Data Analytics Methods for Marketing

This course is a part of Meta Marketing Analytics, a 5-course Specialization series from Coursera.

This course explores common analytics methods used by marketers. You’ll learn how to define a target audience using segmentation with K-means clustering. You’ll also explore how linear regression can help marketers plan and forecast. You’ll learn to evaluate the effectiveness of advertising using experiments as well as observational methods and you’ll explore methods to optimize your marketing mix; marketing mix modeling and attribution. Finally, you’ll learn to evaluate sales funnel shapes, visualize and optimize them. By the end of this course you will be able to: • Describe when analytics is most commonly used in marketing • Understand your audience using analytics and variable descriptions • Segment a population into different audiences using cluster analysis • Use historical data to plan your marketing across different channels • Use linear regression to forecast marketing outcomes • Describe marketing mix modeling • Describe attribution modeling • Apply different attribution models • Evaluate advertising effectiveness and describe the shortcomings • Describe the use of experiments to evaluate advertising effectiveness • Explain how A/B testing works and how you can use it to optimize ads • Evaluate results of an experiment and assess the strength of the experiment • Evaluate and optimize your sales funnel This course is for people who want to learn how to plan and forecast marketing efforts as well as evaluate marketing methods and sales funnels for optimization. Learners don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Ideally learners have already completed course 1 (Marketing Analytics Foundation), course 2 (Introduction to Data Analytics), and course 3 (Statistics for Marketing) in this program.