Mobile App Unit Economics: A Working Guide

A reference doc for the marketing team. Use it alongside subcalculator.space so everyone walks out of the session with the same vocabulary, the same default assumptions, and a sharper sense of which numbers actually move the business.

How to read this doc

Each metric has four parts:

1. What it is in plain language
2. Formula so there’s no ambiguity
3. A worked example with realistic mobile app numbers
4. What it actually tells you (and where it lies to you)

At the end there’s a section on how the metrics connect, what the highest-leverage inputs usually are, and what to debate during the session.

1. ACQUISITION METRICS

1.1 CPI — Cost Per Install

What it is: The average paid cost to get one person to install the app. Only counts paid installs, not organic.

Formula:

CPI = Total ad spend / Paid installs

Example: You spend $20,000 on Meta ads in a month and get 8,000 installs attributed to that spend. CPI = $2.50.

What it tells you: How efficient your top-of-funnel buying is on a given channel. It’s the cleanest channel-comparison metric because it strips out everything downstream.

Where it lies: CPI is meaningless on its own. A $1 CPI from a channel that converts 0.5% to paid is worse than a $5 CPI from a channel that converts 8%. Never optimize CPI in isolation. The teams that win optimize CAC and treat CPI as a diagnostic.

Realistic ranges (2025–2026, US/EU iOS):

• Meta: $3 to $8
• TikTok: $2 to $6
• Apple Search Ads: $1.50 to $5
• Google UAC: $2 to $7
• Health, fitness, finance verticals run higher. Casual games and utilities run lower.

1.2 CAC — Customer Acquisition Cost

What it is: The fully-loaded cost to acquire one paying customer. This is the number that ties marketing to the P&L.

Formula:

CAC = Total acquisition spend / New paying customers

“Total acquisition spend” should include ad spend, creative production, influencer fees, agency fees, and ideally a portion of marketing salaries if you want a true blended number. Most teams just use ad spend, which is fine as long as you’re consistent.

Example: $20,000 in ad spend produces 8,000 installs. Of those, 4% become paying users. That’s 320 paying users. CAC = $20,000 / 320 = $62.50.

What it tells you: What you can afford to spend on paid growth before the unit economics break. Everything downstream (LTV, payback period, ratios) depends on this number being honest.

Where it lies:

• Blended vs paid: “Blended CAC” mixes paid customers with organic ones, which makes the number look smaller than it is. Always show both. Paid CAC is the one that determines whether you can scale spend.
• Attribution windows: If you measure CAC on a 7-day window but most users convert on day 14, you’re undercounting wins and overstating CAC.
• Channel mixing: A single blended CAC across Meta + TikTok + ASA hides which channel is actually working. Always break it down.

Rule of thumb: If your paid CAC is rising month over month while spend is flat, your creative is fatiguing. If it’s rising while spend grows, you’ve hit the ceiling of your audience and need a new angle or channel.

1.3 CAC by Channel

What it is: CAC computed separately for each acquisition source.

Why it matters more than blended: Blended CAC is an average that conceals everything actionable. The conversation you actually want with the team is “Meta is at $45 and scaling, TikTok is at $90 and broken, ASA is at $30 but capped at $5K/month spend.” That’s a strategy meeting. “Our CAC is $58” is just a number.

Example (a real-feeling table):

The blended number says “we’re at $62.” The breakdown says “ASA is the bargain, double down. Influencer is broken at this volume, kill or rework.”

2. FUNNEL CONVERSION METRICS

2.1 Install → Trial Start %

What it is: Of users who install, how many actually start the free trial (or whatever the activation event is).

Formula:

Install-to-trial % = Trial starts / Installs

Example: 8,000 installs, 3,200 trial starts. Install-to-trial = 40%.

What it tells you: How well onboarding sells the value before asking for payment. A weak number here usually means the paywall hits too early, the value prop isn’t clear in the first 60 seconds, or the trial requires too much friction (credit card upfront, account creation, permission spam).

Realistic ranges:

• Hard paywall (no free tier): 8 to 20%
• Soft paywall with free use: 25 to 50%
• Freemium with optional trial: 40 to 70%

2.2 Trial → Paid %

What it is: Of users who start a trial, how many convert to paying customers when the trial ends.

Formula:

Trial-to-paid % = Paid conversions / Trial starts

Example: 3,200 trial starts, 320 convert to paid. Trial-to-paid = 10%.

What it tells you: Whether the product delivered enough value during the trial that users opted in to keep paying. This is the single highest-leverage metric in most subscription apps and the one most teams underinvest in.

A 2 percentage point lift in trial-to-paid is usually worth more than a 20% reduction in CPI.

Realistic ranges:

• No credit card required: 5 to 15%
• Credit card required upfront: 40 to 60%
• Hybrid (CC optional): wildly varies, 15 to 35%

Where teams leak:

• No expiration reminder before the trial ends
• Trial length doesn’t match time-to-value (more on this below)
• Onboarding doesn’t push users to the “aha moment” before the trial expires
• No win-back offer for trial cancellers

2.3 Retention: D1 / D7 / D30

What it is: The percentage of users who open the app again N days after install. D1 = next-day return rate. D7 = 7 days later. D30 = 30 days later.

Formula:

DN retention = Users active on day N / Users who installed on day 0

Example: 8,000 installs. 3,200 open it on day 1 (D1 = 40%). 1,600 open on day 7 (D7 = 20%). 800 open on day 30 (D30 = 10%).

What it tells you: Retention is the leading indicator of LTV. You can read someone’s LTV three months in the future from their D7 today, with surprising accuracy. This is why investors obsess over retention curves.

Benchmarks (good/median/bad for consumer mobile):

(Health, fitness, and habit apps tend to retain higher. Utility apps lower. Games have their own curves.)

The shape matters more than the absolute number. If your D7-to-D30 ratio is healthy (i.e. the curve flattens instead of falling off a cliff), you have a real product. If D30 is a fraction of D7, you have a leaky bucket and no amount of paid acquisition will fix it.

2.4 Onboarding Completion %

What it is: The percentage of users who finish the entire onboarding flow (whatever you define as the “aha moment” or activation event).

Formula:

Onboarding completion % = Users who reach activation event / Installs

Example: 8,000 installs, 4,800 complete onboarding. Completion = 60%.

What it tells you: Where in the first 5 minutes you’re losing people. Pair this with screen-by-screen drop-off (Mixpanel, Amplitude, PostHog all do this) and you’ll usually find one or two screens that account for most of the loss. Fix those and everything downstream improves.

Rule of thumb: Anything below 50% completion in a freemium app is a problem. Below 30% is a fire.

3. MONETIZATION METRICS

3.1 ARPU vs ARPPU

What they are:

• ARPU (Average Revenue Per User): Revenue divided by all users.
• ARPPU (Average Revenue Per Paying User): Revenue divided by only paying users.

Formulas:

ARPU  = Total revenue / Total users
ARPPU = Total revenue / Paying users

Example: 10,000 total users, 500 paying, $25,000 monthly revenue.

• ARPU = $2.50
• ARPPU = $50.00

What they tell you (different stories):

• ARPU is the headline business metric. It tells you how much each user is worth on average, including freeloaders. This is what scales with growth.
• ARPPU tells you whether your pricing is working for the people who actually pay. If ARPPU is low, your pricing is too cheap or your users are downgrading. If ARPPU is high but ARPU is low, you have a conversion problem (lots of free users, few paying), not a pricing problem.

Why both: A team obsessed with ARPU might bloat the user base with low-intent installs. A team obsessed with ARPPU might price out the casual users who would have converted at a lower tier. You need both in view.

3.2 Trial Length and Trial-to-Paid by Length

Why this is its own line item: Trial length is one of the most-tested and most-misunderstood variables in subscription apps. The common assumption is “longer trial = more conversion.” It’s usually wrong.

The pattern most teams find when they actually test it:

The principle: Trial length should match time-to-value. If your product delivers a clear win in 2 days, a 7-day trial creates urgency. If it takes 3 weeks for the user to see results (e.g. fitness, language learning), a 7-day trial sets you up to fail.

What to test in the calculator: Flex trial-to-paid by ±5 percentage points and see what it does to LTV and payback. Almost always the largest swing in the model.

3.3 Refund Rate

What it is: The percentage of paid transactions that get refunded by Apple or Google.

Formula:

Refund rate = Refunded transactions / Total paid transactions

Example: 320 paid conversions in a month, 38 refunds. Refund rate = 11.9%.

What it tells you: This is the metric most calculators forget. Apple and Google make refunds easy, and refund rates of 5 to 15% are normal in consumer subscription. If you’re modeling LTV without subtracting refunds, you’re overstating it by 5 to 15% across the board.

Where it gets ugly: Some categories (anything with a “diet,” “AI,” or “premium” promise) see refund rates pushing 20%. Pay attention to your category benchmark and bake it into the model.

How to reduce it: Clearer pre-purchase expectations, post-purchase onboarding that immediately delivers a quick win, and ironically, easier in-app cancellation (because users who can cancel directly don’t go to the app store and leave a one-star review on the way out).

3.4 Net Revenue After Store Cut

What it is: Gross revenue minus Apple’s or Google’s commission.

The cut:

• Apple: 30% standard, 15% if you’re under $1M annual revenue (Small Business Program) or after a user has been subscribed for 12+ months
• Google: same structure

Formula:

Net revenue = Gross revenue × (1 - store commission)

Example: $25,000 in gross subscription revenue at 30% Apple cut. Net = $17,500.

Why this matters in unit economics: Your revenue is gross. Your cash is net. CAC and LTV must be computed on net, not gross, or your payback period is a fantasy.

The trap: A lot of teams build their model on gross revenue because that’s what shows up in App Store Connect headlines. Then they wonder why the bank account doesn’t match the spreadsheet.

4. LTV AND PAYBACK

4.1 LTV — Lifetime Value (done right)

What it is: The total net revenue you expect to earn from one customer over the entire time they use your product.

The lazy formula (avoid):

LTV = ARPU / Churn rate

This formula assumes constant churn forever, which is never true. Real churn is high in month 1, drops in month 3, stabilizes around month 6. The formula will overstate LTV by 30 to 100% for most apps.

The right way: cohort-based LTV. Take a cohort of users who all started in the same month. Track how much net revenue they generate in month 1, month 2, month 3, and so on. Add it up. That’s the actual LTV of that cohort. Project forward based on the curve, not on a single churn assumption.

Example (cohort LTV):

The lazy formula on the same numbers ($10 ARPU / 15% monthly churn) would give you $66.66. That’s a 48% overstatement, and you’d build your acquisition strategy on a phantom number.

What it tells you: What you can afford to spend to acquire one customer. LTV is the ceiling on CAC.

4.2 Monthly Churn % by Cohort

What it is: The percentage of paying users who cancel each month, measured by when they signed up.

Formula:

Monthly churn = Cancellations in month / Active subs at start of month

Why “by cohort”: Aggregate churn is a lying average. New users churn at 20% in month 1. Users who survive to month 6 churn at 3%. If you mix them, you get a misleading single number that hides the real story (which is: most of your churn happens in the first 60 days).

Example:

The retention story this tells: if you can get a user past month 3, they’re likely to stick. So onboarding, early engagement, and the first 90 days are where to invest.

4.3 LTV:CAC Ratio

What it is: The single most-cited unit economics metric.

Formula:

LTV:CAC = LTV / CAC

Example: LTV = $45, CAC = $62. Ratio = 0.73. That’s broken — you’re losing money on every paying user.

If LTV = $90 and CAC = $30, ratio = 3.0. That’s healthy.

Rules of thumb (mobile consumer):

• <1: Every customer loses money. Stop spending until you fix the funnel.
• 1 to 2: Marginal. You’ll grow but you’ll bleed cash.
• 3: The classic floor. Sustainable.
• 3 to 5: Healthy. Reinvest aggressively.
• >5: Either you’ve found something rare or (more likely) you’re under-spending and leaving growth on the table.

The trap: A 10:1 ratio sounds amazing but usually means you’re being too conservative on paid spend. A team with 10:1 should be pushing into less-efficient channels until the ratio drops to 4:1, because absolute profit is what matters, not the ratio itself.

4.4 CAC Payback Period

What it is: How many months it takes for a single customer to generate enough net revenue to pay back what you spent acquiring them.

Formula:

Payback = CAC / Monthly net revenue per customer

Example: CAC = $30. Net revenue per customer per month = $7. Payback = 4.3 months.

What it tells you: How long your cash is tied up before each customer goes from cost to profit. This is the metric that determines how fast you can scale spend without running out of money.

Benchmarks for mobile consumer subscription:

• <6 months: Excellent. You can grow aggressively on whatever cash you have.
• 6 to 12: Healthy for most categories.
• 12 to 18: Acceptable if retention is strong and you have funding runway.
• >18: You need either much better retention or much cheaper acquisition.

Why payback matters more than LTV:CAC for cash-constrained teams: A 5:1 LTV:CAC sounds great, but if the payback is 24 months and you have 12 months of runway, you go bust before the math works. Payback is the cash-flow reality check on the LTV:CAC fantasy.

5. HOW THE METRICS CONNECT

Here’s the chain, in the order money actually flows:

Ad spend
   ↓
CPI → Installs
   ↓  (× Install-to-trial %)
Trial starts
   ↓  (× Trial-to-paid %)
Paying users
   ↓  (× Net ARPPU)
Monthly net revenue per user
   ↓  (× retention curve over time)
LTV

And the verdict metrics that fall out:

• CAC = Ad spend / Paying users (combines CPI, install-to-trial, and trial-to-paid)
• LTV:CAC = LTV / CAC
• Payback = CAC / Monthly net revenue per user

The leverage insight: When you flex any input ±20% in the calculator, the ones that move the model the most are usually:

1. Trial-to-paid % (biggest)
2. D30 retention (drives LTV directly)
3. Refund rate (silent killer)
4. CPI (always the first place teams look, often the smallest lever)

This is the most important thing for the team to internalize: CPI is where everyone instinctively optimizes, but trial-to-paid and retention are usually where the real money is.

6. WHAT TO ACTUALLY DEBATE IN THE SESSION

Defining the metrics is the easy part. Here’s what’s worth real discussion time:

1. Which input has the biggest impact when you flex it ±20%? Open subcalculator.space, take the team through one ±20% flex on each major input, and let them see for themselves which lever moves the model most. This kills the instinct to obsess over CPI.

2. Channel-level CAC vs blended. Where is the team being lied to by averages? Is there a channel that looks dead on blended numbers but is secretly your best on a per-channel breakdown?

3. Where does the funnel leak most? Walk through install → trial → paid → retained step by step. Where’s the biggest drop relative to benchmarks? That’s the highest-ROI thing to fix this quarter.

4. Are the calculator’s defaults realistic for our category? Most calculators ship with generic SaaS defaults. Mobile consumer subscription is different (refund rate higher, trial conversion different, store cuts apply). Replace the defaults with our actual numbers or close benchmarks for our vertical.

5. What’s our payback period today, and what would it need to be to scale spend 3x? This reframes the conversation from “how do we lower CAC” to “how do we shorten payback,” which usually points at retention and monetization, not just acquisition.

6. What’s the one number we’d commit to moving by end of quarter? Pick one. Force the prioritization. The team that walks out with “we’re going to lift trial-to-paid from 11% to 14% by July” beats the team that walks out with a tidy spreadsheet.

7. Quick reference card (for the wall)

That’s the full picture. The team should leave the session with the same vocabulary, the same default assumptions, and a clear sense of which two or three numbers to move next.