Real time analytics allows to track and monitor users and their activities; and then adjust what is presented to them. It could be a relevant advertisement like showing Nikon camera advertisement for a user searching for a new camera or camera prices or anything similar.
Taking one step further is when the price of an item(s) is adjusted depending on whether that user is a loyal customer and/or there is higher possibility that he/she may buy other accessories. Realtime predictive analytics makes this possible.
Below is an nice graph presented by Wall Street Journal and at http://www.ritholtz.com/blog/2012/09/lucky-us-toilet-paper-priced-like-airline-tickets/
Graph shows three companies’ (Sears, BestBuy and Amazon) price variation over a day for a microwave. Amazon increased prices during the peak hours by more than 10% (~8am to 12.30pm and then again 3pm to ~9pm EDT). All times shown in graph are in PDT (Pacific Day Time) timezone.
Even more interesting will be observe whether prices were varied based on user location or where Amazon’s servers were located? As it is simple to geo map the IP address of a user computer/device and vary the prices accordingly! Different users from different cities at different times will see different prices. The price points and user experience can be optimized for improved sales!