Identifying prospects has come a long way since the days of simple list buying. With the hype flying fast and furious, sometimes its difficult to sort out truth from fiction and to put things in the proper perspective. Without worshipping buzzwords, however, two of the most important developments in recent years have been the rise of statistics and databases. These have allowed companies to create more accurate profiles of their prospects, even giving rise to the term prospect modeling itself. Lets see how these two developments have helped shape prospect modeling.

Types

With the creation of commercial databases of Census data in the late 1970s, the first easily-available customer demographics were born. Using these statistics allowed companies a high-level view of customers and their behavior. Coupling this with statistical analysees of that data, updating the data (with between Census polls and surveys), and bringing in other sources (such as drivers license records, courthouse records, and so forth) allowed for the classification of people into certain population strata, generally called types. Single mothers might be a type; new homeowners might be another type; residents of cities with a population over one million people might be another type. These types are as specific or as general as the data allows, although it is difficult to narrow down to prospects of a particular business without purchase data. Still, these statistics allow the establishment of certain types of customers and potential customers, providing that first pass at bringing prospects into focus.

Specificity

Because companies themselves have purchase data, they can use it to correlate general demographic data to provide detailed information about their customers and potential new customers. Additionally, they can pull together relevant data measurements and discard irrelevant ones, thus sharpening their focus further. In a nutshell, having statistics allows you to determine trends and customers in the abstract, while adding purchase data to the mix allows you to determine characteristics of customers in the specific your customers and potential customers. Thus statistics allow for a great deal of specificity in prospect modeling.

Location (Geodemographic Statistics)

Yet another way that statistics can aid in modeling prospects is through geodemographic data; this data correlates Census and commercial data with geography, identifying where current customers and likely new customers reside, work, or even shop. This type of information may be more useful than it first appears. For instance, if you knew that likely customers lived in the Northeastern U.S, as opposed to the Midwest, you might be able to play off of regional identification to more effectively market your wares. Or if you knew that your customers shopped in large urban centers, but lived in the nearby suburbs, or dwelled primarily in zip code 32901, then you have more clues about them which can help you model new prospects even more efficiently. Why is that? New prospects have much in common with current customers, and the more you know your current customers, the more you will know your future customers.

Conclusion

Without the widespread use of databases and statistics, prospect modeling could not exist. These developments have created this technique and serve as its anchor points. Understanding what they do can allow you to craft more accurate customer profiles, which can in turn supercharge your prospect models, and then lead to even more sales.