What metrics would you use to evaluate a new Ads Manager feature for advertisers

Problem Statement Description

Product context: Meta is a social technology company; its products include Facebook, Instagram, WhatsApp, Messenger, Threads, Quest, creator tools, and ads.

Meta is evaluating a new feature in Ads Manager intended to help advertisers create, manage, optimize, or understand their campaigns more effectively. The feature may affect advertisers ranging from small businesses running simple campaigns to sophisticated performance marketers managing large budgets across Facebook, Instagram, Messenger, and other Meta surfaces.

Your task is to define how you would measure whether this new Ads Manager feature is successful. Focus on the advertiser workflow: discovering the feature, choosing to use it, applying it to campaigns, interpreting results, and deciding whether to keep using it. Consider both advertiser value and Meta’s ads ecosystem health.

A strong response should clarify what “success” means before selecting metrics, including who the feature is for, what advertiser problem it solves, and how its impact would show up in behavior, campaign outcomes, and business results. You should also account for measurement risks such as attribution, seasonality, advertiser heterogeneity, and short-term gains that could harm long-term trust.

The metric framework should consider:

- A clear north-star or primary success metric tied to advertiser value, not just feature clicks

- Adoption and activation metrics, including the denominator of eligible advertisers or campaigns

- Usage depth, repeat usage, and retention across advertiser segments, budgets, objectives, and maturity levels

- Campaign performance outcomes such as efficiency, delivery quality, conversions, or return on ad spend where applicable

- Meta business impact, including spend, revenue, budget growth, and advertiser retention

- Guardrail metrics for advertiser trust, user experience, ad quality, policy compliance, and ecosystem health

- Instrumentation needs, including event logging, experiment design, attribution windows, and cohort definitions

- How the metrics would support a launch, iteration, rollback, or scaling decision

The goal is to demonstrate how you would build a practical measurement plan for a new Meta Ads Manager feature that helps product, engineering, data science, and ads business teams understand whether the feature creates durable value for advertisers and for Meta.