Forecasting Models for Marketing Decisions

Forecasting Models for Marketing Decisions

Course
en
English
12 h
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Source
  • From www.coursera.org
Conditions
  • Self-paced
  • Free Access
  • Fee-based Certificate
More info
  • 4 Sequences
  • Intermediate Level

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Course details

Syllabus

  • Week 1 - Basics of Forecasting Models
    This module will discuss how to identify the necessary components of a forecasting model based on patterns in the history data. You will also be able to evaluate the performance of a forecasting model using both in-sample and out-of-sample metrics.
  • Week 2 - Customer Analytics: Predicting Individual Customer Behavior
    "Meaningful Marketing Insights," This content will be familiar for learners who completed the first course; please think of this portion of the class as a review.
  • Week 3 - Managing Customer Equity: Linking Customer Analytics to Customer Value
    This module will discuss managing customer equity, acquisition, retention, & market value, and customer valuation. You will learn how to decompose customer value into its underlying components.
  • Week 4 - Marketing Mix Modeling
    A common task in developing forecasting models is to use them to make decisions regarding the marketing mix activity. With a marketing mix model, organizations can assess the efficacy of different marketing actions. Included is a sample of data for a popular f...

Prerequisite

None.

Instructors

David Schweidel
Associate Professor of Marketing
Goizueta Business School

Editor

Emory University, located in Atlanta, Georgia, is one of the world's leading research universities. Its mission is to create, preserve, teach and apply knowledge in the service of humanity.

Platform

Coursera is a digital company offering massive open online course founded by computer teachers Andrew Ng and Daphne Koller Stanford University, located in Mountain View, California. 

Coursera works with top universities and organizations to make some of their courses available online, and offers courses in many subjects, including: physics, engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and other subjects.

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