How Conjoint Handles Really Complicated Ideas: Adventures in Advanced Choice-Based Conjoint Applications

This article includes some highlights from a workshop at the Sawtooth Software conference.  Presented by Bryan Orme, President of Sawtooth Software and David Lyon, Principal at Aurora Market Modeling, LLC, this workshop covered lots of different advanced applications of Choice-Based Conjoint (Discrete Choice Modeling).

Here are just a few highlights from this four-hour workshop:

  • Alternative-Specific Designs give the flexibility to show unique sets of attributes based on the context (e.g., if asking about choices for short-range travel, when “drive my car” is shown, “parking fee $8.00” is also shown. However, when “ride the bus” is shown, “picks up every 20 min.” and “75 cents per one-way trip” are shown).  These designs make the exercise more realistic.
  • Prohibitions (restricting attribute levels that can be shown in combination) can be a red flag for the way you have set up your attributes and levels. General rule: if the respondent will be confused by a combination, use a prohibition; however, if it’s simply “we don’t offer this combination”, then you don’t have to use a prohibition.  Remember that prohibitions reduce the efficiency of your design and you’d need more sample to overcome this issue.
  • Conditional Pricing (varying the price points shown conditioned on the presence of a level of a certain attribute) only makes sense if levels of price are in some sense consistent across conditions: typically the same $ difference or same % difference).
  • Hierarchical Bayes is general the best analysis method for these scenarios – certainly better than Aggregate Logit – and this is true for even basic CBC analyses.
  • Summed Pricing extends the idea of conditional pricing and attaches incremental price changes to each level of each attribute. It’s important to add +/- X% to summed prices so that price is not confounded with levels.  Doing so helps partial out the effects of each level; so that the utilities are not “conditional” on the price.  +/- 30% was suggested as the random variation to be applied.
  • Bundling can get complicated, but the idea is to create a custom design that mimics the purchase process as much as possible. Modeling and simulation in this case is custom.
  • Volumetric CBC asks respondents to declare a “quantity” of each choice that they would buy, versus making a “discrete” choice as to which one they would buy. Volumetric CBC appears better at revealing near-term spikes in market share, whereas, discrete choice is better at showing a long-term equilibrium for a product (it is more stable over time).
  • “Evoked Set” CBC takes a reduced set of attribute levels forward into the task (e.g., start with 14 levels, but respondents will only answer about 7). Takes some fancy data-processing work.
  • Build-Your-Own (BYO) is task where respondents see all attributes and choose which level of each that they prefer. BYO can be a good “training task” for respondents, especially if they are going into a complex choice task.  BYO is built into Sawtooth Software’s Adaptive Choice-Based Conjoint.

Menu-Based Choice (MBC) is one of the newest additions to the conjoint family.  Design is very customized and respondents have an opportunity to choose various items from a “menu” to build their preferred product.  Counts analysis can work, but Hierarchical Bayes (HB) analyses form the basis of models being developed to manage this type of data.  However, some research suggests that HB doesn’t give as much lift over Aggregate Logit in menu-based choice as it does in regular CBC.