Analytics & Experimentation Strategy to Improve Business Outcomes

Context

Assume you are a software engineer interviewing for a role focused on analytics and experimentation. The product is a large-scale software platform (web + mobile) with free and paid tiers. The business cares about user growth, engagement, reliability, and subscription revenue.

Task

Propose research directions and solution approaches to measurably improve business outcomes.

Requirements

1. Objectives and Metrics
   • Define a clear Objective and a small set of Key Metrics (including guardrails). Explain why these matter and how they ladder to business outcomes.
2. Hypothesis Generation and Prioritization
   • Describe how you would generate hypotheses (e.g., from data, user research, logs) and how you would prioritize them (e.g., RICE/ICE, expected value).
3. Experiments and Pilots
   • Explain experiment/pilot designs (A/B, cluster/geo, switchback), sample sizing/power/MDE, ramp plans, instrumentation, and success criteria.
   • Show how you would estimate impact and cost before running.
4. Phased Plan
   • Outline a 3–6 month plan with phases, deliverables, owners, and decision checkpoints.
5. Data Requirements
   • List the minimal data and telemetry needed to support analysis and decisions.
6. Risks and Mitigations
   • Identify major risks (statistical, product, operational, ethical) and how to mitigate them.
7. Success Criteria
   • Define what success looks like for both business outcomes and experimentation capability.