Track Metrics to Measure Push Notification Quality
Quick Overview
Track Metrics to Measure Push Notification Quality evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Track Metrics to Measure Push Notification Quality
Company: Meta
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Onsite
##### Scenario
Evaluating push-notification quality for a consumer app
##### Question
Which metrics would you track to measure the quality of push notifications? How would you define and set thresholds for “high-quality” notifications? Design an experiment to evaluate a new push-notification algorithm at launch.
##### Hints
Think engagement, retention, opt-outs, control-vs-treatment setup, sample size.
Quick Answer: Track Metrics to Measure Push Notification Quality evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Track Metrics to Measure Push Notification Quality
Scenario
A consumer mobile app sends push notifications to drive user engagement. You need to evaluate the quality of these notifications, define what constitutes “high-quality,” and design a rigorous experiment to validate a new notification-selection algorithm at launch.
Assumptions (minimal):
You can randomize at the user level and collect event-level telemetry (send, delivered, opened/tapped, session starts, conversions, opt-outs, uninstalls).
Platforms include iOS and Android; weekly seasonality exists.
Questions
Metrics: Which metrics would you track to measure push-notification quality? Include engagement, retention, and opt-out signals.
Thresholds: How would you define and set thresholds for “high-quality” notifications?
Experiment: Design an experiment to evaluate a new push-notification algorithm at launch. Specify units of randomization, key metrics and guardrails, sample size/power, duration, and rollout safeguards.
Hints: Consider engagement, retention, opt-outs, control vs. treatment setup, and sample size.
Constraints & Assumptions
Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.
Clarifying Questions to Ask Guidance
Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
State assumptions about instrumentation, randomization, sample size, and data quality.
Separate descriptive analysis from causal claims.
What a Strong Answer Covers Guidance
A metric framework with primary, guardrail, and diagnostic metrics.
A credible analysis or experiment design with clear assumptions and bias checks.
SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
An actionable recommendation that explains trade-offs and next steps.
Follow-up Questions Guidance
What sanity checks would you run before trusting the result?
How would you handle novelty effects, seasonality, or selection bias?