The Power to Predict
By David Dorf-Oracle on Feb 23, 2009
When you call CapitalOne, they identify the caller (using CallerID) then determine the most likely reason for the call. For example, if you were just assessed a late fee, chances are you're calling to complain about the fee. This prepares the customer-service rep and helps speed the call along. And while you're on the phone, they determine which offers you're most likely to be interested in, so they may offer you some steak knives, which can be conveniently billed directly to your credit card. CapitalOne was a pioneer in applying real-time analytics to customer interactions back in the 1990s, and it continues to help them grow.
Today eHarmony tells us who to date, Netflix recommends movies to watch, and Pandora tells us what music we're likely to enjoy. Using contextual data (who you are, time of day, where you came from), history (your past interactions), plus some interesting algorithms (e.g. regression) can enhance customer interactions in real-time. Retailers like Amazon have been getting pretty good at this, like recommending past products you've browsed, or items related to past purchases. Imagine the possibilities if other sources of data were incorporated, like from social sites...
1. David arrived to Amazon from a Google search on digital cameras
2. David's brother Mike has a birthday coming up (from Facebook).
3. David only purchases things on sale.
4. Mike has a Canon digital camera on his wishlist.
==> Offer a high-end Canon digital camera on sale
Oracle has a strong track record with its Real-Time Decision product in several industries, including retail where its use in e-commerce seems to be gaining momentum. It provides three key capabilities:
Improve Business Responsiveness
+Optimize customer experiences with cross-channel real-time decisions at the point of interaction
Maximize Interaction Value
+Learn from each interaction and take the best action with embedded predictive analytics and rules
Enable Self-Adaptive Processes
+Integrate business intelligence into interaction processes with closed-loop predictive analytics automation
Combined with business intelligence and data mining, real-time decisions have the potential to streamline customer interactions and increase sales; both are welcomed in retail.