
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…
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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.…
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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…
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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…
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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…
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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…
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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…
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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...
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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…
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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…
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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…
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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,…
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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,…
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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…
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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…
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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.…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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:…
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