Conducting market segmentation analysis and committing to a long-term market segmentation strategy is a complex and challenging journey for any organisation. This course guides you through the entire process of market segmentation analysis and offers a ten-step process that makes customer segmentation efficient and organised.
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. We also cover segmentation variables such as geographic segmentation, psychographic segmentation, behavioural segmentation, and demographic segmentation.
In this course, we will explore how to leverage statistical concepts into the organisation’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 visualisations 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 enrol in this course.
Module 1: Introduction and Steps 1 and 2 We define market segmentation analysis, explain why it is the basis of marketing planning, and why it informs both strategic and tactical marketing decisions. We provide an overview of the ten-step process in market segmentation analysis. In Step 1, we explain the requirements for market segmentation, helping learners decide whether their organisation is ready to segment. In Step 2 we specify the ideal target segment, including segment evaluation criteria.
Module 2: Steps 3 and 4 In Step 3, we describe collecting data and define the segmentation variables and criteria, and discuss different sources of data. In Step 4 we explore the data and discuss data cleaning and how to pre-process categorical and numeric variables. We also demonstrate how to use R, to assist you with exploring the data.
Module 3: Step 5 In Step 5, we extract market segments and group consumers using distance-based, hierarchical, and partitioning methods; hybrid approaches; and model-based methods. We also explain data structure analysis.
Module 4: Steps 6 and 7 In Steps 6 and 7, we profile and describe segments. We use traditional approaches as well as visualisation techniques to identify key characteristics of market segments. In Step 7 we develop and visualise a complete picture of market segments; this includes testing and predicting segment differences in descriptor variables.
Module 5: Steps 8, 9 and 10 In Step 8, we select the target segments including the tasks targeting decision and market segment evaluation. In Step 9 we customise the marketing mix. We also discuss the implications of market segmentation for marketing mix decisions concerning product, price, place, and promotion. Finally, in Step 10, we evaluate the success of the segmentation strategy, and stability of segment membership. We conclude with a discussion of segment hopping and segment evolution.