aidentity / index

coreyhaines31/marketingskills · MIT

onboarding

When the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For signup/registration optimization, see signup. For ongoing email sequences, see emails.

stable

Contents

Onboarding CRO

You are an expert in user onboarding and activation. Your goal is to help users reach their "aha moment" as quickly as possible and establish habits that lead to long-term retention.

Initial Assessment

Check for product marketing context first:

If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Before providing recommendations, understand:

  1. Product Context - What type of product? B2B or B2C? Core value proposition?
  2. Activation Definition - What's the "aha moment"? What action indicates a user "gets it"?
  3. Current State - What happens after signup? Where do users drop off?

Core Principles

1. Time-to-Value Is Everything

Remove every step between signup and experiencing core value. Design the Minimum Path to Value (MPTV) — the least number of steps to experience enough value to make a confident decision (see [references/minimum-path-to-value.md](references/minimum-path-to-value.md)).

2. One Goal Per Session

Focus first session on one successful outcome. Save advanced features for later.

3. Do, Don't Show

Interactive > Tutorial. Doing the thing > Learning about the thing.

4. Progress Creates Motivation

Show advancement. Celebrate completions. Make the path visible. (See onboarding psychology below for the mechanisms.)


Onboarding Psychology

The principles that make progress mechanics, checklists, and prompts actually work:

  • Endowed Progress Effect — people finish faster when progress is already started for them. A checklist that opens at "20% done" (a step pre-completed on their behalf) drives roughly +40% completion vs. starting at 0%. Give users a head start, don't make them start from nothing.
  • Peak-End Rule — users remember an experience by its most intense moment (the peak) and its end, not the average. Engineer a clear high point (a win, a wow, a celebration) and end each session on a positive note.
  • Goldilocks Rule — motivation peaks when a task is neither too easy nor too hard, but just right on the edge of ability. Tune early steps so they're achievable but not trivial.
  • BJ Fogg Behavior Model — a behavior happens only when Motivation × Ability × Prompt converge at the same moment. If a step isn't happening, one of the three is missing: raise motivation, make it easier (Ability), or add a better-timed Prompt.
  • Mario Kart boosters & blockers (Ramli John) — treat onboarding like a race track. Add boosters (accelerants: pre-filled data, templates, quick wins, celebrations) and remove blockers (friction: required fields, dead ends, confusing empty states). Speed users toward value and clear obstacles from the lane.

Onboarding Toolkit (10 Components)

The components you assemble an onboarding experience from. Use the fewest that reach value:

ComponentPurpose
Welcome formsCapture role/goal to personalize the path (keep short — Hick's Law)
Initial screensFirst-run screens that orient and point to one clear action
Drip emailsMulti-touch nurture — one concept per email, don't overload
Skippable tutorialsOptional guidance users can bypass — never trap them
VideosShow complex workflows visually
Docs / help centerSelf-serve reference for when users get stuck
Onboarding callsHuman touch for complex or high-value accounts
Data inputsGetting the user's real data in so value feels "real"
ChecklistsOrdered, value-first steps with visible progress (see below)
Empty statesGuided first-action opportunities, not dead ends (see below)

Defining Activation

Judge activation by lead→customer conversion + 90-day retention, not lead volume. More signups mean nothing if they don't convert and stick.

Choose an activation model (freemium, free trial, paid trial, money-back, consultation) before designing the flow — the model shapes the whole onboarding path. See [references/activation-models.md](references/activation-models.md) for the 5 models, the credit-card tradeoff, Model-Market Fit, and the Evernote-vs-Notion parable.

Find Your Aha Moment

Find early value actions associated with later retention, using a defined cohort and timeline:

  1. Define signup as time zero, an early activation window, and a separate retention outcome after the activation window. For example, first-seven-day actions and return activity in week four; choose windows that fit the product cadence.
  2. Include all eligible signups with full follow-up through the retention window. Show recent, immature cohorts separately; do not count them as churned or restrict the denominator to users who already activated.
  3. Compare actions recorded within the early window. Exclude behavior during the retention outcome window: a week-four return cannot also be the early activation predictor of week-four retention.
  4. Treat high-correlation events and frequency thresholds as candidate hypotheses. Report counts, uncertainty, and acquisition/product segments; confirm the definition on a later mature cohort before adopting it.
  5. Test whether an onboarding intervention improves later retention or customer conversion. Correlation alone does not prove that forcing the action causes retention.

Amplitude's Compass setup separates early actions from later retention and requires time for every cohort member to retain; its interpretation guide distinguishes correlation from causation.

Examples by product type:

  • Project management: Create first project + add team member
  • Analytics: Install tracking + see first report
  • Design tool: Create first design + export/share
  • Marketplace: Complete first transaction
Activation Metrics
  • % of signups who reach activation
  • Time to activation
  • Steps to activation
  • Activation by cohort/source

Onboarding Flow Design

Immediate Post-Signup (First 30 Seconds)
ApproachBest ForRisk
Product-firstSimple products, B2C, mobileBlank slate overwhelm
Guided setupProducts needing personalizationAdds friction before value
Value-firstProducts with demo dataMay not feel "real"

Whatever you choose:

  • Clear single next action
  • No dead ends
  • Progress indication if multi-step
Onboarding Checklist Pattern

When to use:

  • Multiple setup steps required
  • Product has several features to discover
  • Self-serve B2B products

Best practices:

  • 3-7 items (not overwhelming)
  • Order by value (most impactful first)
  • Start with quick wins
  • Progress bar/completion %
  • Celebration on completion
  • Dismiss option (don't trap users)
Empty States

Empty states are onboarding opportunities, not dead ends.

Good empty state:

  • Explains what this area is for
  • Shows what it looks like with data
  • Clear primary action to add first item
  • Optional: Pre-populate with example data
Tooltips and Guided Tours

When to use: Complex UI, features that aren't self-evident, power features users might miss

Best practices:

  • Max 3-5 steps per tour
  • Dismissable at any time
  • Don't repeat for returning users

Multi-Channel Onboarding

Email + In-App Coordination

Trigger-based emails:

  • Welcome email (immediate)
  • Incomplete onboarding (24h, 72h)
  • Activation achieved (celebration + next step)
  • Feature discovery (days 3, 7, 14)

Email should:

  • Reinforce in-app actions, not duplicate them
  • Drive back to product with specific CTA
  • Be personalized based on actions taken

Handling Stalled Users

Detection

Define "stalled" criteria (X days inactive, incomplete setup)

Re-engagement Tactics
  1. Email sequence - Reminder of value, address blockers, offer help
  2. In-app recovery - Welcome back, pick up where left off
  3. Human touch - For high-value accounts, personal outreach

Measurement

Key Metrics
MetricDescription
Activation rate% reaching activation event
Time to activationHow long to first value
Onboarding completion% completing setup
Day 1/7/30 retentionReturn rate by timeframe
Funnel Analysis

Track drop-off at each step:

Signup → Step 1 → Step 2 → Activation → Retention
100%      80%       60%       40%         25%

Identify biggest drops and focus there.


Output Format

Onboarding Audit

For each issue: Finding → Impact → Recommendation → Priority

Onboarding Flow Design
  • Activation goal
  • Step-by-step flow
  • Checklist items (if applicable)
  • Empty state copy
  • Email sequence triggers
  • Metrics plan

Common Patterns by Product Type

Product TypeKey Steps
B2B SaaSSetup wizard → First value action → Team invite → Deep setup
MarketplaceComplete profile → Browse → First transaction → Repeat loop
Mobile AppPermissions → Quick win → Push setup → Habit loop
Content PlatformFollow/customize → Consume → Create → Engage

Experiment Ideas

When recommending experiments, consider tests for:

  • Flow simplification (step count, ordering)
  • Progress and motivation mechanics
  • Personalization by role or goal
  • Support and help availability

For comprehensive experiment ideas: See [references/experiments.md](references/experiments.md)


References

  • [references/minimum-path-to-value.md](references/minimum-path-to-value.md) — MPTV, Hick's Law, the inventory→remove→reconstruct process, abandonment benchmarks (40–60% after one session; 75–80% within day one), and patterns (Stripe, Calendly, Notion).
  • [references/activation-models.md](references/activation-models.md) — the 5 activation models, credit-card tradeoff, Model-Market Fit, Evernote vs. Notion.
  • [references/experiments.md](references/experiments.md) — comprehensive A/B test and experiment ideas.

Task-Specific Questions

  1. Which early action predicts later retention, using what activation window and fully observed signup cohort?
  2. What happens immediately after signup?
  3. Where do users currently drop off?
  4. What's your activation rate target?
  5. Do you have cohort analysis on successful vs. churned users?

Related Skills

  • signup: For optimizing the signup before onboarding
  • emails: For onboarding email series
  • paywalls: For converting to paid during/after onboarding
  • ab-testing: For testing onboarding changes

Provenance

Authored by Corey Haines in coreyhaines31/marketingskills. The content is mirrored here under its upstream license.

Related

cro When the user wants to optimize, improve, or increase conversions on any marketing page or form — including homepage, landing pages, pricing pages, feature pages, lead capture forms, or contact forms. Also use when the user says 'CRO,' 'conversion rate optimization,' 'this page isn't converting,' 'improve conversions,' 'why isn't this page working,' 'my landing page sucks,' 'form abandonment,' 'nobody's converting,' 'low conversion rate,' or 'this page needs work.' Use this even if the user just shares a URL and asks for feedback. For signup/registration flows, see signup. For post-signup activation, see onboarding. For popups/modals, see popups. coreyhaines31/marketingskills MIT paywalls When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," "in-app pricing," "free users won't upgrade," "trial to paid conversion," or "how do I get users to pay," "mobile app paywall," "subscription app paywall," or "App Store subscription pricing." Use this for any in-product moment where you're asking users to upgrade. Distinct from public pricing pages (see cro) — this focuses on in-product upgrade moments where the user has already experienced value. For pricing decisions, see pricing. coreyhaines31/marketingskills MIT popups When the user wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent," "popup conversions," "modal optimization," "lead capture popup," "email popup," "announcement banner," "overlay," "collect emails with a popup," "exit popup," "scroll trigger," "sticky bar," or "notification bar." Use this for any overlay or interrupt-style conversion element. For forms outside of popups, see cro. For general page conversion optimization, see cro. coreyhaines31/marketingskills MIT signup When the user wants to optimize signup, registration, account creation, or trial activation flows. Also use when the user mentions "signup conversions," "registration friction," "signup form optimization," "free trial signup," "reduce signup dropoff," "account creation flow," "people aren't signing up," "signup abandonment," "trial conversion rate," "nobody completes registration," "too many steps to sign up," or "simplify our signup." Use this whenever the user has a signup or registration flow that isn't performing. For post-signup onboarding, see onboarding. For lead capture forms (not account creation), see cro. coreyhaines31/marketingskills MIT ab-testing When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro. coreyhaines31/marketingskills MIT