Company Context

Rippling is a multi-product workforce management platform serving businesses with HR, IT, payroll, and identity products in one system. The company has strong product breadth, and growth depends on getting new admins to activate key workflows quickly after signup.

Problem

A growth team proposes a new idea inside Rippling's admin onboarding flow: after a company connects payroll and adds its first employee, show a guided checklist that recommends the next highest-value setup actions, such as device management, app provisioning, and benefits enrollment. The hypothesis is that this will increase cross-product adoption and improve 30-day activation.

Today, 62% of newly created admin accounts complete payroll setup, but only 21% enable a second Rippling product within 30 days. User interviews suggest many admins understand Rippling's breadth but feel overwhelmed by setup choices and do not know which module to configure next. Engineering estimates that a fully personalized recommendation engine would take 4 months, while a rules-based checklist could ship in 4 weeks.

Your job is to decide whether this idea is an MVP worth testing rather than a feature that should wait for more validation or broader build-out.

Deliverables

1. Define the core user problem and identify which admin segment you would target first.
2. Explain how you would determine whether the proposed checklist is a true MVP versus an overbuilt solution.
3. Prioritize the minimum scope for the first test, including what you would intentionally exclude.
4. Define success criteria, leading indicators, and guardrails for the MVP test.
5. Describe the key trade-offs and what evidence would convince you to scale, iterate, or stop.

Constraints

• MVP must launch within 4 weeks.
• Team capacity: 1 product designer, 2 engineers, 1 data analyst.
• No new ML infrastructure or major backend platform changes.
• The test must run within existing Rippling onboarding surfaces and cannot require a sales-assisted rollout.
• You may assume enough traffic to evaluate directional results within 2-4 weeks, but not enough to optimize across many segments simultaneously.