Apr 22, 2019

Imagine feeding known data into a black box and then analyzing the output. When you didn’t get what you wanted, you simply adjusted a few knobs on the box and fed it a different data set and monitored the output again. You would repeat this process of adjusting the knobs on the box until the black box gave you the output you expected. This is what is called ‘supervised learning’ in the world of Machine Learning (ML) a subset of Artificial Intelligence (AI). 

Once the box did give the expected outputs, it is considered ‘trained’. This finely tuned black box can now be used to predict the output of other data sets.

In sales, imagine feeding a similar black box information on past customer purchases. After a few iterations and fine tuning of the knobs, the black box would be able to ‘predict’ who is more likely to buy (i.e., customer personas) and what are they most likely to buy (i.e., upsell or...


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