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This post will walk you through how to format your respondent-level conjoint data when programmed using your survey platform of choice. There are many survey…

Text data is one of the great pains of survey analysis. Open-ended questions allow us to obtain data that is less biased by our preconceptions.…

Text data often refers to entities, such as people, organizations, or places. These entities can be automatically extracted from text data, and then used in…

It can often be difficult and time-consuming to organize raw text data into meaningful insights. Manually coding even a single text question can take several…

Categorizing text data can be a time-consuming and expensive activity.  In cases where time is short and budgets low, using automatic categorization of text data…

In a standard choice experiment, respondents are presented with alternatives which have a common set of attributes. Alternative-specific designs relax this requirement and are designed to…

A correspondence analysis is the standard tool for creating brand maps. It shows which brands compete with which other brands and the basis for that…

After running a market segmentation, one problem many researchers commonly face is how to go about assigning respondents in subsequent surveys to the existing s...

Choice-based conjoint (CBC) studies usually specify a fixed number of levels for each attribute. The resulting attribute then becomes categorical. But that doesn’t mean you…

Modern tools for analyzing conjoint analysis, such as hierarchical Bayes, produce rich data showing preferences for each person in a market. The main deliverable from…

Indifference curves are a way of showing relative preferences for quantities of two things (e.g., preferences for price versus delivery times for fast food). This…

In this blog post, I describe the Efficient algorithm for generating choice model designs. This algorithm is used for generating choice model designs with partial profiles,…

While choice-based conjoint analysis represents one of the more sophisticated techniques used in market research, presentation of its results commonly consists only of a simulator,…

In a stated preference discrete choice experiment, respondents are asked a number of questions. Each question asks them to choose between a number of alternatives…

Today, you can produce a wide range of choice model experimental designs with numerous different algorithms. But with all this design diversity, how do you…

Choice experiments, also known as choice-based conjoint (CBC), are widely used for predicting the performance of new products and changes to products’ designs and portfolios.…

This post introduces the key concepts in designing experiments for choice-based conjoint analysis (also known as choice modeling). I use a simple example to describe…

Working out the sample size required for a choice-based conjoint study is a mixture of art and science. What makes it tricky is that the…

The hard bit of designing a choice-based conjoint analysis (choice modeling) study is creating the experimental design. However, there are a few others parts of…

Ready to dive further into conjoint analysis? In this post I describe the main applications of choice-based conjoint analysis (choice modeling; CBC). If you haven’t…

Choice-based conjoint analysis is a technique for quantifying how the attributes of products and services affect their performance. It is used to help decision makers…

This page lists the key frameworks and processes for identifying relevant variables to use when segmenting a market. The best way to identify relevant segmentation…

Golden questions are questions used to allocate people to segments. They are also known as self-selection questions. The main applications of golden questions are: As discovery…

When segmenting a market, a practical challenge is to work out the number of segments. There are eight approaches to choosing the number of segments:…