This seems to be a close analogue of something I’ve seen in business communications settings: customer segmentation. In that context, I had the same reaction to it as you’re expressing on an individual interpersonal basis: it seems better to make predictions about the individual directly, rather than binning people into segments and then making predictions about the segments.
If you have a bunch of data about a bunch of (prospective?) customers, can your algorithms perform better (say in terms of identifying people’s preferred means of contact) by predicting for each individual customer, rather than going via some pencil-sketched segment and then declaring that for such-and-such a segment you’re best off communicating with them via email because that’s what that segment as a whole prefers?
Predicting on an individual basis would be more accurate, since it’s much more likely to catch edge cases and follow preference changes over time. At the same time, it’s more expensive and the price scales with the size of your customer base, unlike categorization. That’s probably why most businesses don’t practice it. Social media algorithms use both, maybe because technologies like neural networks push the price down.
This seems to be a close analogue of something I’ve seen in business communications settings: customer segmentation. In that context, I had the same reaction to it as you’re expressing on an individual interpersonal basis: it seems better to make predictions about the individual directly, rather than binning people into segments and then making predictions about the segments.
If you have a bunch of data about a bunch of (prospective?) customers, can your algorithms perform better (say in terms of identifying people’s preferred means of contact) by predicting for each individual customer, rather than going via some pencil-sketched segment and then declaring that for such-and-such a segment you’re best off communicating with them via email because that’s what that segment as a whole prefers?
Predicting on an individual basis would be more accurate, since it’s much more likely to catch edge cases and follow preference changes over time. At the same time, it’s more expensive and the price scales with the size of your customer base, unlike categorization. That’s probably why most businesses don’t practice it. Social media algorithms use both, maybe because technologies like neural networks push the price down.