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Goals, North-Star Metric and Guardrails for Scam Protection Across Meta's Apps

MetaProduct Manager · Other
MediumProduct ManagerOtherAnalytics & Experimentation

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A metrics question asking a product manager to set goals and success metrics for a scam-prevention product across Meta's apps. It tests goal prioritization, the choice and measurement of a north-star metric, leading indicators by scam type and surface, guardrails against over-enforcement, and measurement under adversarial behavior.

Imagine you are the product manager for a scam-prevention product at Meta, built to protect people from scams across its apps. Define the goals for this product and the metrics you would use to tell whether it is succeeding.

Clarifying Questions

  • Which surfaces and scam types are in scope: Marketplace buying and selling, messages from strangers on Messenger, WhatsApp or Instagram, scam ads, or impersonation of people and brands?
  • Is the product the detection and enforcement system behind the scenes, the user-facing protections such as warnings and reporting, or both?
  • Should the product protect everyone equally, or put extra weight on higher-risk groups such as older adults, teens and new users?
  • Is this a new product being launched, or an existing one whose goals and metrics need to be reset?

Hints

Hint 1

Ask what the counts really tell you: Before picking a headline number, ask whether it would go up when scams get worse, when detection gets better, or both, and what legitimate users could lose if the product becomes too aggressive.

Follow-up questions

  • Sampled scam prevalence is flat while victim reports rise sharply in one country. How would you investigate?
  • How would you show that a new warning on messages from unknown accounts caused a drop in scams, rather than seasonality or scammers moving elsewhere?
  • Leadership wants one target for next year. Which metric would you commit to, and what kind of target would you set?