital marketing data analysis. Dive into SEO and Paid search strategies, web analytics, online testing, machine learning and AI/Big Data applications to strengthen your digital marketing efforts and leverage your resources most effectively.

Digital Marketing Analytics: Tools and Techniques

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Comienza el 15 oct
Termina el 31 dic
4 semanas estimadas
3–5 horas por semana
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Sobre este curso

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Businesses today have access to an increasingly large amount of detailed customer data, and this influx of “big data” is only going to continue. Combined with a detailed history of marketing actions, there is a newfound potential for deriving actionable insights, but you need the tools to do so. Using real-world applications from various industries, this course will help you understand the tools and strategies used to make data-driven decisions that you can put to use in your own company or business.

This valuable data may include in-store and online customer transactions, customer surveys, web analytics, as well as prices and advertising. You’ll also learn how to assess critical managerial problems, develop relevant hypotheses, analyze data and, most importantly, draw inferences to create convincing narratives which yield actionable results. Artificial intelligence and machine learning will be explored as tools to deepen analytical skills and acumen and hone decision making.

This comprehensive exploration into digital marketing analytics tools and techniques is critical knowledge for any marketing influencers, digital marketing analysts and product and brand decision makers within small and medium businesses as well as larger organizations with international reach.

This course is part of Maryland Smith’s Digital Marketing Professional Certificate. For more information, see here.

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  • Idioma: English
  • Transcripción de video: English
  • Programas asociados:

Lo que aprenderás

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2.1 SEO and SEM - KPIs and keyword strategies

2.2 Web Analytics - A/B Testing

2.3 Recommendation Systems

2.4 Machine Learning/AI applications/Big Data

2.5 Text and Image Analysis

2.6 Attribution/MMM

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