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BLOG: Adapt or die: Analytics should drive your enterprise evolution

Rob F. Walker, Ph.D. | Nov. 15, 2013
Today, getting actively recalibrating intelligence out of your data, rather than depending on pre-scripted responses, is what gives businesses the competitive edge they need to survive

As humans have evolved throughout our existence, so has the way in which we work.  What has allowed our species to survive for millions of years?  It comes down to learning.  From learning the best methods for building a fire to learning the proper way to gather food, learning from mistakes and successes meant life over death. Our ability to learn processes, adopt them over time and then teach them to others has been key to our successful existence.

In contrast, business software, which operates in an environment that's changing much faster than the African savannahs, remains surprisingly static. Not only is software particularly hard to change (making 'change request' such a dreaded phrase), but those changes are bound to be manual. And now, Big Data not only pours into large organizations at unprecendented volume and velocity, but with an extreme variety in forms. At the same time business processes, amidst an Internet of Things, execute at the speed of light. To survive like humans did, companies needs to adapt. And for that, their software needs to learn. 

The evolution of business
Not surprisingly, as people have changed, the way that businesses interact with customers and respond to their needs must change in parallel. There is no cookie cutter example of the perfect customer or the perfect customer interaction. Whether it's via Twitter, Facebook or the age-old method of picking up the phone, customers want businesses to anticipate their needs before they can anticipate them.

Similar to how we've adapted as humans, computers can be taught that same sort of behavior. In business, we see this when we predict that based on a customer's profile they'll want a certain type of product.  When they choose something completely different, we're thrown off by their behavior. By adapting to changes in behavior, adaptive analytics can actually anticipate a customer's needs in a way that goes beyond the cookie cutter mold.

Businesses face a number of challenges, with losses among the primary concerns. Whether it's losing a customer or money, both are equally detrimental to a business.  We're becoming more and more accustomed to the growing rate of change that we don't think there are alternatives.

This is extremely hard on a business when we think about churn or attrition. The average cost of acquiring a customer, for instance, is upwards of $300 for an average telecommunications company. Multiplied by one million customers, the costs could be exorbitant. Retaining customers, at an individual cost commensurate with (future) value, is much cheaper. This requires predictive models that are bound to get quickly outdated with competitive offers, demographic changes, new regulations, or new available products. Is there any way for a modern business to keep up with the change?

 

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