Investigate why conversion fell after a Acrobat launch at global scale

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 has recently launched a major Acrobat experience globally, aimed in part at creative professionals such as video editors who use PDFs for scripts, storyboards, contracts, client approvals, asset handoffs, and review workflows. Shortly after launch, overall conversion declined. You are asked to investigate the drop as the product manager responsible for framing the root-cause analysis.

The conversion decline may involve multiple surfaces, regions, devices, acquisition channels, account types, pricing flows, or workflow entry points. The launch may also have changed onboarding, AI-assisted document actions, collaboration features, plan packaging, checkout, localization, or performance. Your task is not to immediately propose a fix, but to structure how you would isolate the issue, validate the data, identify likely causes, and guide the team toward evidence-based action.

Assume this is a high-scale global product environment with consumer, SMB, and enterprise users; paid and free tiers; web, desktop, and mobile experiences; and meaningful differences across countries, currencies, compliance needs, and creative workflows. The investigation should balance urgency with rigor, since a false diagnosis could lead to the wrong rollback or an unnecessary disruption to the launch.

The experience should consider:

- How you would define the conversion metric, including numerator, denominator, funnel steps, attribution window, and whether the issue is trial start, purchase, upgrade, renewal, or activation-to-paid conversion.

- How you would confirm the anomaly is real by checking instrumentation, event logging, experiment exposure, release timing, traffic mix, seasonality, bot activity, and reporting pipeline changes.

- Which segment cuts you would prioritize, such as geography, language, platform, browser, acquisition channel, plan type, new versus returning users, enterprise versus individual buyers, and video-editor-related workflows.

- How you would compare pre-launch and post-launch behavior across the funnel to identify where users are dropping off, including landing pages, sign-in, onboarding, feature discovery, pricing, checkout, and payment confirmation.

- What hypotheses you would form around product changes, performance, localization, pricing, AI feature messaging, trust signals, collaboration flows, competitive alternatives, or enterprise procurement friction.

- What evidence you would seek from quantitative data, session replay, customer support tickets, sales feedback, app reviews, social sentiment, and feedback from regional teams.

- How you would decide between mitigation options such as targeted rollback, hotfix, experiment pause, messaging change, traffic reallocation, or deeper investigation.

- How you would prevent recurrence through launch readiness checks, monitoring dashboards, alerting thresholds, experiment governance, regional QA, and post-launch review processes.

The goal is to demonstrate how you would lead a structured RCA for a global Acrobat conversion drop: clearly frame the problem, separate measurement issues from real user behavior changes, prioritize the highest-impact cuts, generate testable hypotheses, and drive the organization toward a confident diagnosis and responsible mitigation plan.