This track will teach you how to understand conjoint studies, the right way to employ MaxDiff (generally, with large lists and for segmentation studies), non-conjoint pricing techniques as well as the common pitfalls to avoid when undertaking drivers or segmentation analysis. Also included is an hour-long expert panel discussion where you can learn from leading practitioners in the field.
In this three-part session you will learn:
• Why conjoint analysis has flourished and been so widely used in business/insights applications and economic investigations,
• The different types of conjoint/choice analysis and learning when you should use each,
• How to build choice models that deliver meaningful data,
• How to design, field, and interpret choice-based conjoint (CBC) exercises,
• How to analyze CBC data using counts, logit, latent class, and hierarchical Bayes,
• How to build and use what-if market simulators.
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Surveys are often the best way to learn about price expectations, thresholds, and to gauge the market’s price sensitivity. This is especially the case for new-to-the-world products or for product improvements. But, asking respondents about price is one of the biggest challenges we face! In this session, we discuss (in a non-technical way) three of the most commonly used survey-based methods for pricing research: monadic price experiments, Gabor-Granger, and Van Westendorp PSM. We compare the approaches and highlight the pros and cons of each.
MaxDiff has become an increasingly popular research technique, whether it’s for advertising claim ranking or product feature prioritization. In this session we will discuss the do's and don'ts of MaxDiff and how to avoid some of the pitfalls researchers so often fall into. We will highlight some tips and tricks, best practices and many uses of MaxDiff.
Driver analysis and segmentation are two things marketing research analysts do about as frequently as they run choice experiments. Worse ways of doing driver analysis are easy to identify and also easy to avoid by using demonstrably better methods. Less happily, a constellation of mutually reinforcing problems can thwart the segmentation analyst. This session will help identify potential shortcomings and solutions for both techniques.
Driver analysis and segmentation are two things marketing research analysts do about as frequently as they run choice experiments. Worse ways of doing driver analysis are easy to identify and also easy to avoid by using demonstrably better methods. Less happily, a constellation of mutually reinforcing problems can thwart the segmentation analyst. This session will help identify potential shortcomings and solutions for both techniques.
Driver analysis and segmentation are two things marketing research analysts do about as frequently as they run choice experiments. Worse ways of doing driver analysis are easy to identify and also easy to avoid by using demonstrably better methods. Less happily, a constellation of mutually reinforcing problems can thwart the segmentation analyst. This session will help identify potential shortcomings and solutions for both techniques.
While there are hundreds of ways to segment customer groups, one of the increasingly popular approaches applies choice-based techniques, such as MaxDiff. This session will teach you the benefits of using MaxDiff to cluster customers and walks you through the steps of designing it and analyzing it. Participants will become comfortable setting up MaxDiff experiments and, using Latent Class logistic regression, segment customers into distinct groups.
While there are hundreds of ways to segment customer groups, one of the increasingly popular approaches applies choice-based techniques, such as MaxDiff. This session will teach you the benefits of using MaxDiff to cluster customers and walks you through the steps of designing it and analyzing it. Participants will become comfortable setting up MaxDiff experiments and, using Latent Class logistic regression, segment customers into distinct groups.
While there are hundreds of ways to segment customer groups, one of the increasingly popular approaches applies choice-based techniques, such as MaxDiff. This session will teach you the benefits of using MaxDiff to cluster customers and walks you through the steps of designing it and analyzing it. Participants will become comfortable setting up MaxDiff experiments and, using Latent Class logistic regression, segment customers into distinct groups.
MaxDiff has become the gold standard technique when it comes to ranking a series of items, but it can often become labourious for respondents when we are required to test a large number of items. In this session we will discuss the best ways to handle MaxDiff studies when you have a large number of items (60+) in order to ensure respondents do not get too fatigued.
MaxDiff has become the gold standard technique when it comes to ranking a series of items, but it can often become labourious for respondents when we are required to test a large number of items. In this session we will discuss the best ways to handle MaxDiff studies when you have a large number of items (60+) in order to ensure respondents do not get too fatigued.
MaxDiff has become the gold standard technique when it comes to ranking a series of items, but it can often become labourious for respondents when we are required to test a large number of items. In this session we will discuss the best ways to handle MaxDiff studies when you have a large number of items (60+) in order to ensure respondents do not get too fatigued.
This presentation investigates whether large language models (LLMs) can replicate human behaviour in answering both Conjoint and MaxDiff choice tasks. The research comprising of more than 50 experiments, reviewed different LLM’s, parameter settings such as temperature, and whether different executions of the prompts would yield better results. More than 250,000 LLM choices were generated to see how close LLM’s could get to replicating the utility structure of real-world Conjoint and MaxDiff projects and the presentation will answer eight key hypotheses that were tested. The big question is whether machines can replace human respondents or is there still a role for human respondents and analysts!
This presentation investigates whether large language models (LLMs) can replicate human behaviour in answering both Conjoint and MaxDiff choice tasks. The research comprising of more than 50 experiments, reviewed different LLM’s, parameter settings such as temperature, and whether different executions of the prompts would yield better results. More than 250,000 LLM choices were generated to see how close LLM’s could get to replicating the utility structure of real-world Conjoint and MaxDiff projects and the presentation will answer eight key hypotheses that were tested. The big question is whether machines can replace human respondents or is there still a role for human respondents and analysts!
Bryan brings us a run down of the advances made from the best papers from the 2024 A&I Summit, held during May in San Antonio, Texas.
Bryan brings us a run down of the advances made from the best papers from the 2024 A&I Summit, held during May in San Antonio, Texas.
Choice modelling experts from Ipsos, GfK & Sawtooth are on hand to discuss an array of topics as well as to answer any questions you might have on the world of choice modelling.
Choice modelling experts from Ipsos, GfK & Sawtooth are on hand to discuss an array of topics as well as to answer any questions you might have on the world of choice modelling.
