Get to know your customers and learn about the process of dividing a broad consumer market into sub-groups of consumers (segments) based on shared characteristics such as geography, demographic, behaviour and psychographic.

Course syllabus

• As an introduction to this course we will present a best practice, evidence based, scientific and reproducible workflow for our analysis using the statistical programming language R. We will learn about R scripts and Rmarkdown documents.
• The preparation of a suitable dataset forms the basis of the actual segmentation analysis. In particular we discuss how to engineer features, suitable pre-processing techniques (imputation, scaling, encoding) and approaches to reduce the dimensions of our modeling dataset.
• A very informative segmentation approach is the RFM (Recency-Frequency-Monetary) analysis. It is a marketing technique used to determine quantitatively how recently customers have purchased (recency), how often they purchase (frequency), and how much they spend (monetary). We will learn how to split the modeling dataset into suitable RFM groups and how to present the findings in tabular and chart form.
• Using clustering algorithms we demonstrate how to find natural groups of customers having similar characteristics.
• Evaluating the quality of a segmentation is a critical step to obtain robust results. We will explain relevant metrics and diagnostic plots to determine segmentation quality.
• Finally, we show to prepare informative visualizations of the identified customer segments to identify their characteristics profile.

Course prerequisites

The course is aimed at business and data analysts who are familiar with working with tabular datasets (SQL, spreadsheets) and have at least beginner-level experience with the R programming language.

What should you bring?

• Laptop with latest version of R and RStudio installed
• We will send a list of packages to install after registration
• After registration we will provide you with links to download all course materials

Instructor — Stefan Schliebs, PhD

Stefan is the lead data scientist at Quantiful. He holds a Master degree in Computer Science from the University of Leipzig, Germany and a PhD from the Auckland University of Technology. He received numerous academic rewards, published more than 35 scientific articles in international journals, conferences and books and lectured data science courses on university level to 100s of students. He worked as a data scientist in various industries and has 10+ years of experience in commercial and academic environments. He is a co-organizer of the R User Meetup Group Auckland and Hackathon participant.

Location

Details will be announced closer to the course date.

Course fees

TBA.

There are limited seats at a discounted price for early career professionals and graduates.

Interested?

Please leave us your email address and we will get back to you with more details shortly.