Don’t miss this opportunity to expand your skills and network with leading choice modelling practitioners. This advanced track will focus on techniques such as Menu-Based Choice, Situational Choice Experiments, and Segmentation methods such as SALC - as well as having papers from guest speakers (NiQ & Ipsos) focusing on Volumetric Conjoint, SKU-Based Conjoint Pricing, Conjoint Calibration, Best Practice for Measuring Price Sensitivity and new approaches to Segmentation analysis.
Register nowMenu-Based Choice (MBC) is a flexible choice modeling approach for solving a variety of multi-check (combinatorial) menu-selection problems. Examples include: choosing options to put on an automobile, selections from a restaurant menu, banking options, configuring an insurance policy, purchasing bundled vs. a la carte services including mobile phones, internet, and cable. This session will seek to introduce you to the concept, theory, and practice of undertaking an MBC study, with a particular focus on how this can be used to create Perceptual Choice Experiments.
Menu-Based Choice (MBC) is a flexible choice modeling approach for solving a variety of multi-check (combinatorial) menu-selection problems. Examples include: choosing options to put on an automobile, selections from a restaurant menu, banking options, configuring an insurance policy, purchasing bundled vs. a la carte services including mobile phones, internet, and cable. This session will seek to introduce you to the concept, theory, and practice of undertaking an MBC study, with a particular focus on how this can be used to create Perceptual Choice Experiments.
A situational choice experiment (SCE), differs from a CBC in that its questions show one experimentally designed profile which describes the attributes and levels of the choice situation or context or chooser, and elicits choices among a fixed set of alternatives; SCE then uses polytomous MNL to generate utilities. Few marketers know about SCEs and there doesn’t seem to be a single source reference about them, two limitations we hope to remedy in this presentation.
A situational choice experiment (SCE), differs from a CBC in that its questions show one experimentally designed profile which describes the attributes and levels of the choice situation or context or chooser, and elicits choices among a fixed set of alternatives; SCE then uses polytomous MNL to generate utilities. Few marketers know about SCEs and there doesn’t seem to be a single source reference about them, two limitations we hope to remedy in this presentation.
Based on the findings of our 2025 Sawtooth conference paper, we will present a comprehensive overview of the potential pitfalls of undertaking Volumetric Conjoint in its many guises, and offer some tips and guidance for how to analyse this kind of study.
Based on the findings of our 2025 Sawtooth conference paper, we will present a comprehensive overview of the potential pitfalls of undertaking Volumetric Conjoint in its many guises, and offer some tips and guidance for how to analyse this kind of study.
Did you know that there is more than one way to perform a MaxDiff exercise? Most MaxDiff is based on “Best Worst Case 1”, where each item is a distinct Object. But you can also easily conduct and analyze Best Worst Case 2 (the “Profile” case) experiments if you know how to do it! This session will introduce you to Best-Worst Case 2 and highlight how it can compare to traditional conjoint analysis.
Did you know that there is more than one way to perform a MaxDiff exercise? Most MaxDiff is based on “Best Worst Case 1”, where each item is a distinct Object. But you can also easily conduct and analyze Best Worst Case 2 (the “Profile” case) experiments if you know how to do it! This session will introduce you to Best-Worst Case 2 and highlight how it can compare to traditional conjoint analysis.
An expansive dive into price sensitivity measurement using conjoint analysis. Topics will include: (1) How many choice tasks are necessary for pricing research (2) Display strategies for consumer packaged good research (3) Tips and tricks for handling complicated pricing schemes (4) Discussion of different approaches to calculating price sensitivity and reporting suggestions.
An expansive dive into price sensitivity measurement using conjoint analysis. Topics will include: (1) How many choice tasks are necessary for pricing research (2) Display strategies for consumer packaged good research (3) Tips and tricks for handling complicated pricing schemes (4) Discussion of different approaches to calculating price sensitivity and reporting suggestions.
Recent marketing theories challenge traditional concepts like differentiation and niche targeting, favoring broader market penetration. Is this the end of segmentation? We explore how these theories are transforming segmentation practices and pushing analytical boundaries. New analytics are proposed to assess segment differences and similarities, effectively blending mass marketing with targeted efforts to help marketers achieve both immediate sales activation and sustained brand-building goals.
Recent marketing theories challenge traditional concepts like differentiation and niche targeting, favoring broader market penetration. Is this the end of segmentation? We explore how these theories are transforming segmentation practices and pushing analytical boundaries. New analytics are proposed to assess segment differences and similarities, effectively blending mass marketing with targeted efforts to help marketers achieve both immediate sales activation and sustained brand-building goals.
A persistent challenge in conjoint analysis is the discrepancy between preference shares and actual market shares. This gap often arises from the omission of critical market dynamics and assumptions such as 100% awareness and distribution in our models. We propose an innovative approach that integrates the 4P marketing framework (Product, Price, Place, and Promotion) into the calibration process of conjoint analysis. This method offers a more holistic and accurate representation of market behavior, thereby enhancing the predictive validity of conjoint models.
A persistent challenge in conjoint analysis is the discrepancy between preference shares and actual market shares. This gap often arises from the omission of critical market dynamics and assumptions such as 100% awareness and distribution in our models. We propose an innovative approach that integrates the 4P marketing framework (Product, Price, Place, and Promotion) into the calibration process of conjoint analysis. This method offers a more holistic and accurate representation of market behavior, thereby enhancing the predictive validity of conjoint models.
SKU-Based Conjoint involves presenting respondents with numerous products ‘Stock Keeping Units (SKUs)’ at different price points. In contrast to typical conjoints, the product concept is fixed and refers to existing (or close to launch) products. This design is best suited for pricing research. However, the complexity arises from the number of SKU, leading to lengthy surveys that can overwhelm respondents. This can result in decreased data quality and unreliable insights. Therefore, we want to share our best practices for setting up designs (relevant set, number of SKUs) and analysing pricing effectively.
SKU-Based Conjoint involves presenting respondents with numerous products ‘Stock Keeping Units (SKUs)’ at different price points. In contrast to typical conjoints, the product concept is fixed and refers to existing (or close to launch) products. This design is best suited for pricing research. However, the complexity arises from the number of SKU, leading to lengthy surveys that can overwhelm respondents. This can result in decreased data quality and unreliable insights. Therefore, we want to share our best practices for setting up designs (relevant set, number of SKUs) and analysing pricing effectively.
Individual differences in scale usage make it hard to identify signal from noise in segmentation studies based on choice data. The Scale-Adjusted Latent Class model (SALC) makes it easier to assign the right respondent to the right segment. We will investigate the effectiveness of the SALC model by showing examples from both artificial and real-life datasets. The SALC model will be compared to other, more common segmentation algorithms, both from a statistical perspective and from a practical, market relevance point of view.
Individual differences in scale usage make it hard to identify signal from noise in segmentation studies based on choice data. The Scale-Adjusted Latent Class model (SALC) makes it easier to assign the right respondent to the right segment. We will investigate the effectiveness of the SALC model by showing examples from both artificial and real-life datasets. The SALC model will be compared to other, more common segmentation algorithms, both from a statistical perspective and from a practical, market relevance point of view.
