Business Context

Databricks recently redesigned the first-run onboarding flow in the workspace, specifically the path from landing in a new workspace to creating and running a first notebook. The UX team needs to explain not only what changed in the design, but why it was the best decision given the product data available.

Metric Scenario

Before the redesign, 62% of new workspace users clicked Create within their first session, 41% created a notebook, 28% attached compute, and 19% ran at least one cell within 24 hours. After the redesign, the corresponding rates are 71%, 49%, 35%, and 24%. However, median time-to-first-cell-run increased from 11 minutes to 14 minutes, and 7-day retention for new users is flat at 32%.

The Head of Design asks you to evaluate whether the redesign was actually a better product decision, how you would articulate that using metrics, and what additional cuts of the data you would review before presenting the conclusion to leadership.

Requirements

1. Define the primary KPI you would use to judge success for this onboarding redesign and explain why.
2. Decompose the onboarding funnel to determine where the redesign helped most and where it may have introduced friction.
3. Explain how you would justify the design decision using both leading and lagging metrics.
4. Identify at least 3 segment cuts that could change the interpretation of the results.
5. Recommend whether to keep, iterate, or roll back the redesign, based on the data given.

Data Available

• workspace_events: page_view, create_click, notebook_created, compute_attached, cell_run, publish_time, user_id, workspace_id, timestamp, device_type
• user_profiles: user_id, persona (data engineer/data scientist/analyst), company_size, region, signup_date
• workspace_metadata: workspace_id, cloud, plan_tier, admin_enabled_defaults, creation_date
• retention_daily: user_id, day_n_active, sessions, notebooks_opened