Forecasting Models for Marketing Decisions

About this course: How will customers act in the future? What will demand for our products and services be? How much inventory should we order for the next season? Beyond simply forecasting what customers will do, marketers need to understand how their actions can shape future behavior. In Developing Forecasting Tools with Excel, learners will develop an understanding of the basic components of a forecasting model, how to build their own forecasting models, and how to evaluate the performance of forecasting models. All of this is done using Microsoft Excel, ensuring that learners can take their skills and apply them to their own business problems.

Who is this class for: Prerequisites: It is recommended that you complete the "Meaningful Marketing Insights" and "Managing Uncertainty in Marketing Analytics" courses offered by Coursera before starting this course. Learners should have the ability to extract information from the data available and make meaningful insights, understanding of regression methods, and basic knowledge of Microsoft Excel.

Created by:  Emory University

• Taught by:  David Schweidel, Associate Professor of Marketing

  Goizueta Business School

Basic Info Course 3 of 6 in the Foundations of Marketing Analytics Specialization Level Intermediate Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.1 stars Average User Rating 4.1See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Emory University 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.

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.


4 videos, 3 readings expand

1. Video: Approaches to Forecasting
2. Reading: Simple Forecasting Model Assignment
3. Video: Regression - Based Modeling
4. Video: Examining the Residuals
5. Video: Assessing Forecasting Performance
6. Reading: A Predictive Analytics Primer
7. Reading: Nowcasting: A Model for the Future of Marketing

Graded: Module 1

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.


6 videos, 5 readings expand

1. Reading: Read This First
2. Video: How to Use Customer Centric Analytics
3. Video: Business Decisions Drive Customer Choices
4. Video: Illustrating Customer Acquisition in Excel
5. Video: Timing Models in Marketing
6. Reading: Leveraging Customer Analytics: The Insurance Industry
7. Reading: Companies still struggle to unlock customer data analytics insight
8. Reading: Retention Exercise Instructions
9. Video: Model for Retention Excel Demonstration
10. Reading: Kiwi Bubbles Instructions
11. Video: Kiwi Bubbles Exercise Excel Demonstration

Graded: Module 2

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.


4 videos, 2 readings expand

1. Video: Managing Customer Equity
2. Video: Acquisition, Retention, & Market Value
3. Video: Customer Valuation
4. Video: Marketing to New & Current Customers
5. Reading: What You Should Know About Customer Success Technology
6. Reading: Online Communities Foster Customer Loyalty
7. Discussion Prompt: Social Networking and Customer Valuation

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 frozen food category. In addition to weekly sales and pricing, for the focal brand we have information on whether the product was featured in the store’s advertising (e.g., newspaper circular) and if the product was on display in the store. We also have pricing information from competitors. In this module, we will build a series of regression models to evaluate the impact of the brand’s actions and competitors’ actions.


4 readings expand

1. Reading: Marketing Mix Modeling
2. Reading: Instructions for Exercise
3. Discussion Prompt: Discussion Question
4. Reading: Instructions for Exercise
5. Reading: Instructions for Exercise

Graded: Conducting Exploratory Analysis Quiz
Graded: Building a Marketing Mix Model Quiz
Graded: Marketing Mix Model Peer Assessment