I spent a very long time on Amazon and Staples.com recently trying to find a replacement for the little hand-held gadget that I use to clip crossword puzzles out of my newspapers. Typing “Paper cutter” in the search box didn’t get me there. And neither did “Scissors”. I eventually gave up. It wasn’t until I saw it in a good old-fashioned mail-order catalog that I learned it is called a “Gift wrap cutter.” And so I was finally able to go back online and order one.

The first is never assume that the way you refer to something is universally understood.

The second is that potential customers may not consider themselves as being in the market for a given product, even if they are. 

• • • •  Number of ads or sponsorships per game
    • •  Where the ads appear (between rounds, upon game entry) 
    • •  Prize pool
    • •  Having sponsor-related questions
    • •  Getting bonus ‘lives’ for watching sponsor videos

You may have heard about the spat between Apple and Samsung. Apple is suing Samsung for alleged patent infringements that relate to features of the iphone and ipad. The damages claimed by Apple? North of 2 billion dollars. The obvious question is how Apple came up with those numbers? The non-obvious answer is, partly by using conjoint analysis – the tried and tested approach we often use for product development work at TRC.    

Apple hired John Hauser, Professor of Marketing at MIT’s Sloan School of Management to conduct the research. Prof. Hauser is a very well known expert in the area of product management. He has mentored and coauthored several conjoint related articles with my colleague Olivier Toubia at Columbia University. For this case, Prof. Hauser conducted two online studies (n=507 for phones and n=459 for tablets) to establish that consumers indeed valued the features that Apple was arguing about. Details about the conjoint studies are hard to get, but it appears that he has used Sawtooth Software (which we use at TRC) and used the advanced statistical estimation procedure known as Hierarchical Bayes (HB) (which we also use at TRC) to get the best possible results. It also appears that he may have run a conjoint with seven features, incorporating graphical representations to enhance respondent understanding.

There are several lessons to be learnt here for those interested in conducting a conjoint study. First, conjoint sample sizes do not have to be huge. I suspect they are larger than absolutely necessary here because the studies are being used in litigation. Second, he has wisely confined the studies to just seven attributes. We repeatedly recommend to clients that conjoint studies should not be overloaded with attributes. Conjoint tasks can be taxing for survey respondents, and the more difficult they are, the less attention will be paid. Third, he is using HB estimation to obtain preferences at the individual level, which is the state of the science approach. Last, he is incorporating graphics wherever possible to ensure that respondents clearly understand the features. When designing conjoint studies it is good to take these (and other) lessons into consideration to ensure that we get robust results.

So, what was the outcome?

As a result of the conjoint study, Prof. Hauser was able to determine that consumers would be willing to spend an additional $32 to $102 for features like sliding to unlock, universal search and automatic word correction. Under cross examination he acknowledged that this was stated preference in a survey and not necessarily what Apple could charge in a competitive marketplace. This is another point that we often make to clients both in conjoint and other contexts. There is a big difference between answering a survey and actual real world behavior (where several other factors come into play). While survey results (including conjoint) can be very good comparatively, they may not be especially good absolutely. Apple used the help of another MIT trained economist to bring in outside information and finally ended up with a damage estimate of slightly more than $2 billion.