Funnel design for AI products: from visitor to paying customer

Time: about 30 min reading + 40 min practice

The percentages, amounts and timelines in this lesson are rough reference points and teaching models, not market statistics and not a promise of income. Measure your own conversion rates. Service prices are given as of October 2026 where noted; for the rest, check the websites.

The gist

You have a product. You have traffic. But revenue grows slowly, or not at all. The problem is almost never the product or the traffic; it's the funnel between them.

A funnel is the plumbing between "someone heard about you" and "someone pays you." Water (traffic) doesn't flow where it needs to on its own; you have to route it through a series of pipes of the right diameter. Too narrow at the start, and nothing gets in: people leave the landing page. Too wide at the end, and every "curious" visitor gets in but nobody pays, and resources are wasted.

Funnel design isn't a marketing trick. It's engineering: you measure every joint, find where it leaks, fix it, measure again. AI products in 2026 have their own quirks: a short "tried it → got the value" cycle, a high level of skepticism ("another ChatGPT wrapper?"), and the need to show a concrete result before the purchase.

In this lesson we'll cover the 5 stages of a typical AI funnel, 3 archetypes (self-service / mid-touch / enterprise), the tech stack for starting out and for scaling, and the funnel math on a teaching example.

🎨 Picture this: a funnel is plumbing. Each narrowing of the pipe is a conversion stage. If the pipe at the "TOFU → MOFU" joint is too narrow (a weak headline on the landing page), almost all the water misses. If the pipe at the "Trial → Paid" joint is too wide (you give a trial with no onboarding), water flows in and then drains back out. Good plumbing is a system of valves with known throughput at every joint.

🎯 Decision tree: which funnel your product needs

The key question isn't "how do I make my funnel more powerful" but "which funnel archetype even fits my price point and audience."

Self-Service (Type A) if:

• ✓ An inexpensive subscription (roughly $9-99/month)
• ✓ The product makes sense in 30 seconds (the value is visible on the landing page)
• ✓ B2C or prosumer (a developer/marketer who makes their own buying decisions)
• ✓ You're ready to scale through traffic, not through a sales team
• ✓ Onboarding can be fully automated

Product-Led + Sales (Type B) if:

• ✓ A mid-range price (roughly $99-999/month)
• ✓ It's used by teams (you need to onboard 2-10 people)
• ✓ B2B SMB or mid-market
• ✓ Customer success helps keep clients
• ✓ A free trial works, but sometimes a demo is needed

Sales-Led Enterprise (Type C) if:

• ✓ A high price (ACV, the annual contract value, in the tens of thousands of dollars)
• ✓ Procurement and legal review are a required part of the cycle
• ✓ Regulated industries (finance, healthcare)
• ✓ You're ready to invest in an SDR + AE team
• ✓ A 30-90 day deal cycle is normal

By default, an AI product from a single founder is Type A or B. Type C comes once there's meaningful revenue and a clear understanding of the ICP (ideal customer profile).

🎨 Picture this: a soda vending machine vs. a restaurant vs. corporate catering. The vending machine is self-service, $2 a can, thousands of customers a day. The restaurant has a server helping you, a $50 average check, hundreds of customers a day. Catering means a written contract, $50K per event, dozens of clients a month. Different funnels, different unit economics, different teams.

Key concepts

• TOFU (Top of Funnel): the awareness stage; someone hears about the product for the first time through SEO, social media or a referral
• MOFU (Middle of Funnel): the education and trust stage; someone explores the product, reads case studies, subscribes to emails
• BOFU (Bottom of Funnel): the consideration stage; someone compares options and tries the triggers (trial, demo)
• Conversion: the moment of payment, going from prospect to paying customer
• Retention: keeping customers after the first payment, fighting churn
• Expansion: growing ACV (average contract value) through upsells, cross-sells and seat expansion
• Conversion rate: the percentage that moves from one stage to the next (they multiply in a cascade)
• Funnel leak: a spot where conversion is unexpectedly low, the main target for optimization
• Aha moment: the moment the user realizes the product's value (critical for Trial → Paid)
• Activation: the user completed a key action (created their first project, sent their first message, etc.)

Theory

The 5 stages of a typical AI product funnel

TOFU (Top of Funnel) — Awareness
   ↓ (visitor → email: roughly 1-5%)
MOFU (Middle) — Interest + Education
   ↓ (email → engaged: roughly 30-50%)
BOFU (Bottom) — Consideration + Trial
   ↓ (engaged → trial: roughly 10-20%)
Conversion — Purchase
   ↓ (trial → paid: roughly 5-25%)
Retention + Expansion
   ↓ (month-over-month retention: roughly 90-95%)

Each stage is its own thing, with its own goals, content, channels and KPIs. Most founders' mistake is optimizing only TOFU (more traffic!) when the hole is in MOFU or BOFU. That's like pouring water into a leaky bucket.

🎨 Picture this: the Amazon River. A river doesn't have one "correct" channel. It has headwaters (TOFU), tributaries (MOFU), the main channel (BOFU), a delta (Conversion) and the ocean (Retention). Block one tributary and the water finds another way around. Build a dam in the delta and the whole region floods. A funnel works like a river system: as a whole.

TOFU, awareness: cold visitor → knows about you

Goal: capture an email or get a micro-conversion (a download, a subscription, use of a free tool). Don't try to sell here. The goal is to land in their inbox.

Content type: educational, not sales. "How we built X" instead of "Buy our X." Tutorials, tool lists, market overviews, case studies (even other people's).

Channels:

• An SEO blog (long-tail keywords, low competition)
• X (formerly Twitter) (build in public, tech threads)
• YouTube (tutorials, reviews)
• Reddit (r/SaaS, r/ChatGPT, r/MachineLearning, but karma first!)
• LinkedIn (B2B, especially for Type B/C)
• Indie Hackers (for solo founders)
• Hacker News (for a launch)

KPIs:

• Site visitors (organic + direct)
• Email signups
• Social reach (impressions)
• Backlinks (for compounding SEO)

Conversion benchmark (rough): visitor → email = 1-5%

What's specific to AI in 2026:

• AI visibility matters more than SEO for people who use ChatGPT/Claude as search
• "Mentioned in 3+ AI threads" is the new PR
• Demo videos (60 seconds) convert better than text

MOFU, interest: knows → trusts

Goal: build trust through value. They're already in your inbox; now show them you're not just another spammer.

Content type: deeper than TOFU. Tutorials with code, case studies with numbers, free tools (a calculator, an audit), original research.

Channels:

• Email sequences (5-10 emails, drip)
• Retargeting ads (Meta, LinkedIn)
• Community engagement (Discord, Slack groups)
• Webinars / live demos
• Podcast appearances

KPIs:

• Email open rate (a rough target of 35%+)
• Click-through rate (a rough CTR target of 5-10%)
• Repeat website visits
• Content engagement time

Conversion benchmark (rough): email → engaged = 30-50%

Patterns that work:

• "Reply with X and I'll send Y": a personal touchpoint
• A free tool that's genuinely useful (HubSpot's Website Grader is an example)
• Emails with a personal story (the founder's voice, not corporate)

BOFU, consideration: trusts → considers buying

Goal: show that your product solves their specific problem. They already understand the category; now they're comparing options.

Content type: product demos, a free trial, an ROI calculator, consultation booking, comparison pages ("X vs. Y"), pricing pages.

Channels:

• Landing pages (use-case specific)
• Sales pages
• Live demos (for Type B/C)
• A free trial signup flow
• A pricing page

KPIs:

• Free trial signups
• Demo bookings (for Type B/C)
• Pricing page views
• Comparison page reads

Conversion benchmark (rough): engaged → trial = 10-20%

Anti-pattern: showing pricing too early, at TOFU. Pricing works at BOFU, once there's trust. At TOFU, pricing kills curiosity.

Conversion: considers → buys

Goal: remove friction, close. They're already in a trial or on a demo; now you have to keep them from leaving.

Content type: clear pricing, social proof (testimonials, logos), guarantees, an FAQ for objections, urgency (without faking it).

Channels:

• The checkout page (minimal friction)
• Sales calls (Type B/C)
• Abandoned cart emails (Type A)
• In-app upgrade prompts
• Customer success outreach (Type B/C)

KPIs:

• Trial → paid conversion
• Demo → close rate
• Cart abandonment rate
• Time to purchase

Conversion benchmark (rough): trial → paid = 5-25% (it depends heavily on the product: a narrow audience with a clear value converts better than a broad freemium audience)

Retention + expansion: buys → stays + upgrades

Goal: reduce churn + grow ACV. Beginners often ignore this stage, but it determines LTV (lifetime value) and your real unit economics.

Content type: onboarding flows, in-app guidance, customer success outreach, upsell prompts, changelog updates.

Channels:

• In-app (banners, modals, tours)
• Email (lifecycle automation)
• Customer success team calls (Type B/C)
• Community (Discord, a forum)

KPIs:

• Month-over-month churn (a rough target of <5% for SaaS)
• Net Revenue Retention (NRR, a rough target of 110%+)
• Activation rate (% reaching the aha moment in the first 7 days)
• Feature adoption rate

What's specific to AI: retention drops if the "wow effect" fades fast. If someone subscribed to a $20/month AI tool, used it twice and forgot about it, they'll churn within 90 days. You need workflow integration, not one-off use.

3 funnel archetypes (which to choose)

Type A: Self-Service SaaS (low-touch)

Price: roughly $9-99/month Sales calls: none Funnel: landing → signup → onboarding (in-app) → upgrade prompt → paid

Best for:

• B2C, prosumers
• Simple products with a clear value
• High-velocity SaaS (lots of small deals)

Examples:

• Cursor
• ChatGPT Plus ($20/month as of October 2026)
• ElevenLabs Starter ($6/month as of October 2026)
• Beehiiv

Tech stack: Stripe + Customer.io + PostHog + Carrd/Webflow

Type B: Product-Led + Sales (mid-touch)

Price: roughly $99-999/month Sales calls: optional (for team plans / enterprise) Funnel: landing → trial → in-app onboarding → check-in calls (optional) → upgrade → paid

Best for:

• B2B SMB, mid-market
• Team products (5-50 seats)
• Hybrid: a developer tries it, a manager buys it

Examples:

• HubSpot Starter
• Notion (team plans)
• Linear
• Loom Business

Tech stack: Stripe + HubSpot + PostHog + Userpilot + Webflow

Type C: Sales-Led Enterprise (high-touch)

Price: high, often by contract Sales calls: required (demo, security review, procurement) Funnel: lead magnet → SDR qualification call → AE demo → POC → procurement → close

Best for:

• B2B enterprise, regulated industries
• A long sales cycle (30-90 days)
• Complex products that need customization

Examples:

• Datadog (enterprise contracts)
• Salesforce (enterprise contracts)
• Claude Enterprise (see Anthropic's website for terms)
• Snowflake (usage-based, enterprise contracts)

Tech stack: Salesforce + Gong + Outreach + Looker + Webflow

Free trial vs. freemium vs. demo-only vs. sandbox

The choice of "first touch" model shapes everything else in the funnel.

Decision rule:

• If the product shows its value in 60 seconds of use → Sandbox or Freemium
• If onboarding takes 30+ minutes → Free Trial (the time limit creates urgency)
• If the price is high (thousands of dollars a year) → Demo-only (because the unit economics don't allow a free tier)

What's specific to AI in 2026: freemium for AI products often hurts unit economics, since every free request costs real money (compute + API). You need limits: messages/day, tokens/month.

CTAs at each stage

CTAs (calls to action) are the "valves" in the plumbing. The wrong CTA at the right stage = a leak.

Anti-pattern: "Buy now" at TOFU. It's like proposing on the first date. A cold visitor isn't ready yet.

Best practice: one primary CTA per page. Multiple CTAs (Buy now + Sign up + Subscribe + Watch demo) scatter attention and usually lower conversion.

Common funnel leak points

This is the core diagnosis. If something isn't working, it's almost always one of these 4 leaks.

Leak 1: Landing page → email (a very low percentage)

Causes:

• A weak headline ("AI tool for developers" instead of a specific benefit)
• Unclear value (what do I get for my email?)
• Too much friction (an 8-field form instead of 1)
• Trust issues (no testimonials, logos or social proof)

Fixes:

• A/B test headlines
• Cut the form down to email only (you can enrich later)
• Add social proof above the form, if it's real (for example, an actual subscriber count)
• A specific value prop ("Get the 14-day AI agent course")

Leak 2: Trial signup → activation (a low share)

Causes:

• Bad onboarding (a ton of checklist items nobody reads to the end)
• No aha moment (the user doesn't understand what the product is for)
• The empty state problem (opens the app → it's empty → closes it)
• Too many features at once

Fixes:

• Progressive disclosure (show 1 feature at a time)
• In-app guidance (Userpilot, Appcues, or custom with intro.js)
• Sample data / templates (Notion templates are the gold standard)
• A time-to-value metric (how quickly the user does their first useful thing)

Leak 3: Trial → paid (a low share)

Causes:

• Not enough value experienced (the trial period is short or features are locked)
• A missing critical feature (the one needed to close)
• Pricing shock (the trial page said "free," checkout says "$99/month")
• Friction in the payment flow

Fixes:

• Extend the trial if usage is active (14 → 30 days for highly engaged users)
• Add the critical feature OR drop the promise you're not keeping
• Transparent pricing from the start (show the price before signup)
• A one-click upgrade (the Stripe Customer Portal)

Leak 4: Customer → renewal (low retention)

Causes:

• Poor onboarding (people pay but don't use it)
• Misaligned expectations (the product does X; they expected Y)
• No expansion driver (single-feature use → no reason to stay)
• A competitor with a better offer

Fixes:

• Better onboarding (customer success outreach in the first 30 days)
• Quarterly business reviews (for Type B/C)
• Build network effects (team features, integrations)
• A loyalty program (a price freeze for early customers)

The tech stack for a funnel

Tooling is tools, not strategy. But without it you can't measure the leaks.

The starter stack (first sales): PostHog (free tier) + Carrd + Kit (free plan) + Resend Cost: minimal

The scale stack (steady sales):

• HubSpot Starter + Hotjar + Userpilot + Webflow Cost: noticeably higher; work it out from the services' current prices

Anti-pattern: buying an enterprise stack when you don't have any sales yet. The tools solve problems you don't have yet.

🎨 Picture this: buying a $5K professional espresso machine when you only make coffee for yourself in the morning. First learn to brew by hand, then scale up your tooling. Tools amplify your strategy; if there's no strategy, they amplify emptiness.

Funnel math: a teaching example

Numbers are more concrete than theory. Let's take a made-up AI tool at $99/month and calculate how the funnel turns traffic into revenue. This is a teaching model with invented assumptions, not a forecast and not a promise of income: it shows the method of calculation.

Starting conditions:

• An AI productivity tool for writers
• Trial: 14 days, all features
• Target audience: solo writers, content marketers
• Marketing budget: $0 (organic only)

Month 1 funnel:

10,000 visitors → 5% signup = 500 emails
500 emails → 20% start trial = 100 trials
100 trials → 15% convert = 15 paid customers
15 customers × $99/month = $1,485 MRR

Cohort retention assumptions:

• Monthly churn: 5% (an assumption for the example)
• Net Revenue Retention: 100% (no expansion in the first year)
• Trial-to-paid: a steady 15%
• Traffic growth: 2% month-over-month (compounding)

A 12-month projection:

Revenue for the year under this model: about $105K (the sum of monthly MRR, calculated with the formulas above)

What changes the picture (same model; run the numbers yourself):

• Doubling the conversion rate (5% → 10%) = roughly double the revenue for the year
• Doubling trial-to-paid (15% → 30%) = roughly double the revenue for the year
• Cutting churn (5% → 2%) = about $116K instead of $105K
• Doubling traffic (10K → 20K visitors) = roughly double the revenue for the year

The lesson: improving one joint by 100% doubles the model's revenue. Improving each of the five joints by 20% gives roughly 2.5 times more (1.2⁵ ≈ 2.5, compounding). Funnel optimization is a compounding game. Your real numbers will be different: plug in your own.

Anti-patterns

• ❌ Optimizing the wrong stage: lots of traffic when conversion is 0. First make sure the funnel works on small traffic (100 visitors → do you see anything?), then scale.
• ❌ Skipping MOFU entirely: "Buy now" for a cold visitor. AI products especially need an education stage (skepticism is high).
• ❌ No analytics: you can't know what's leaking. PostHog's free tier covers most of a starter's needs.
• ❌ One funnel for everyone: segment by source/persona. A visitor from Reddit and one from LinkedIn behave differently.
• ❌ Optimizing to 100%: diminishing returns after 30%. Perfectionism here = a waste of time.
• ❌ No focus on retention: a business built on LTV needs retention. If LTV = 3 months, you burn through your CAC in the very first month.
• ❌ Hiding pricing: "Contact us for pricing" only works for Type C enterprise. For Type A/B it's a conversion killer.
• ❌ Fake urgency: "Only 3 spots left!" when it isn't true. Get caught once, and your trust is gone.

Practice

Step 1: Map your current funnel

Grab paper or Whimsical/FigJam. Draw the 5 stages for your product.

TOFU: [where does the traffic come from?]
   ↓
MOFU: [how do they learn more?]
   ↓
BOFU: [what triggers a trial/demo?]
   ↓
Conversion: [how do they pay?]
   ↓
Retention: [how do you keep them?]

For each stage, fill in:

• Goal (what you want to happen)
• Content (what you show)
• Channels (where they come from)
• KPI (what you measure)
• Current conversion rate (if you know it)

Step 2: Set up PostHog (analytics with a free tier)

PostHog has a free monthly allowance of events (see its pricing page for the size); it's usually enough to get started.

# Install via NPM (for a Next.js/React SaaS)
npm install posthog-js

# In app.tsx (React) or _app.tsx (Next.js):

import posthog from 'posthog-js'

if (typeof window !== 'undefined') {
  posthog.init(process.env.NEXT_PUBLIC_POSTHOG_KEY!, {
    api_host: 'https://us.i.posthog.com',  // for the EU cloud: https://eu.i.posthog.com
    defaults: '2026-05-30',                // a set of default settings from the PostHog documentation
  })
}

// Tracking the key funnel events:
posthog.capture('email_signup', { source: 'landing_v1' })
posthog.capture('trial_started', { plan: 'pro' })
posthog.capture('aha_moment_reached', { feature: 'first_export' })
posthog.capture('subscription_paid', { plan: 'pro', mrr: 99 })

Create a Funnel report in PostHog:

1. Product analytics → New insight → Funnels
2. Steps: pageview → email_signup → trial_started → subscription_paid
3. Time window: 30 days
4. Result: you see the conversion rate at each joint

Step 3: Set up a Carrd landing page

Carrd is an inexpensive way to launch a landing page (check the current paid plan price on its website).

The structure of a single-page landing:

[Hero section]
- Headline: a specific benefit ("Write 10x faster with AI")
- Subheadline: who it's for + how
- Primary CTA: "Start free trial" (button)
- Hero image / demo gif

[Social proof bar]
- Client / publication logos
- "Used by [N] writers" (real numbers only)

[3 benefits section]
- Benefit 1 (icon + 50 words)
- Benefit 2 (icon + 50 words)
- Benefit 3 (icon + 50 words)

[How it works (3 steps)]
1. Sign up (10 seconds)
2. Connect your data
3. Start writing

[Testimonials (3-5)]
- Photo + name + role + quote

[Pricing (for Type A) or just a CTA (for Type B/C)]

[FAQ (5-10 questions)]
- Pricing, refunds, security, etc.

[Final CTA repeat]
- "Start free trial" again

Step 4: Set up a Kit (ConvertKit) email sequence

Kit (formerly ConvertKit) and Beehiiv both have free plans with a subscriber cap; check the current limits and the free features on their pricing pages.

A welcome sequence (5 emails over 14 days):

Day 0 (immediate): Welcome + what you'll get from subscribing
Day 2: Tutorial - a "first win" with the product (or a related skill)
Day 5: Case study - a real customer story with numbers
Day 8: Founder story - why I built the product, what I believe
Day 12: Soft CTA - "Want to try? Here's how to start" (offer a discount only if it's real)

Tracking:

• Open rate (a rough reference point: 35%+)
• CTR (a rough reference point: 5-10%)
• Reply rate (any real reply is a good sign)

Step 5: Identify your top leak

After 2 weeks of data in PostHog:

-- Pseudo-SQL for a PostHog funnel:
SELECT stage, count(*), conversion_to_next
FROM funnel
WHERE date >= now() - interval '14 days'
GROUP BY stage

Decision tree:

Stage 1: visitor → email well below your reference point?
  → Fix the landing page (headline, form, social proof)

Stage 2: email → trial well below your reference point?
  → Fix the email sequence (CTAs, value)

Stage 3: trial → activation well below your reference point?
  → Fix onboarding (in-app guidance, sample data)

Stage 4: trial → paid well below your reference point?
  → Fix pricing OR trial length OR the missing critical feature

Stage 5: monthly churn well above your reference point?
  → Fix retention (customer success, expansion features)

(Take your reference points from your first weeks of data and from the rough values above.)

Fix ONE leak at a time. Don't try to optimize everything at once: you won't be able to tell what actually worked.

Step 6: Run your first A/B test

PostHog Free already has A/B testing built in (feature flags).

The simplest test: a headline variation.

// In the PostHog dashboard, create a feature flag: 'landing_headline'
// Variants: A (control), B (variant)

const headline = posthog.getFeatureFlag('landing_headline')

return (
  <h1>
    {headline === 'A'
      ? 'AI tool for writers'
      : 'Write 10x faster with AI'}
  </h1>
)

Run it for at least 2 weeks (you need statistical significance, usually 200+ conversions per variant).

Common winners:

• A specific benefit > a generic feature
• A number > a vague claim ("10x faster" > "very fast")
• Customer language > marketing language
• Negative framing sometimes wins ("Stop writing slowly" > "Write faster")

Tools and resources

• PostHog: open-source product analytics with funnels, A/B testing, heatmaps. Has a free tier of events
• HubSpot: a CRM with marketing automation, a free tier for starter funnels
• Hotjar: heatmaps and session recordings for understanding UX leaks
• Kit (formerly ConvertKit): email marketing for creators and SaaS, with a free plan (check the current subscriber limit on its website)
• Userpilot: in-app onboarding tours and feature adoption tracking
• Carrd: single-page landing pages, inexpensive (check the current price on its website)
• Webflow: multi-page websites without code; see its website for pricing
• Lenny's Newsletter: a newsletter about product and growth
• Reforge: advanced material on retention and growth loops

Audience levels

Beginner (a solo product, before the first sales):

• A simple linear funnel: Carrd → Kit (ConvertKit) → Stripe
• PostHog Free for basic analytics
• Focus: one primary CTA, one email sequence, retention later
• You DON'T need: HubSpot, Hotjar, Userpilot, A/B testing

Intermediate (an SMB SaaS, steady sales):

• Segmented funnels (by source: organic vs. paid vs. referral)
• A/B testing of the main pages (built into PostHog)
• • HubSpot Free for CRM and lead scoring
• Focus: the top leak identified, fixing 1-2 per month
• Onboarding email automation (5-7 emails)

Professional (a scale-up, large revenue):

• Multi-product funnels (if you have a free tier + paid + enterprise)
• A CDP (customer data platform: Segment, RudderStack)
• A conversion engineering team (1-2 dedicated people)
• Userpilot + Hotjar + Mixpanel or Amplitude
• Sales team integration (Type B/C funnels)
• Cohort analysis, predictive churn modeling

Checklist (✅)

• The 5 stages identified for your product (TOFU, MOFU, BOFU, Conversion, Retention)
• A funnel map drawn with current conversion rates (or marked unknown)
• PostHog (or GA4) installed and tracking 4 key events
• The top 3 leak points identified from data (after 2 weeks of collection)
• The primary fix launched (one leak, not three)
• The first A/B test running (headline or CTA, not a redesign)
• An email welcome sequence (at least 3 emails) written and active
• Funnel benchmark metrics recorded (a baseline to compare against in a month)

Key takeaways

A funnel isn't a marketing trick; it's plumbing engineering. Without measuring every joint, you don't know where it leaks. PostHog's free tier + 4 basic events give a starter most of the picture. You don't need an enterprise stack; you need one measurable baseline.

The funnel archetype is set by price. An inexpensive subscription = Self-Service (Type A). A mid-range price = Product-Led + Sales (Type B). A high price = Sales-Led Enterprise (Type C). Trying to apply a Type C funnel to a cheap product will kill your unit economics.

Compound optimization beats single-stage heroics. Improving each of the 5 joints by 20% gives roughly 2.5 times more revenue (1.2⁵). Improving one joint by 100% gives only ×2. Fix one leak at a time, measure for 2 weeks, move on to the next. Funnel design is a marathon, not a sprint.

Cross-references

• Choosing a niche: niche selection and audience targeting (what determines your TOFU channels)
• Lead magnets that convert: lead magnets and email capture tactics (the TOFU → MOFU bridge)
• Real monetization cases: monetization cases and pricing strategies (the Conversion stage)
• Unit economics of the AI stack: unit economics, LTV/CAC calculations (what determines whether a funnel is viable)

Next lesson

→ Cold outreach with Claude