Tekram research is to market research as vendors is to consumers. Market research is what they do to us. Tekram research is what we do to them. Think of PubWan as the little people's purchasing department.
Simple tekram research consists of accumulating open source databases of observed instances of products for sale, the places they are seen, and the prices asked. Functionally, this is essentially a "shop bot." The difference is that pubwan data are nonproprietary, and pubwan is not a for-profit entity.
A much more sophisticated form of tekram research is needed when vendors propagate disinformation in the form of package dealing and other nonlinear pricing schemes. A data modeling schema for this type of tekram research is roughly outlined at http://geocities.com/n8chz/pvo.htm.
The least transparent type of consumer market uses contingency pricing. This is when a consumer has to give information to get information; specifically a price quote. An example of market opacity through contingency is the consumer insurance market.
A tekram research strategy for insurance would be very challenging to implement, but is probably a very worthwhile goal. The point of sale data provided by the PubwanParticipant would have to include sensitive information about the participant... information with demographic implications such as zip codes and credit scores. This is why pubwan must be implemented with some data NOT queryable. This is a serious compromise of the ideals that inspired pubwan, but it is believed that privacy and transparency can both be addressed.
Insurers are now allowed to set premiums based in part on credit scores. As is to be expected, the premium is inversely proportional to the ability to pay. For this reason, the MaxhiSchema for plotting efficiency frontiers should minimize credit score. This reflects the mathematical tradeoff between low credit scores and low premiums.