Over the past 25 years, Wall Street has increasingly turned to market research tools to improve the accuracy of fundamental research used to predict future company and industry conditions. From marketplace surveys/channel checks originated by RCM’s Grassroots Research division in the 1980s, Gerson Lehrman’s use of expert networks as focus groups, to the data mining techniques of FirstRain’s and Windows SharePoint Services’ Web-based alerts, market research tools are an accepted commodity on Wall Street today. In fact, these tools have become so commonplace that they no longer provide much of a competitive advantage.

It is in this current environment that Blueshift Research has turned to the academic principles of market research techniques to give its clients an edge and to increase their likelihood of making the right decisions. This process, called pattern mining, uses three main principles to create a new approach in the market research world.

First, pattern mining uses the tools of all the above research techniques and then overlays them with the principles of the late Nobel Prize winner Herbert Simon’s artificial intelligence approach to sorting information. According to Wikipedia, Simon said “becoming an expert required about 10 years of experience, and he and his colleagues estimated that expertise was the result of learning roughly 50,000 chunks of information.” This overlay of using such experts to sort through relevant primary and secondary information on related company and industry topics is one of the three major premises on which Blueshift Research is created.

The second major principle is that independent unique data points stating the same thing are much more important than quantity of data as a whole in predicting what might happen. In most market research techniques – whether key word searches on the Web or surveys of respondents regarding company issues – the higher the quantity of data, the higher the quality. Blueshift turns this theory around, stating, “More important than the quantity of similar answers is the independence of where the answers are coming from.” Or simply put, totally independent data sources or data points not independent of each other.

The third and final principle on which Blueshift Research is based is that a set approach of creating and actively seeking data from independent data sources will yield much more unique information than a structured approach on the Web or a survey/primary expert network project. Using an expert to seek out this data and independent sources can reveal more unique ideas with potential investment implication than creating and combing through data from Web searches, surveys or expert network interviews. If you are looking for needles in a haystack, a structured approach rather than a random search will improve the probability of finding them.

Blueshift Research uses this proprietary pattern mining process in three ways to increase clients’ chances of finding unique, investable information:

First, to uncover exclusive ideas or updates that, with additional work, could lead to investable actions;

Second, to confirm these ideas with enough unique sources to have a very high probablity of correctness and relevance;

And, third, to take our clients’ investment ideas and perform proprietary work with independent data points to determine the research’s accuracy based on a variety of independent sources.