onboarding - marketingskills online
What It Does
This skill gives the LLM the ability to diagnose onboarding drop-off like an activation CRO specialist.
It is built for SaaS products, apps, marketplaces, and content products where users sign up, click around, and leave before reaching the action that predicts retention.
The output is an activation repair plan: where users break, what that break costs, what to fix first, how to recover stalled users, and which experiments to run next.
What Makes It Different
How It Works
The skill starts with the inputs that separate a real activation diagnosis from a generic audit:
- Product type and core value
- Current activation definition or suspected aha moment
- What happens immediately after signup
- Funnel steps and drop-off numbers if available
- Existing emails, in-app prompts, empty states, checklists, or setup flows
- Retention signals from users who stick around
From there, it maps the break and returns a repair plan:
- Activation goal and aha moment
- Worst drop-off point and likely cause
- Estimated upside from fixing the worst drop
- First-session goal and step sequence
- Checklist, empty-state, tooltip, or guided setup changes when useful
- Email and in-app triggers
- Stalled-user recovery rules
- Metrics to track
- Experiment directions from the onboarding experiment library
What The Output Looks Like
Users receive an activation repair plan that tells the team what to fix first and why.
- A clear activation definition and aha moment
- Funnel diagnosis with the biggest drop-off point when data is available
- Improvement estimate for the highest-leak step
- A redesigned first-session path
- Recommendations ordered by expected impact and effort
- Specific fixes for empty states, first actions, progress mechanics, checklists, guided setup, and role-based paths
- Stalled-user recovery tactics for email, in-app, and human follow-up
- Experiment ideas to test the recommended changes
- A measurement plan for activation rate, time to activation, setup completion, and Day 1/7/30 retention
Typical Use Cases
- Fix a product where users sign up but never reach the aha moment
- Find the exact funnel step where new users fall off
- Redesign the first-run experience around one successful first session
- Improve activation rate for a SaaS, marketplace, mobile app, or content product
- Diagnose why users click around but do not commit to the key action
- Plan experiments for onboarding simplification, personalization, or recovery flows
- Build recovery paths for users who stall after signup or incomplete setup
Example
Input
We run an agent marketplace. 80% of users view agent pages, 30% click chat, and only 3% purchase. Help us redesign onboarding so more users reach activation.
Output
A structured activation repair plan identifying the likely aha moment, the biggest funnel break, what the break is worth fixing, a redesigned first-session path, recommendations for empty states and paywall timing, recovery triggers for stalled users, onboarding experiments to test, and a measurement plan focused on activation and retention.
Inputs It Handles
Users can provide:
- What your product is
- What activation means in your product
- What happens right after signup
- Where users currently drop off
- Any funnel or cohort data you already have
- Existing onboarding screens, checklists, emails, or empty states
- What retained users tend to do early
It can also help when the team knows onboarding feels weak but has not yet clearly defined the aha moment. In that case, it starts by proposing activation candidates and the data needed to validate them.
Best For
- Product and growth teams fixing activation drop-off
- SaaS teams redesigning setup and first-run UX
- Marketplaces and apps with a weak first-value experience
- Founders who want users to reach value faster and retain better
- Lifecycle teams recovering users who stall before activation
