Choosing a Primary Metric, Funnel Metrics and Guardrails to Judge a Product Launch
Company: Lightspeed
Role: Product Manager
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Other
A product has just launched. Which metrics would you use to decide whether the launch was a success, and how would you choose and organize them?
```hint Start from what the launch was for
The right metrics depend on what the launch was meant to change and over what window; a brand-new product and a feature added to an existing one call for different success signals.
```
### Clarifying Questions
- Is this a brand-new product, or a feature launched inside an existing product with an established user base?
- What was the launch meant to achieve: new users, deeper engagement from existing users, revenue, or a strategic position?
- Over what window should success be judged: launch week, the first quarter, or longer?
- Were success targets agreed before launch, and was the rollout staged or released to everyone at once?
### What a Strong Answer Covers
- Tying the metrics to the launch's goal and target users before naming any metric
- One primary success metric plus a small set of funnel metrics from reach through retention and revenue
- Guardrails for product health, cannibalization and harm to the rest of the product
- Phasing the read-out over time against targets set in advance
- Separating the launch's real effect from novelty, seasonality and launch marketing
### Follow-up Questions
- A month after launch, adoption beat the target but retention among adopters is well below plan. Is the launch a success, and what do you do next?
- The launch went to every user at once with no holdout. How would you estimate its effect?
- How would your metrics change for a launch in an enterprise product sold to companies rather than to individuals?
Overview: An analytics prompt about judging a product launch: which metrics show that it succeeded and how to organize them. It tests tying metrics to the launch goal, choosing a primary metric with funnel and guardrail metrics, timing the read-out, and separating the launch's real effect from noise.