Being the development and training partner of Sawtooth Software, we teamed up to give a proven three-day software training track in Rotterdam, The Netherlands on September 19–21, 2018.

This intensive three-day workshop provided attendees with practical solutions about conjoint/choice methodologies in order to improve their research or consulting practice. This workshop covered three current and most popular choice research methodologies:

  • CBC (Choice-Based Conjoint),
  • MaxDiff (Maximum Difference Scaling)
  • ACBC (Adaptive CBC)

As the workshop is hand-on practical training, they had the chance to create choice surveys yourself and analyze sample data using a team-oriented case study approach.

These informative sessions were taught by senior staff of both SKIM and Sawtooth Software. In the practical sessions, participants worked hands-on with the Sawtooth Software’s Lighthouse Studio (formerly known as SSI Web) platform.

Agenda of SKIM/Sawtooth Software Choice Modeling Workshop 2018

Day 1 of 3: Introduction to CBC

This session introduces discrete choice (CBC) analysis through interactive, hands-on training. Below are some of the topics covered the first day:

  • Conjoint methodology: overview, concepts, and objectives
  • Formulating attributes and levels
  • Designing conjoint experiments
  • Best practices / common mistakes related to CBC
  • Choosing among the four experimental design strategies: Complete Enumeration, Shortcut, Balanced Overlap, and Random
  • Prohibitions: are they universally bad? Testing the impact of modest to severe prohibitions
  • Design Testing: Quick Test vs. more advanced test using simulated respondents. How the advanced test can help with sample size decisions

Day 2 of 3: Intermediate CBC

This session builds upon the concepts learned in the introductory part of the training. You will receive practical experience while creating surveys in Sawtooth Software’s Lighthouse Studio system and analyzing the results. We will go beyond the basics of CBC and will cover:

  • Analyzing CBC data using Counts, Logit, Latent Class, and hierarchical Bayes (HB)
  • Using market simulators to estimate preference for competitive products in market scenarios, including price sensitivity
  • Conditional Pricing/Display: customizing price ranges and concept display without the use of prohibitions
  • Introduction to Alternative-Specific Designs

Day 3 of 3: MaxDiff and Adaptive CBC

This session covers two of our most popular survey strategies: MaxDiff (best/worst item scaling) and Adaptive CBC. Topic to be covered:

  • Designing, programming, and analyzing MaxDiff experiments
  • MaxDiff Analyzer tool and TURF analysis
  • Benefits and motivation for Adaptive CBC (ACBC)
  • Designing, programming, and analyzing ACBC studies
  • When to use non-adaptive CBC and Adaptive CBC (ACBC)