Design an experimentation dashboard for Premium

Problem Statement Description

Product context: LinkedIn is Microsoft's professional network; its products include profiles, feed, jobs, recruiting, LinkedIn Learning, sales tools, messaging, and ads.

LinkedIn Premium runs experiments that may affect how learning-oriented members discover courses, evaluate the value of Premium, start trials, subscribe, and continue engaging with skill-building content. Your task is to define what an experimentation dashboard for Premium should show so product teams can understand whether experiments are improving member and business outcomes without harming trust, learning quality, or the broader LinkedIn ecosystem.

Focus on Premium experiences for learning users, such as upsell surfaces, trial flows, course recommendations, skill insights, learning reminders, or Premium-only learning benefits. The dashboard should help PMs, data scientists, designers, and business stakeholders interpret experiment results consistently across cohorts and make reliable launch, iterate, or stop decisions.

This is a metrics design question, not a visual design exercise. Emphasize what metrics should be tracked, how they should be defined, what denominators and cohorts matter, what instrumentation is required, and how the dashboard should support decision-making.

The experience should consider:

- Primary success metrics for Premium learning experiments, including how to distinguish learning engagement, conversion, retention, and monetization outcomes.

- Clear metric definitions, denominators, time windows, and attribution rules for trial starts, paid subscriptions, course starts, course completions, and repeat learning behavior.

- Segmentation by member intent, subscription status, geography, acquisition channel, job-seeking status, skill area, and new versus returning learning users.

- Experiment health checks such as sample size, randomization balance, exposure logging, event completeness, latency, and statistical confidence.

- Guardrail metrics related to cancellations, refund requests, spammy engagement, degraded feed/search experience, notification fatigue, member trust, and content quality.

- Funnel instrumentation across impression, click, trial start, payment, learning activation, continued usage, and renewal moments.

- Dashboard views that help compare variants, detect heterogeneous effects, identify trade-offs, and decide whether results are actionable.

- Decision usefulness for different stakeholders, including PMs evaluating product impact, data scientists validating experiment quality, and executives reviewing Premium growth.

The goal is to describe a practical experimentation dashboard that enables LinkedIn Premium teams to evaluate learning-user experiments with confidence, balancing member value, professional trust, and subscription business impact.