Track Success and Guardrail Metrics for Push Notifications

Q: Track Success and Guardrail Metrics for Push Notifications

This question evaluates a data scientist's competency in experimental design, metric selection, and causal inference for networked mobile products, focusing on defining primary success and negative-impact guardrail metrics for a push-notification feature.

Q: How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Question

Push-Notification Metrics and Network-Aware Experiment Design

Scenario

You are designing and evaluating a new push-notification feature for a TripAdvisor-like mobile app where users can create and share itineraries. Because users influence one another through shares and engagement, the product exhibits strong network effects.

Question

What primary success metrics would you track for the push-notification feature?
Which guardrail (negative-impact) metrics would you include (e.g., uninstalls, unsubscribes)?
Given the app’s network effects, how would you design an A/B test for the notification to mitigate interference/spillovers? Describe the unit of randomization and explain why.

Hints: Think engagement, retention, negative-impact guardrails, and cluster-based randomization to reduce spillover.

Track Success and Guardrail Metrics for Push Notifications

Push-Notification Metrics and Network-Aware Experiment Design

Scenario

Question

Solution

Comments (0)

Track Success and Guardrail Metrics for Push Notifications

Overview

Push-Notification Metrics and Network-Aware Experiment Design

Scenario

Question

Solution

Comments (0)