Onboarding Flow Planner

Design onboarding that reaches value fast and reduces early churn.

Inputs

Segment

Acquisition mode

Product name

ICP (who)

Primary outcome (what they want)

Activation event

First-session checklist (one per line)

Keep it 4–6 steps. If it takes longer than 10–15 minutes, it’s too much.

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Generated onboarding plan (copy/paste)

ONBOARDING PLAN

Product: ProblemSeekr
ICP: Founders validating SaaS ideas
Primary outcome: find high-signal problems faster
Trial mode: Free trial

ACTIVATION EVENT
- User imports/creates data and gets first actionable output

FIRST SESSION CHECKLIST (what happens in the first 10–15 minutes)
- Create workspace
- Connect/import data
- Run first workflow
- Save/export result
- Invite 1 teammate (optional)

7-DAY ONBOARDING SEQUENCE (email/in-app)
- Day 0: Welcome + set up first workflow (time-to-value).
- Day 1: Quick win - show a result and explain what it means.
- Day 2: Remove friction - templates, examples, and best practices.
- Day 3: Proof - show a use case, metric, or short success story.
- Day 5: Depth - advanced workflow or automation (optional).
- Day 7: Next step - upgrade / book help / ask for feedback.

EARLY CHURN RISKS
- Time-to-value is too long
- Setup is confusing or requires too much data
- User doesn’t understand the output / ROI
- Workflow does not repeat (no reason to return)
- Product feels generic (no tight ICP fit)
- Price sensitivity
- No clear ROI story
- Switching costs

MITIGATIONS
- Show value in the first session (one workflow, one outcome)
- Use templates and defaults to reduce setup time
- Make the output explain itself (what it means + what to do next)
- Add a recurring hook (weekly digest, scheduled report, alerts)
- Make it obvious what to do next (single primary CTA)
- Clarify ROI with 1–2 measurable metrics
- Provide quick-start video or walkthrough

NOTES
- Keep the path linear: one primary action at a time.
- Time-to-value should be measurable and visible.

Disclaimer: This is a planning framework. Your actual best onboarding comes from observing user behavior and iterating quickly.