Understanding the value of an individual customer over time will help companies direct these investments wisely, mitigating the risk of attracting the wrong mix of customers will all the new personalized offers.
There is a classic 90’s movie, Dumb and Dumber, where Jim Carey’s character (Lloyd) asks his love interest (Mary) about the chances of them ending up together:
Lloyd: What do you think the chances are for us to end up together? Mary: Not good. Lloyd: You mean, not good like one out of a hundred? Mary: I’d say more like one out of a million. Lloyd: So you’re telling me there’s a chance…YEAH!
Sometimes this is the sad situation that marketers face when using heavy analytical tools to gauge customer interest. Throw statistics at the problem and try to determine the interest of a consumer through a propensity score. What does a .25 propensity score mean and how is it calculated? How does a marketer use this information to improve the customer experience? What happens if decisions from this data don’t hit the mark?
“Black box” is a common phrase for technologies that attempt to help marketers through algorithms, models, and statistical analysis. The concept of black box technology has been around for a while and is defined in Wikipedia:
A black box is a device, object, or system whose inner workings are unknown; only the input, transfer, and output are known characteristics. In science and engineering, a black box is a device, system or object which can be viewed solely in terms of its input, output and transfer characteristics without any knowledge of its internal workings, that is, its implementation is “opaque” (black).
I like finding associations around. Usually there are much more connections between objects than you would notice from the first sight. The connections are particularly obvious when we look at the company as at some live entity.
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