Investigate why conversion fell after a Experience Cloud launch

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 new Experience Cloud initiative aimed at improving acquisition and conversion among video editors, potentially connecting creative workflows with enterprise marketing use cases such as campaign asset management, personalization, approvals, analytics, and collaboration. Shortly after launch, the team observes a meaningful drop in conversion versus the pre-launch baseline.

You are asked to investigate the root cause of the conversion decline. Treat this as a product RCA exercise: clarify what “conversion” means, define the affected funnel, determine whether the drop is real or a measurement artifact, isolate impacted user segments, and develop evidence-backed hypotheses about what changed after launch.

The investigation should account for Adobe’s context: professional creative users, trust in workflow depth, AI-enabled features, integrations across creative and marketing tools, and competitive expectations shaped by products such as CapCut, Canva, Figma, Google, Microsoft, OpenAI, and Autodesk. Your analysis should focus on diagnosing the issue rather than proposing a full product redesign.

The experience should consider:

- The exact conversion event, denominator, baseline period, and expected seasonality or campaign effects

- Funnel steps for video editors, from landing or campaign entry through signup, trial, activation, purchase, or enterprise lead submission

- Segmentation by traffic source, geography, device, account type, editor persona, plan, new versus returning users, and enterprise versus individual buyers

- Instrumentation checks, including event tracking changes, attribution logic, experiment exposure, tagging, consent/privacy impacts, and data pipeline delays

- Launch-related changes such as messaging, pricing, onboarding, performance, integrations, AI feature positioning, permissions, or creative workflow fit

- External factors including competitor campaigns, paid media mix shifts, macro demand changes, or changes in search/social acquisition quality

- Evidence needed to prioritize hypotheses, such as cohort trends, session recordings, support tickets, sales feedback, error logs, experimentation data, and qualitative user feedback

- Mitigation and prevention paths, including short-term containment, stakeholder communication, monitoring, and future launch readiness checks

Your goal is to structure a clear RCA plan that separates signal from noise, identifies the most likely drivers of the conversion decline, and explains how you would use data and cross-functional evidence to recommend next steps with confidence.