What the LTV:CAC ratio measures in a mobile app
LTV:CAC for mobile apps compares the discounted value a user contributes across the app relationship against the total cost of acquiring them. The reading is not the same as in B2B SaaS: in mobile, the retention curve drops very fast in D1-D7, monthly ARPU is usually low, and CAC varies 3x-5x across channels by bid and vertical. That is why the ratio loses meaning if it is not computed with the real retention curve and with CAC segmented by source.
Formulas that work in mobile
The simple version — LTV = ARPU × gross margin / monthly churn — underestimates value when the app has cohorts with long retention (subscription apps, fitness, finance). The actionable version decomposes LTV as:
- Net LTV = ARPPU × (cumulative 12-month payers) × gross margin − platform costs (Apple/Google 15%-30%).
- CAC by channel = spend on that channel ÷ attributed installs that were still active at D30.
- Payback period = CAC ÷ (monthly ARPU × gross margin). Under 12 months is healthy; under 6 months is excellent.
Why flat monthly churn misleads
Typical mobile apps lose 75%+ of users by D7 and 95%+ by D30 (AppsFlyer 2024). If you apply a flat 20% monthly churn you are assuming linearity where there is exponential decay. Real retention is modeled with the D1/D7/D30 curve and extrapolated with a power function — not with constant churn — to approximate mature cohort behavior.
CAC broken down by channel
Apple Search Ads, Meta UAC, Google App Campaigns and TikTok Ads have very different profiles. ASA wins on intent (CR 67% in 2024, SplitMetrics) but caps volume; Meta and Google UAC scale better but with more volatile CAC after iOS 14.5 and the SKAdNetwork era. A blended CAC hides that one channel subsidizes another: that is why the simulator lets you input CAC by channel and see which source has the best real ratio and payback.
When your unit economics do not work
Ratio 2:1 with payback >18 months and runway <12 months is a red flag: every dollar spent on UA drains cash with no return before you run out of runway. The answer is almost never to cut CAC proportionally — the best channels are already optimized — but to raise ARPU via a more aggressive paywall, annual bundles or hybrid monetization (IAP + ads).
How a growth manager uses this calculator
Enter ARPU, margin, D1/D7/D30 curve and CAC by channel. Get projected 12- and 24-month LTV, LTV:CAC ratio by source, payback period and a per-channel break-even line. The output tells you which channel to cut, which to double and where to raise paywall before committing runway.
Cohort analysis: the only honest LTV
Aggregate LTV is a lagging illusion; the real shape lives in the cohort. Extract installs by acquisition week, track revenue and retention at D7/D30/D60/D90/D180 and plot cumulative ARPU by cohort. You will see three things a blended number hides: whether new cohorts monetize better or worse than previous ones (product-market fit direction), which channels deliver cohorts with durable tail vs fast decay, and when a seasonal spike inflated an average that is no longer representative. If your dashboard still only shows blended LTV and blended CAC, your UA team is optimizing on an average that contains two or three regimes glued together.
Benchmarks by vertical
Segwise's gaming-vertical breakdown works as a sanity check: simulation games LTV $0.50-$1.20, puzzle $1.00-$2.50, action $2.00-$5.00, hypercasual $0.10-$0.35. Subscription apps live on a different curve: Adapty 2024 reports iOS subscription apps averaging ~$8.39 D90 ARPU vs $1.54 on Android — a 5.4x spread driven by willingness-to-pay and the share of organic installs. For a subscription app, blended LTV of $15-25 with CAC of $4-8 is typical in healthy categories (meditation, fitness, finance).
Common mistakes that break the math
- Using gross revenue instead of net: Apple and Google take 15%-30%. Skipping it inflates LTV and makes a 2:1 ratio look like 2.5:1.
- Mixing paid and organic in CAC: organic installs have cost (brand, content, ASO) but should not load onto paid CAC. Report paid and blended CAC separately.
- Forgetting gross margin: ARPU × lifetime is not LTV; ARPU × lifetime × gross margin is. In subscription apps with platform fees the typical margin is 60%-75%, not 80%-90% as in pure SaaS.
- Projecting LTV on incomplete cohorts: a 30-day cohort cannot tell you 12-month LTV without a curve-fitting model. Always label your LTV with the observation window (D90 LTV, D180 LTV, pLTV-projected 12m).
- Confusing payback with LTV:CAC ratio: a 4:1 ratio with 24-month payback can still be lethal if your runway is 14 months — you run out of cash before the math closes. Always read both numbers together and pair them with cash-on-hand.
Privacy-first attribution: the post-iOS 14.5 reality
Apple's App Tracking Transparency (ATT) framework (released iOS 14.5, April 2021) and the deprecation of IDFA for user-level tracking fundamentally changed mobile attribution. In 2026, the UA manager operates in a world where:
- SKAdNetwork (SKAN 4) provides deterministic but delayed conversion reporting with limited dimensionality — you know a campaign drove installs, but you cannot always attribute to which specific creative, audience, or targeting.
- Modeled attribution (Meta Advantage+ SKAN, Google Privacy Sandbox) fills the IDFA gap with statistical inference, not user-level data.
- Apple Search Ads retains user-level data within Apple's own ecosystem (no IDFA required for ASA targeting), which is why ASA conversion rates (67% in 2024 per SplitMetrics) remain exceptionally high — it targets based on App Store signals, not cross-app tracking.
Implication for LTV:CAC analysis: pre-ATT CAC data from 2020–2021 is not comparable to post-ATT data from 2023–2026. If you are modeling a cohort that spans this period, the apparent improvement in CAC may reflect attribution loss (fewer installs being credited to paid, inflating organic attribution) rather than genuine channel efficiency.
Retention benchmarks by vertical (2026)
D30 retention is the most cited cohort metric in mobile because it filters out the long tail of bounced installs and represents a user who has had time to experience the core product loop. AppsFlyer State of App Monetization 2024 benchmarks:
- Gaming (casual/puzzle): D1 35–45%, D7 15–22%, D30 6–10%.
- Gaming (mid-core/strategy): D1 30–40%, D7 12–18%, D30 5–8%.
- Health and fitness: D1 25–35%, D7 12–18%, D30 8–14%.
- Fintech / personal finance: D1 40–55%, D7 25–35%, D30 15–25%.
- Social and messaging: D1 60–75%, D7 40–55%, D30 25–40%.
Fintech apps retain meaningfully better because financial utility is daily and the switching cost (re-entering bank accounts, learning a new interface) is real. A fintech app at D30 20% vs a casual game at D30 7% will produce 3–4× the LTV on the same install, which justifies 2–3× higher CAC tolerance.
The viral coefficient and organic flywheel
Many LTV:CAC models ignore the viral coefficient (K-factor): the number of new users each existing user generates through referrals, social sharing, and word of mouth. If K = 0.3, each paid install generates 0.3 organic installs on top; effective CAC = paid CAC ÷ (1 + K) = CAC ÷ 1.3 — a 23% reduction in effective acquisition cost.
Apps with strong referral programs (Robinhood's free stock referral, Cash App's $5 send referral, Duolingo's streak-share mechanic) achieve K-factors of 0.4–0.8, effectively cutting blended CAC in half while organic channels scale. The implication for LTV:CAC is material: a 3:1 ratio on paid-only basis can become 5:1 or 6:1 when organic flywheel effects are included. Apps that grow from word-of-mouth and content (TikTok virality, Reddit communities, influencer review) can sustain unit economics that appear impossible on a purely paid basis.
Worked example: fintech app, $48 CAC vs $145 LTV (3.0:1)
Nómada Capital (fictitious name, representative data) is a personal finance app targeting freelancers and self-employed workers in Mexico and Colombia. Monthly subscription: MXN 99 / COP 12,000 (approximately USD 5.80/month blended). Apple/Google fee: 15% (small business rate). Gross margin on subscription revenue: 72%.
Retention curve (D-day → retention): D1 58%, D7 38%, D30 22%, D90 14%, D180 10%, D365 7%.
LTV calculation (12-month cohort):
- Average active months per user: integral of retention curve ≈ 5.8 months.
- Net ARPU: USD 5.80 × 85% (after platform fee) × 72% (gross margin) = USD 3.54/month.
- LTV (12m): 5.8 × USD 3.54 = USD 20.50 (12-month observable window).
- Projected LTV (24m with power-law extrapolation): USD 32.80.
- Blended CAC (ASA 40% budget + Meta 40% + organic 20%): USD 48.
- Effective LTV:CAC ratio (24m pLTV): USD 32.80 ÷ USD 48 = 0.68:1 — loss on every install.
Diagnosis: the subscription price (USD 5.80) is too low for the CAC the channels require. Action: annual plan at USD 49.99/year (USD 4.17/month) with 14-day free trial, reducing churn friction and improving LTV to USD 145 projected (paid upfront at 72% margin = USD 36 net, with 85% year-1 retention on annual plan). Blended CAC stays USD 48. Ratio: 3.0:1 with 16-month payback on annual cohort.
How to read the result alongside cash runway
Healthy unit economics on paper are not enough if the business does not survive long enough to harvest them. A practical heuristic: payback period must be shorter than (current runway − 6 safety months). With 12 months of runway, only channels paying back in under 6 months should receive incremental UA budget; the rest go on hold until you raise, hit cash-flow positive, or reduce burn. The simulator surfaces this constraint by combining the LTV:CAC view with a runway view, so growth, finance and product can decide together rather than each defending their own dashboard.