- Platform
- Coursera
- Provider
- Emory University
- Length
- 4 weeks
- Language
- English
- Credentials
- Paid Certificate Available
- Part of
- Course Link
Overview
How do consumers see your brand relative to your competitors? How should a new product be positioned when it’s launched? Which customer segments are most interested in our current offerings? For these questions and many others, surveys remain the tried and true method for gaining marketing insights. From one-off customer satisfaction surveys to brand tracking surveys that are administered on a continuous basis, they provide the information that marketers need to understand how their products, services and brands are seen by consumers. In Analytic Methods for Survey Data, learners will become familiar with established statistical methods for converting survey responses to insights that can support marketing decisions. Techniques discussed include factor analytics, cluster analysis, discriminant analysis and multi-dimensional scaling. These techniques are presented within the STP (Segmentation, Positioning, Targeting) Framework, enabling learners to apply the analytic techniques to develop a marketing strategy. It is recommended that you complete the Meaningful Marketing Insights course offered by Coursera before taking this course.
Note: This course would require using XL Stat, an Excel Add-on that students would need to purchase. XL Stat offers a 30-day free trial, so students could complete this course without incurring additional expense.
Taught by
David Schweidel
How do consumers see your brand relative to your competitors? How should a new product be positioned when it’s launched? Which customer segments are most interested in our current offerings? For these questions and many others, surveys remain the tried and true method for gaining marketing insights. From one-off customer satisfaction surveys to brand tracking surveys that are administered on a continuous basis, they provide the information that marketers need to understand how their products, services and brands are seen by consumers. In Analytic Methods for Survey Data, learners will become familiar with established statistical methods for converting survey responses to insights that can support marketing decisions. Techniques discussed include factor analytics, cluster analysis, discriminant analysis and multi-dimensional scaling. These techniques are presented within the STP (Segmentation, Positioning, Targeting) Framework, enabling learners to apply the analytic techniques to develop a marketing strategy. It is recommended that you complete the Meaningful Marketing Insights course offered by Coursera before taking this course.
Note: This course would require using XL Stat, an Excel Add-on that students would need to purchase. XL Stat offers a 30-day free trial, so students could complete this course without incurring additional expense.
Syllabus
Introduction to Factor Analysis
This module will provide readings and discussions to provide an introduction to the topic of Factor Analysis. It is recommended that you complete the Meaningful Marketing Insights course offered by Coursera before taking this course.
Implementing Factor Analysis
This module will provide lectures and exercises that will inform students on how to determine the number of factors to consider in your analysis and to evaluate the fit of the data.
Customer Segmentation
This module will introduce components of customer segmentation to students. Students will use this knowledge to be able to analyze data and make more informed business decisions.
Perceptual Maps
This module will explain and describe perceptual maps. Students will be able to create perceptual maps and also analyze data from perceptual maps.
Introduction to Factor Analysis
This module will provide readings and discussions to provide an introduction to the topic of Factor Analysis. It is recommended that you complete the Meaningful Marketing Insights course offered by Coursera before taking this course.
Implementing Factor Analysis
This module will provide lectures and exercises that will inform students on how to determine the number of factors to consider in your analysis and to evaluate the fit of the data.
Customer Segmentation
This module will introduce components of customer segmentation to students. Students will use this knowledge to be able to analyze data and make more informed business decisions.
Perceptual Maps
This module will explain and describe perceptual maps. Students will be able to create perceptual maps and also analyze data from perceptual maps.
Taught by
David Schweidel