Conversion in LinkedIn fell after a redesign. How would you investigate

Problem Statement Description

Product context: Microsoft is a productivity, software, AI, gaming, and cloud company; its products include Windows, Microsoft 365, Teams, LinkedIn, Xbox, Azure, Dynamics, and Copilot. LinkedIn is Microsoft's professional network; its products include profiles, feed, jobs, recruiting, LinkedIn Learning, sales tools, messaging, and ads.

LinkedIn has recently launched a redesigned experience for a conversion-critical flow used by IT leaders, such as signing up, upgrading, requesting contact, responding to a lead-gen offer, or completing another high-intent action. After launch, the team observes that conversion has fallen versus the prior experience. You are asked to investigate the drop and determine what is happening, how serious it is, and what actions the team should take next.

This is a root-cause analysis interview question. Focus on how you would frame the anomaly, validate that the decline is real, segment the data, inspect the redesign funnel, generate hypotheses, and decide on mitigation. Assume LinkedIn operates at large scale, with multiple surfaces, logged-in and logged-out users, enterprise trust expectations, experimentation systems, and dependencies across design, ranking, notifications, identity, payments, sales, and analytics.

Your investigation should be specific to LinkedIn’s user journey and the IT leader segment. Consider that these users may arrive through search, feed, email, ads, Sales Navigator, company pages, content, or referrals, and may have different levels of intent, account permissions, privacy expectations, and enterprise purchasing context.

The experience should consider:

- How “conversion” is defined, including the exact numerator, denominator, funnel start, funnel end, and whether the metric is session-based, user-based, account-based, or lead-based.

- Whether the drop is statistically and practically meaningful, including timing, seasonality, traffic mix changes, experiment exposure, and comparison against control or historical baselines.

- Funnel segmentation by platform, geography, acquisition channel, logged-in state, account type, company size, seniority, industry, and IT leader-specific cohorts.

- Instrumentation and logging checks, including event schema changes, missing events, duplicated events, attribution changes, latency, consent/privacy impacts, and dashboard regressions.

- Product hypotheses tied to the redesign, such as discoverability, information clarity, trust signals, page speed, form friction, CTA placement, personalization, accessibility, or mobile usability.

- External and operational factors, such as campaign changes, sales handoff issues, pricing or packaging changes, notification/email changes, ranking changes, outages, or competitor/news effects.

- Evidence needed to prioritize hypotheses, including quantitative funnel cuts, session replays where appropriate, user research, support/sales feedback, experiment analysis, and cohort-level diagnostics.

- Mitigation and prevention paths, including rollback criteria, targeted fixes, communication to stakeholders, monitoring, and safeguards for future redesign launches.

The goal is to demonstrate a structured RCA approach that separates measurement issues from real user experience problems, narrows the investigation using data and segmentation, identifies likely causes without jumping to conclusions, and recommends a responsible path to restore conversion while protecting LinkedIn user trust and enterprise credibility.