Forecasting Models for Marketing Decisions

Emory University via Coursera

Go to Course: https://www.coursera.org/learn/forecasting-models-marketing-decisions

Introduction

### Course Review: Forecasting Models for Marketing Decisions on Coursera In today’s data-driven world, understanding customer behavior and predicting market trends has become indispensable for businesses aiming to thrive. The course titled **"Forecasting Models for Marketing Decisions"** offered on Coursera provides a comprehensive roadmap for marketers who want to harness the power of predictive analytics. This course presents not just theoretical insights but also practical tools to make informed marketing decisions rooted in data analysis. #### Overview The course poses critical questions for marketers such as: How will customers act in the future? What will demand for our products be? How much inventory should we order for the upcoming season? Beyond just predicting future customer actions, this course emphasizes the importance of understanding how marketing strategies can influence these behaviors. #### What You Will Learn The course is structured into several key modules, each meticulously designed to build upon your knowledge and skills: 1. **Basics of Forecasting Models**: - This module initiates your journey by identifying the essential components of a forecasting model derived from historical data patterns. You will learn to evaluate forecasting models' performances through both in-sample and out-of-sample metrics, providing a solid foundation for the more complex concepts that follow. 2. **Customer Analytics: Predicting Individual Customer Behavior**: - A pivotal segment that delves into customer insights, this module serves as a refresher for those who may have encountered these concepts previously. Understanding individual customer behavior is crucial for tailoring marketing strategies to drive engagement and conversion. 3. **Managing Customer Equity: Linking Customer Analytics to Customer Value**: - Here, you'll explore the interrelationship between customer equity, acquisition, retention, and overall market value. This module teaches how to decompose customer value into its fundamental components, allowing marketers to assess strategies focused on customer lifetime value. 4. **Marketing Mix Modeling**: - This practical module focuses on utilizing forecasting models to assess marketing mix activities. Through real-world data, particularly from a popular frozen food category, you will engage in building regression models to evaluate the effectiveness of various marketing actions, including pricing strategies and promotional activities. #### Why You Should Take This Course - **Hands-On Experience**: The course includes practical exercises that allow learners to build and refine their forecasting models using Excel, a widely utilized tool in marketing analytics. - **Strong Foundations in Analytics**: With its focus on customer analytics and managing customer equity, this course equips marketers with the skills to adapt and fine-tune their strategies based on predictive data. - **Real-World Applications**: The use of actual market data enhances the learning experience by providing context and relevance to the theoretical concepts discussed. This is vital for learners who aspire to apply knowledge directly to their marketing roles. #### Conclusion and Recommendation If you're a marketing professional seeking to elevate your data analytics skills and enrich your decision-making capabilities, **"Forecasting Models for Marketing Decisions"** is a highly recommended course. It effectively combines theory and practical application, ensuring that you not only learn essential concepts but also gain the ability to apply them to real-world challenges. This course is ideal for anyone looking to integrate forecasting models into their marketing strategies—whether you’re a beginner eager to learn the basics or an experienced marketer looking to brush up on advanced techniques. Enroll today, and take your first step toward making informed, data-driven marketing decisions that can shape the future of your business.

Syllabus

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.

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.

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.

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.