Predict Consumer Decisions with Choice-Based Conjoint

A course showing managers and researchers how to run Choice Based Conjoint experiments to predict people’s decisions

A course showing managers and researchers how to run Choice Based Conjoint experiments to predict people’s decisions

What you’ll learn
Understand the capabilities of Choice-Based Conjoint and Discrete Choice Experiments
Recognise the requirements of running a CBC and DCE research project
Be able to develop and run your own small scale CBC and DCE project
Practice your design and analysis skills with real data sets and extra examples
Requirements
There is no assumed knowledge for the early topics in this course.
For those progressing to the advanced topics in this course an understanding of introductory level statistics is recommended.
Access to MS Excel for running analysis is essential for those progressing to advanced topics. Additional topics covering analysis using SPSS are also provided but they are not essential.
Description
Choice-Based Conjoint, also often called Discrete Choice Experimentation, is a powerful research and management tool that allow us to understand and predict people’s preferences. Whether it is a manager wanting to predict product preferences, a health researcher wanting to explore the treatment preferences of patients, or a transport engineer examining people’s choices of public transport this tool can provide the insight needed. This course starts with an introduction to the capabilities and applications of Choice Based Conjoint that is suitable for all audiences. It explains the basic requirements of a CBC research project, and details the outputs that can be obtained. The course then continues on to more advanced topics where you will get training and hands on experience developing and running a CBC project. This includes design, data collection, analysis and reporting of results. This course is suitable for managers, marketing/business researchers, and academic researchers interested in building an understanding of CBC. If you are a PhD student I am happy to provide a discounted rate for this course. Please contact me through the Udemy messaging service. Provide your university email address, your name, and a link to your supervisor’s profile on your university website.

Overview

Section 1: Introduction and Overview

Lecture 1 The lesson plan for this course

Lecture 2 How to progress your learning (for students only)

Section 2: An Overview of Choice Based Conjoint (An ideal summary for Managers)

Lecture 3 The aim of all conjoint tasks: Measuring utility

Lecture 4 The ways of measuring utility

Lecture 5 The look and feel of Choice Based Conjoint

Lecture 6 The capabilities of Choice Based Conjoint

Lecture 7 An example of a Choice Based Conjoint study

Section 3: Designing a Choice Based Conjoint Experiment

Lecture 8 The types of experimental designs

Lecture 9 Alternative design: factorial designs

Lecture 10 Alternative design: main effects designs

Lecture 11 Alternative design: natural designs

Lecture 12 Choice set design: random designs

Lecture 13 Choice set design: combinatorials

Lecture 14 Choice set design: balanced incomplete block designs

Section 4: Laying Out The Survey

Lecture 15 Layout of the choice sets

Lecture 16 Giving effective instructions

Lecture 17 Software for running the survey

Section 5: A Brief Introduction to Analysing Your Data

Lecture 18 Determining your analysis options

Lecture 19 Simple analysis methods

Lecture 20 More complex analysis methods

Section 6: Analysing Your Data: Regression using MS Excel

Lecture 21 A brief overview

Lecture 22 Data layout needed

Lecture 23 Running the analysis

Lecture 24 Interpreting the output

Lecture 25 Calculating choice probabilities and using them

Section 7: Analysing Your Data: The (c)MNL in SPSS

Lecture 26 A brief overview

Lecture 27 Data layout needed

Lecture 28 Running the analysis

Lecture 29 Interpreting the output

Lecture 30 Calculating choice probabilities and using them

Section 8: Building Decision Support Systems with the Results

Lecture 31 An overview of Decisions Support Systems

Lecture 32 The basics of building a DSS

Section 9: Thank You and Good Night

Lecture 33 Further learning for CBC

Lecture 34 Some suppliers that are handy to know about

Managers wanting to understand the capabilities of CBC and DCEs for measuring people’s preferences,Researchers wanting to learn how to implement CBC and DCE projects