The FICO score -- that numeric summation of our credit histories -- continues to find new and novel applications. Most recently, the healthcare industry is using it as a predictor of whether or not patients will take their prescriptions. Whether or not you will take your pills twice a day as instructed, called both adherence and compliance in the world of healthcare, is actually a big issue. (Check out how 2010 Model of Excellence Nominee HealthPrize is making a business out of this). If you don't take your medicine as instructed, you ultimately end up back at the doctor or in the hospital, pushing up healthcare costs for everyone. There are lots of people who for one reason or another don't take their medicine, and with doctors spending ever-less time with patients, it's harder and harder to identify exactly who would most benefit from some follow-up calls and encouragement. Enter the FICO Medical Adherence Score.


This new product is interesting in its own right, but it comes on top of numerous other applications for FICO scores and credit data. Many employers now inspect the credit of potential hires, as sort of a proxy for reliability and character. FICO scores are used by auto insurance companies to set rates (apparently, the higher your credit score, the lower your propensity to sue -- who knew?).


There are many, many examples of this type of data application -- we at Infocommerce Group call it inferential data -- where data point X can be used to infer something totally unrelated. Almost all of this is occurring with consumer data, not business data.

Yes, there is some inferential data in the B2B world. Sometimes called "trigger events," some data publishers understand, for example, that a company that just moved to a new office is an excellent candidate for office supplies. But B2B inferential data is still in its early stages. And that got me musing.


Inferential data is all about trying to discern something you don't know from something you do know. Companies with multi-state operations need more sophisticated accounting, HR and payroll support services. Companies that lease office space disproportionate to their number of employees are likely planning for near-term growth. Companies operating from residential addresses tend to be smaller and have a distinct set of business needs. Companies that get taken to court a lot in contract disputes are probably not great prospects.


It's likely even possible to assign companies to behavioral buckets. Are companies in high-rent districts free-spenders? Should companies with press releases touting many joint venture and partnership deals be sold differently from other companies? Are companies that constantly recruit for the same job titles revolving doors? Do family-owned companies (often spotted by the same surname in multiple executive positions) do business differently, and do they need a distinctive sales pitch?


If a lot of this sounds like business intelligence, it is. But there are types of business intelligence that can be divined on an automated basis by data publishers if they assemble the right data, and understand its inferential value. And by identifying and packaging some of these inferential indicators for our customers, we simultaneously build revenue, value and customer annuity. We are at the earliest stages in the development of powerful and useful B2B inferential data. Now's the time to start staking your claim.