Apple v Samsung – The Patent Trial of the Century
It’s pretty exciting for those of us in quantitative marketing research when we see techniques that we use regularly highlighted in a major patent trial. That’s just what happened as the details of Apple v Samsung became public.
The gory details aside, the essence is that Apple’s expert witness (John Hauser from MIT) used Sawtooth Software (the same software we use for all our conjoint analysis projects) to estimate “willingness to pay”. Hauser showed “that Samsung’s customers are willing to pay between $90 and $100 above the base price of a $199 smartphone and a $499 tablet, respectively, to obtain the patented features covered by Apple’s utility patents”. A second expert then conducted supply-side analysis to arrive at final damage amounts.
How Can We Use This Method to Meet Regular Research Objectives?
The first thing to note is that you likely won’t be working with a second expert to do the supply-side analysis for you – you’ll be working with a research company and you’ll simply want answers to questions such as:
- How much more can I charge for my product if I add feature X?
- What is the incremental value of improving feature Y from its baseline level today to an improved level?
- Which of these X possible feature improvements will result in the greatest ability to charge more for my product, and how much?
The good news is that there is a relatively straightforward way to answer these questions. Here’s how:
- Conduct a conjoint analysis exercise with your target population and include all relevant attributes and levels so that you can model existing products and proposed enhancements
- Make sure that you are testing a range of attributes and levels so that you can model both your product(s) and your key competitors’ products
- Once your market simulator is built, set up a competitive set against which you will model your product
- Place your product at its baseline configuration in this competitive set and estimate share of preference
- Improve your product by adding the feature of interest (this is the feature for which we want to understand the monetary value) and run this product against the same competitive set
- You will see share of preference for your product increase (you just improved it, so this makes sense)
- Raise the price of your improved product until the share of preference returns from its elevated level back to its baseline level
- This difference in price is the incremental value of the feature
There are some fine points to making the above work well, however, the essence is that all companies can have access to these high-end techniques for estimating the value of product features. If you’re interested in learning how this method could help your company make better business decisions, email us at email@example.com or call 877-672-8100 and we’d be happy to discuss your specific situation.