Hailed by many as the key to inflating e-profit margins, data mining is regarded by many analysts as one the emerging technologies that will change the face of e-business and extend the usefulness of network-storage technologies.
Data-mining (sometimes known as content management) -- the use of statistical analysis to uncover hidden patterns in otherwise random information -- is an art that many e-tailers have yet to perfect. While delivering personalised web-content is central to content management, it's a convoluted craft that leads to many wacky assumptions.
"A lot of people think I'm just going to put this in the hands of the marketer and we'll get the secret sauce," said Bob Moran, a managing vice president at the Boston-based Aberdeen Group. Nonetheless, the art of data-mining is taken by many to be just the secret sauce that will spruce up an other-wise dull e-business offering and tempt customers into coming back for more.
Whilst mountains of recorded data on consumer behaviour are piling up, dozens of small data-mining companies are competing with the likes of Oracle and IBM to present the next and best data-mining solution. All these competitors are betting on the fact that there's bound to be a useful structure in all the heaps of consumer-behaviour data that e-tailers and their ilk are avidly collecting and storing.
Once subjected to real-time analysis a pattern for creating higher margins and inflating revenue is sure to emerge alongside a marketing slant that's bound to strike gold. Armed with the right data-mining application marketers can target customers with personalized stock quotes, news updates, special promotions and other information they are most likely to use, dramatically reducing advertising budgets and boosting revenue.
The biggest challenge for the emerging science of data mining, however, remains that ever-elusive quirk of human nature -- finding out what people really want.
At present data-mining concerns itself with the past-behaviour of customers (i.e. what people are likely to purchase based on previous transactions, demographic information and other data points). The real key to treasury, however, lies in figuring out what people would rather purchase, as opposed to what they merely settle for.
"You can figure out the behaviour of performance metrics, but what you're missing -the biggest piece of the puzzle - is what it is that people really want," states Kyle Johnstone, director of business intelligence for Emerald Solutions.
That, he muses, may be a mathematical impossibility.