Add Onboarding

Replace a bare handoff at purchase with a guided first week that gets buyers to their first real result before doubt sets in.

Pro moderate Offer Improvement Recipes

What it adds

Fixes the offer that sells well then abandons the buyer at delivery, producing low usage, weak proof, and avoidable refunds. The AI builder traces what buyers face immediately after purchase in the source files, locates where they stall, and designs a paced sequence that carries them to first value. The outcome is higher activation, stronger results to point at, and fewer support tickets asking where to start.

What your builder is told to do

The actual instructions, in order.

1
Inspect the source files and reconstruct the buyer's first seven days step by step, marking every point where a support thread, drop-off, or unanswered question appears.
2
Define the first value moment precisely — the earliest point where the buyer experiences a real result, not merely completed setup — and make every onboarding step serve reaching it.
3
Work backwards from that moment to the minimum sequence of actions required, cutting any step that does not move the buyer closer, and give each step a single action with an expected duration.
4
Separate structural changes from copy changes: structural work is the sequence, the delivery cadence, the completion checkpoints, and the stall-detection trigger; copy work is each step's instruction, the expectation-setting message at purchase, and the progress language.
5
Add a stall check at the point the source files show drop-off concentrating, with a defined follow-up for buyers who have not progressed.
6
Set expectations at purchase by telling the buyer exactly what arrives, when, and what they should have accomplished by the end of the first week.
7
Specify responsive behavior: on mobile, one step per screen with a persistent progress indicator and the next action always visible without scrolling; on tablet, the step list and current step render side by side; on desktop, the full sequence, current position, and step detail appear together.
8
Define tracking: measure the share of buyers reaching the first value moment, median time to reach it, step-level drop-off, and refund rate in the first fourteen days. Rollback rule — if refunds or support volume rise after launch, shorten the sequence to the steps preceding first value and retest before adding any further stages.

Edge cases it handles

The things an agent skips when you only say "build a add onboarding".

Do not withhold anything the buyer already paid for behind onboarding gating; sequence guidance, not access.
Preserve the delivery commitments stated in the source files, including any timing promise made at purchase.
If usage data is unavailable, base the sequence on support-thread evidence and record that substitution as an assumption.
Buyers who are already experienced must be able to skip ahead without losing progress tracking.
Avoid adding motivational filler between steps; every message must carry an action or a checkpoint.

Definition of done

Your builder is required to check every one of these before reporting the work finished.

The buyer's first seven days are reconstructed from the source files with stall points marked.
The first value moment is defined as a result, not a setup milestone, and every step serves reaching it.
Before and after post-purchase experiences are documented side by side.
A stall check with a defined follow-up exists at the evidenced drop-off point, and a skip path exists for experienced buyers.
Mobile, tablet, and desktop presentations of the sequence are each specified.
Activation and early-refund tracking are defined with a written rollback trigger.
The builder reports which source files it used and lists any drop-off point it inferred rather than observed.

Related offer files

Offer Improvement Recipes

Create An Order Bump

Insert a single low-friction add-on at checkout that completes the purchase without pulling attention off the main decision.

Offer Improvement Recipes

Split Into Tiers

Break a single take-it-or-leave-it offer into tiers that capture buyers at different budgets without gutting the core promise.

Offer Improvement Recipes

Add Usage Limits

Define honest usage limits that tie price to value received and stop a minority of heavy users consuming the entire margin.

How it works

1

Copy the link

Grab the Markdown blueprint URL for this offer type.
2

Give it to your builder

Paste it into Claude Code, Cursor, Codex, or whatever AI builder is already working in your app.
3

It maps, then builds

Your agent reads the blueprint first, then builds the offer into your app around the product, audience, and stack.

Works with your stack

These blueprints are written to adapt. They tell the agent to detect your framework, match your existing design system, and use your source files instead of guessing the offer.

Need it tighter than that? Customize the offer file and tell it exactly which source docs and stack to use.