Template:M intro design System redundancy: Difference between revisions

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→‎Pareto triage

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 We should, by now, feel like we are in a new and better world — right? — yet the customer experience as poor as ever. Not ''worse'', necessarily: just no ''better''. Just try getting hold of a bank manager now. “BAU-as-a-service” has streamlined and enhanced the great heft what businesses do, at the cost of depersonalising service and eliminating outlying opportunities for which the model says there is no business case.
-====Pareto triage====
+You might call this effect “[[Pareto triage]]”. Great for those within a [[Normal distribution|standard deviation]] of the mean, who are happy with the average. But it poorly serves the long tail of oddities and opportunities. Those just beyond that “[[Pareto triage|Pareto threshold]]” have little choice but to manage their expectations and take a marginally unsatisfactory experience as the best they are likely to get. Customers subordinate their own priorities to the preferences of the model. But, unless you are McDonald’s, the proposition that 80% of your customers ''want'' exactly the same thing — as opposed to just being prepared to put up with it, in the absence of a better alternative — is a kind of wishful [[averagarianism]].
-We call this effect “[[Pareto triage]]”. Great, for the huddled masses who just want the normal thing. But it poorly serves the long tail of oddities and opportunities. Those just beyond that “[[Pareto triage|Pareto threshold]]” have little choice but to manage their expectations and take a marginally unsatisfactory experience as the best they are likely to get. Customers subordinate their own priorities to the preferences of the model. This is a poor business outcome. And, unless you are McDonald’s, the idea that 80% of your customers ''want'' exactly the same thing — as opposed to being prepared to put up with it, in the absence of a better alternative — is a kind of wishful [[averagarianism]].
 ====The Moneyball effect: experts are bogus====
 It gets worse for the poor old [[subject matter expert]]s. Even though, inevitably, one has less than perfect information, extrapolations, mathematical derivations and [[Large language model|algorithmic pattern matches]] from a large but finite data set will, it is ''deduced'' — have better predictive value than the gut feel of “[[ineffable]] [[expert]]ise”.