Design an experimentation dashboard for Firefly

Problem Statement Description

Product context: Adobe is a creative, document, and marketing software company; its products include Creative Cloud, Photoshop, Illustrator, Acrobat, Adobe Express, Firefly, and Experience Cloud.

Adobe Firefly is used by small businesses to generate and edit creative assets for marketing, social media, documents, and brand workflows. Product teams are frequently testing changes such as question experiences, templates, model outputs, onboarding flows, pricing questions, collaboration features, and integrations with Adobe tools. The question asks you to define what an experimentation dashboard should show so teams can evaluate these tests consistently and make confident product decisions.

Focus on the metrics system, not the visual design alone. The dashboard should help PMs, designers, engineers, data scientists, and business stakeholders understand whether an experiment improved the intended user outcome, whether the result is trustworthy, and whether there are negative side effects across quality, trust, retention, cost, or monetization.

Because Firefly involves generative AI, the dashboard should account for both traditional product behavior and AI-specific concerns: question-to-output success, user satisfaction with generated content, content safety, brand appropriateness, latency, compute cost, and downstream creative workflow completion. For small businesses, the dashboard should also reflect practical outcomes such as faster asset creation, repeat usage, export/share behavior, and conversion to paid or higher-value plans.

The experience should consider:

- Clear experiment context, including hypothesis, target user segment, feature surface, exposure window, and control/treatment definitions.

- Primary success metrics with precise numerators and denominators, such as activation, generation success, edit completion, export/share rate, repeat usage, or paid conversion.

- Cohort breakdowns for small-business users, including new vs. returning users, industry/use case, plan type, geography, device, acquisition channel, and Firefly surface used.

- Instrumentation quality, including event definitions, sample size, assignment integrity, missing data, bot/internal traffic filtering, and experiment overlap.

- Guardrail metrics for latency, error rate, content safety flags, user-reported quality issues, refund/cancellation signals, compute cost, and impact on adjacent Adobe workflows.

- Statistical interpretation and decision usefulness, including confidence, practical significance, duration, novelty effects, and whether results are stable across key cohorts.

- A way to compare experiments over time so teams can learn which product bets improve creative productivity, trust, collaboration, and monetization.

The goal is to describe a dashboard that enables Adobe Firefly teams to make reliable experiment decisions for small-business users: ship, iterate, stop, or investigate further. Your answer should define the metrics, data cuts, and decision framework clearly enough that a cross-functional team could use the dashboard to evaluate real Firefly experiments without relying on vanity metrics or incomplete signals.