Path to Product
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Path to Product

Machine Learning is a Hammer.

Is your product a nail?

  • A large inventory of stuff to recommend or respond to, with the ability to add more over time — Amazon or Netflix are made great largely by their massive libraries of items to offer you, and by how those libraries change over time — if they only had 5 options, machine learning which one you’d like most wouldn’t do them much good.
  • A use case where small, continuous amounts of incremental lift actually drive business value — Yes, ads fit this use case pretty well. You have to define critical actions or funnels and understand what you want to optimize for. Machine learning isn’t going to figure out your business goals for you.
  • A use case where false positives don’t create substantial harms — ML models are essentially running many tiny experiments, some of which will fail. Seeing an unappealing ad on Facebook is unlikely to make you delete your account, or stop chatting with your friends. But using ML to block registrations for your service, or to dole out discounts, can have substantial negative consequences without the right guardrails in place.
  • A use case that is relatively permanent — ML models need regular investment and maintenance, just like any other software. You’re unlikely to reap rewards over time if you’re treating ML as a “one and done” investment.
  • Limited resources that aren’t being spent on creating a new inventory of assets to recommend (i.e. there aren’t enough options, and the options aren’t different enough, for machine learning recommendations to do much good).
  • Poorly defined use cases that look good in headlines or marketing materials, but do very little to advance the company’s fundamental value or differentiation.
  • Use cases where the ramifications of false positives have not been evaluated
  • … And the need for maintenance over time has not been scoped.

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Emma Townley-Smith

Early-stage Head of Product. Love learning how people and products work.