Company Context

Instagram is one of Meta’s largest consumer products, serving billions of users globally across feed, Stories, Reels, and messaging. Direct messaging is a critical engagement surface because it supports private sharing, relationship-building, and content distribution beyond public posting.

Problem

Instagram is preparing to launch a new direct messaging feature inside Instagram DMs. The product team believes the feature could increase private sharing and deepen retention, but there is disagreement on how to define success. Some leaders want to optimize for raw usage, while others worry that more messaging volume could come from spam, accidental sends, or low-quality interactions that hurt trust.

You are the data analyst embedded with the Instagram Messaging team. Your task is to define the right metric framework for this launch so the team can evaluate whether the feature creates meaningful user value, not just activity.

Assume the feature will launch first to a subset of Instagram users in a few major markets, with limited engineering bandwidth for instrumentation changes in the first release.

Deliverables

1. Define the product goal for this new Instagram DM feature and identify the primary user need it should serve.
2. Propose the key user segments you would evaluate separately and explain why segment-level analysis matters.
3. Define a north star metric, supporting metrics, and guardrail metrics for the launch.
4. Explain how you would distinguish healthy engagement from low-quality or harmful usage.
5. Recommend what success criteria would justify expanding the feature beyond the initial launch markets.

Constraints

• MVP launches within 6 weeks, so only a limited set of new events can be instrumented.
• The feature must work within existing Instagram DM infrastructure and ranking systems.
• Privacy, safety, and youth protections are high-priority constraints.
• The team needs a metric framework that can be understood by Product, Engineering, and Trust & Safety leadership.