Digital Marketing 2

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 marketing influencers, digital marketing analysts, and product and brand decision-makers within small and medium businesses as well as larger organizations with international reach. What You'll Learn in this Course: Learn how to leverage leading tools and approaches to digital marketing data analysis. Dive into Search Engine Marketing and Website analytics, online testing, machine learning, and AI/Big Data applications to strengthen your digital marketing efforts and leverage your resources most effectively. Course Objectives: This course will cover the fundamentals of digital marketing. By the end of this course, you will be able to: 1- Analyze and assess the performance of paid search campaigns, diagnose potential problems, and recommend adjustments to the digital marketing campaign. 2- Describe the importance of Search Engine Optimization and Recommendation Systems in digital environments. 3- Evaluate campaign analytics and use online testing to determine how design affects the performance of a digital marketing campaign. 4- Describe the Paradigm shift in machine learning methods. 5- Identify the process of evaluating the performance of machine learning algorithms. 6- Describe the expanding application of big data as they apply to neural networks

• Analyze and assess paid search campaign performance and recommend improvements
• Describe the role of SEO and recommendation systems in digital environments
• Evaluate campaign analytics and use online testing to optimize performance
• Understand paradigm shifts in machine learning methods and evaluate their performance
• Identify the application of big data in neural networks

• Basic familiarity with digital marketing terminology and concepts
• Readiness for practical application through exercises or case studies