About the course
Conducting market segmentation analysis and committing to a long-term market segmentation strategy is a complex and challenging journey for any organization. This course guides you through the entire process of market segmentation analysis and offers a ten-step process that makes customer segmentation efficient and organized.
This course begins with the decision to conduct market segmentation analysis and continues through to the final stages of evaluating the success of the strategy and monitoring the market for possible changes. You will also cover segmentation variables such as geographic segmentation, psychographic segmentation, behavioral segmentation, and demographic segmentation.
In this course, you will explore how to leverage statistical concepts into the organization’s segmentation strategy, such as the hierarchical clustering and partitioning methods, exploratory data analysis, biclustering, mixture models, and regression models.
The concepts and skills you will gain in this course are relevant in a wide range of contexts in both the for- and not-for-profit sectors.
This course enables you to conduct customer segmentation analysis. You can replicate the calculations and visualizations demonstrated in the customer segmentation models by downloading the data and the R code. R is a free open-source statistical computing environment, and is widely acknowledged as the universal language of computational statistics.
This course is based on and taught by the authors of the book Market Segmentation Analysis: Understanding It, Doing It, and Making It Useful. You will have full access to this valuable resource when you enroll in this course.
What you will learn
By the end of this course, you will know and understand:
- The basics of market segmentation.
- The ten steps of a market segmentation analysis and each one's purpose.
- The key pitfalls that can occur at each step.
- How to undertake each of the ten steps of market segmentation analysis yourself.
- How to commission a market segmentation analysis.
- What questions to ask to ensure your market segmentation analysis is valid.
About the instructors
Sara Dolnicar currently works as the Research Professor in Tourism at UQ Business School. She has previously worked at the School of Management & Marketing at the University of Wollongong and the School of Tourism at the Vienna University of Economics & Business (Austria) where she also served as the Secretary General of the Austrian Society for Applied Research in Tourism. Her core research interests are the improvement of market segmentation methodology and the testing and refinement of measures used in social science research. She has investigated a range of different applied research areas, including sustainable tourism and tourism marketing, environmental volunteering, foster carer and public acceptance of water alternatives and water conservation measures. Sara has (co-)authored more than 300 refereed papers, including more than 140 journal articles and led a total of twelve Australian Research Council (ARC) grants. In 2011 she took up a prestigious ARC Queen Elizabeth II Fellowship. Sara holds a Masters and PhD degree from the Vienna University of Economics and Business and a Masters degree in Psychology at the University of Vienna (Austria).
Associate Professor Bettina Gruen holds a PhD degree in Applied Mathematics from the Vienna University of Technology and qualified in Statistics at the Johannes Kepler University Linz (Austria). After completing her PhD she worked as a Hertha Firnberg scholar at the Vienna University of Business and Economics. In 2011 she joined the Johannes Kepler University Linz where she currently works as Associate Professor in the Department of Applied Statistics. Her core research interests include finite mixture models and their application in model-based clustering including estimation as well as implementation in statistical software; and quantitative methods in economics, marketing and tourism. To date, Bettina has (co)-authored more than 50 refereed journal articles and 7 R packages freely available from the Comprehensive Archive Network (CRAN).
Professor Friedrich Leisch holds a Master and PhD degree in Applied Mathematics from the Vienna University of Technology. His research interests include statistical computing, multivariate statistics, cluster analysis, mixture models, generalised regression, biostatistics, and software development and statistical applications in life and business sciences. He has been a member of the R Core Development Team since 1997 and is one of the initiators of the Comprehensive R Archive Network CRAN.
Duration: 5 weeks, 6-8 hours per week
Instructors: Sara Dolnicar, Bettina Grün, and Friedrich Leisch
Institution: The University of Queensland