The idea of performing analysis on smaller and smaller population to better understand how they will react to stimulus based on previous observations has only recently been made possible by increasingly robust computing power and increasingly inexpensive memory.
These goals, particularly when they support improving customer experience, translate into personalizing (or even individualizing) an offer for a product or service. Personalization involves increasing the speed, accuracy and context of interaction. For example, imagine a father who receives a stellar offer for a pair of noise-cancelling headphones on his mobile device shortly after purchasing an electric guitar and amplifier. That’s an offer that has the right speed (right after purchase), accuracy (mobile as his preferred channel) and context (knowing he may need the headphones to silence the noise coming from his teenage son’s room). This type of offer increases customer satisfaction as it is pertinent at the time of need. The end-result is a happier customer (this company understands me) and a happier brand (increased revenues.)
In the case of many financial institution and large scale merchants having these tools is nothing new, but it is the stand out organizations that we see employing them with the greatest effect, and it is usually those organizations that have the use of analytics at their core that are doing so. Amazon spring immediately to mind in this instance. It remains to be seen if the lead the early movers gained will be enough to stay head and shoulders above the fast learning incumbants.
Overview by Joseph Walent, Associate Director, Customer Interactions Advisory Service at Mercator Advisory Group
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