Have an idea?

Visit Sawtooth Software Feedback to share your ideas on how we can improve our products.

Simulating One (1) Product vs. "None"

I'm interested in your thoughts regarding running simulations about a single product.  There are times when simulating for a single product rather than multiple products or a competitive scenario makes more sense.  Perhaps a true competitor cannot be defined, or this is a new product category so there are no competitors, or maybe the category is so heavily populated that there are many competitors and the interest just lies on general product preference, etc.  Is there a right or more appropriate way to do this?  

One way could simply be to simulate a single product vs. "none" and get shares (in SMRT via first choice, share of preference, or RFC).  Another could be via purchase likelihood in SMRT (without calibration) which, as I understand it, would then simply be a general measure of preference.  Or perhaps it is better to force a competitive scenario where you include some fixed "competitor" in all simulations that perhaps is a middle-of-the-road product based on average utilities.  This competition seems more natural and well-suited to conjoint simulations, since that is what's shown on screen to respondents in a CBC exercise.  But at the same time the fixed product is just generic.  

So if there are any schools of thought on this topic I'd love to hear them or be pointed in the right direction to find them.  Thank you!
asked Jan 20, 2015 by Westley Ritz

1 Answer

0 votes
All these methods you speak of involve an exponential transformation, so the resulting scaling is more like a probability scale (rather than the original scaling of the utilities, where the utilities are interval-scaled and can have positive and negative values).

The "Purchase Likelihood" transformation for each respondent would just be e^u1/(e^u1+e^0), where “u1” is the total utility of the product concept.  Since we’re often working with zero-centered utilities, this means that the purchase likelihood simulation method reflects the likelihood of picking this product concept from a set including this product concept plus one other product of average utility.  (Back in the days of ACA and CVA, the utilities could be given scaling based on respondents’ stated purchase intent for specific product concepts on a 100-point scale, so the resulting exponential transformation indeed was a least-squares fit to respondents’ stated purchase intent for product concepts.)

Simulating a single product vs. the None would be e^u1/(e^u1+e^UNone), where UNone is the utility of the None concept.  Since the None concept utility differs per respondent, those respondents who pick the None a lot would have their shares of preference influenced more toward 0%.  Thus, people who pick None almost exclusively have their shares for product 1 driven mostly to zero.  And, respondents who never picked the none would have their shares weighted up consistently toward 100%.  So, the sensitivity from such respondents for discriminating between products of different quality would be diminished.  

Even better, as you mention, would be to simulate a product’s share of preference relative to a realistic set of competitors, as seen in the market place.

In all these cases, we’re making the results more intuitive for managers and others to understand results we want to present to them.  The values are all positive, with quasi-probability scaling.
answered Jan 20, 2015 by Bryan Orme Platinum Sawtooth Software, Inc. (128,265 points)