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We investigated and diagnosed the product as a feature shock: It had 27 features, many that small businesses did not care about. We told them to whittle the product down to eight features and then increase the price. It worked beyond the firm's wildest dreams, boosting the sales conversion rate and revenue more than 25 percent.
This sounds counterintuitive: Fewer features create more demand? But it was true in this case. Piling too many features into the product was killing demand by hiding the features that truly mattered.
Monetizing Innovation
Madhavan Ramanujam, Georg Tacke
But when it comes to setting up things that have to be perfect, machine learning just isn’t the way to do it—much as humans aren’t either.
ChatGPT Learns Computing
Ben Thompson
Come succede spesso, il punto critico di ogni unbundling, di ogni sotto-segmentazione, è «quanti» (sono interessati al prodotto verticale) e «quanto» (sono disposti a pagare) per avere un prodotto specifico anziché il generalista, e «in quanto» (tempo) il suo competitor generalista cercherà di ucciderlo.
[È Venerdì] L’AI Come Motore Di Ricerca Ha Una Chance?
Gianluca Diegoli
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