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Choice-Based Conjoint with multiple purchases (volumetric CBC)

I want to conduct a Choice-Based Conjoint Analysis for packaged goods that not only models one-time puchases but also predicts market shares with repeat purchases (volumetric analysis).

Is there a way to do so with Sawtooth software? There seems to exist an extension for this purspose (available at http://www.sawtoothsoftware.com/support/downloads/tools-scripts) Does anyone have experience with it?

Thanks a lot!
asked May 31, 2015 by Ulf

1 Answer

0 votes
This topic was covered well in 2010 by Tom Eagle at the 2010 Sawtooth Software Conference.  You can read his paper within the following volume:

http://www.sawtoothsoftware.com/support/technical-papers/conference-proceedings/proceedings2010

The simple one-step approach as done by the “tools/scripts” tool you can find on our website appears to work reasonably well for standard types of CBC datasets.  But, Tom was able to create some more complex data sets where his two-stage joint discrete/continuous models performed better.
The key issue in all of this is whether respondents can give you good volumetric CBC data.  Many researchers with deep experience with this say that respondents often cannot provide very good volumetric data, so there is a healthy deal of skepticism surrounding the volumetric CBC approach.
answered Jun 1, 2015 by Bryan Orme Platinum Sawtooth Software, Inc. (144,040 points)
Thanks for your reply. I read Toms paper and did some additional research on my own. As you already said, asking respondents to fill in the "purchase quantity" doesn’t seem a very valid and realistic way of obtaining data. Do you know if there are any other methods, where I can infer indirectly the purchase volume instead of asking respondents something they most likely will not be able to answer?
Well, if you are willing to assume steady state volume (no increase in volume for the category), then the usual approach is to ask respondents to indicate their typical daily or weekly or monthly or yearly volume (whichever makes most sense for your study).  Then, you weight the share of preference simulation results by the respondents' volume (individual weighting factor per respondent).  Our market simulators allow you to put in a weighting variable.
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