Trace Launch Research Notes
Working evidence file for the 2025/2026 consumer app launch research goal.
This is intentionally rough: source links, numbers, caveats, and synthesis
fragments that feed LAUNCH_RESEARCH_REPORT.md.
Research Rules
- Prioritize 2025/2026 examples and benchmark reports.
- Use 2023/2024 cases only when still strategically relevant.
- Separate verified facts, estimates, and self-reported claims.
- Translate patterns into Trace-specific decisions.
- Avoid importing tactics that conflict with Trace's product philosophy: no per-meal verdicts, no shame-based diet language, no unsafe medical claims.
Source Map
Market Benchmarks
| Source | Topic | Notes |
|---|---|---|
| RevenueCat State of Subscription Apps | Subscription benchmarks | Health & Fitness download-to-trial, trial-to-paid, hard paywall vs freemium, trial cancellation timing |
| Sensor Tower State of Mobile 2026 | App market trends | 2025 consumer spend, non-game IAP revenue shift |
| AppsFlyer State of App Marketing 2025 | Paid UA trend | App install ad spend and non-game paid UA market context |
| Liftoff Mobile Ad Creative Index 2025 | Creative trends | UGC creative as differentiator for top apps |
| Apple Custom Product Pages / PPO | Store conversion | Up to 70 custom product pages; product page testing |
| Google Play Custom Store Listings / Experiments | Store conversion | Audience-specific listings and listing experiments |
| RevenueCat web-to-app funnels | Funnel strategy | Quiz/landing-page education, attribution, segmentation |
| AppsFlyer State of App Marketing in Asia 2025 | APAC UA / market context | Asia app economy +150% over six years; 2024 UA spend near $70B; shift from volume to performance |
| AppsFlyer Vietnam 2025 | Vietnam market context | Dataset of 13.6B installs from 2017-2025; Vietnam moving from scale to value |
| AppsFlyer India 2025 | India market context | Installs +816% since 2017; Android spend pressure, iOS budget growth, fraud risk |
| DataReportal Singapore / Korea / Vietnam / Taiwan 2025-2026 | Country platform context | Internet/social reach; TikTok, KakaoTalk, Zalo, LINE reach |
| LY Corporation LINE global data | Messaging rails | LINE MAU in Japan, Taiwan, Thailand; important for referral/share mechanics |
| Singapore MOH / HPB / Straits Times | Singapore health app and nutrition context | LumiHealth, Healthy 365, Nutri-Grade sodium/saturated fat expansion |
Case Studies
| App | Category | Priority | Source Status |
|---|---|---|---|
| Cal AI | Health / nutrition | High | TechCrunch + Business Insider sourced |
| Simple | Health / fasting / weight loss | High | Company press via Fitt Insider sourced; treat ARR as self-reported |
| Ladder | Fitness | High | BusinessWire funding release + RevenueCat interview sourced |
| Runna | Fitness / training | High | Strava acquisition announcement sourced |
| Yuka | Food/product scanner | High | Company source; second-pass press metrics useful |
| Flo | Women's health | High | Company/newsroom source; second-pass valuation/MAU press source useful |
| BetterMe | Health / fitness | Medium | Pattern covered through web-to-app/funnel sources; needs deeper direct source |
| Finch | Wellness / self-care | Medium | Useful but not deep in first draft; needs second-pass source |
| Tiimo | Productivity / neuroinclusive planning | Medium | Apple App Store Awards + App Store listing sourced |
| Partiful | Social / events | Medium | Business Insider Apple Invites competitor source |
| Clyx | Social / events | Medium | TechCrunch funding/growth source |
| Tea | Social / dating safety | Risk case | TechCrunch removal/privacy source |
| Neon | AI / data marketplace | Risk case | TechCrunch data exposure source |
| Cluely | AI assistant | Risk case | Business Insider funding/controversy source |
| ChatGPT mobile | AI utility | Medium | Appfigures spend estimate source |
| Perplexity mobile | AI search | Medium | Lightly sourced; needs second-pass metrics |
| Duolingo | Education / subscription | Medium | Used mainly as benchmark pattern, needs direct second-pass source |
| Strava | Fitness community | Medium | Strava/Runna acquisition sourced |
| Whoop | Wearable / health | Medium | Deferred from first draft |
| Shotsy | GLP-1 companion | High | Identified as needed second-pass GLP-1 comparator |
| Healthify | India nutrition / AI coaching | High for Asia | Google Play/App Store + funding source; local food database lesson |
| Speak | AI education subscription | Medium for Asia | TechCrunch + Forbes; Korea/Japan/Taiwan localization lesson |
| LumiHealth / Healthy 365 | Singapore public health | High for Singapore | MOH/HPB/Straits Times; health-app trust and gamification lesson |
| DeepSeek | China AI utility | Medium | TechCrunch; app-store shock/capability proof lesson |
| RedNote / Xiaohongshu | China social/lifestyle | Medium | The Verge; Chinese-language lifestyle/community content lesson |
| KakaoTalk / LINE / Zalo | Messaging rails | High for APAC channels | DataReportal/LY sources; referral/share infrastructure |
Asia Quality Review (2026-06-16)
Main finding: the report was much stronger after the 2026-06-15 codebase pass, but still read like a US-iOS launch memo. The weak spots were:
- Channel bias toward TikTok/Meta/Apple Search Ads; underweighted Kakao, Naver, LINE, Zalo, WhatsApp, and Google Play custom listings.
- Platform bias toward iOS despite Android-first project history and APAC Android reach.
- LTV/CAC bias from North American subscription benchmarks; APAC payer LTV can be real but lower and more uneven.
- Food-data bias: no enough focus on hawker meals, Korean stews/banchan, sauces, soups, shared plates, rice/noodle bases, and drinks.
- Country grouping bias: "Asia" was treated as one market instead of Singapore, Korea, Taiwan, Vietnam, India, and mainland China having different launch mechanics.
- Regulatory/distribution gap: zh-CN was not clearly separated from mainland China readiness.
Working recommendation after this pass:
- Singapore = first APAC proof market.
- Korea = second only after Korean food-scan trust and Korean-native store copy.
- Taiwan = later zh-TW/LINE test.
- Vietnam = content/community learning before paid subscription scale.
- India = useful case-study market but not a first launch without Indian food coverage and localized pricing.
- Mainland China = explicitly out of first-launch scope.
Verified Product & Cost Facts (grounded in the repo, 2026-06-15)
These are checked against the codebase, not inferred — they anchor the report's economics and funnel sections. The earlier draft was written without reading the product and got the funnel and measurement sections wrong.
Unit economics (firm)
| Input | Value | Source |
|---|---|---|
| Meal scan COGS | $0.0036/scan (1441 in / 959 out @ Gemini 3 Flash $0.50/$3.00 per 1M) | prod api_call_logs, 109 scans Apr–Jun 2026; memory ai-cost-per-scan; ALGORITHMS.md cost section |
| AI COGS/Pro-user/yr | ~$1.30 (1/day) · ~$4 (3/day median) · ~$8 (6/day) · ~$26 (20/day cap) | same |
| Non-AI infra/user/yr | ~$1–3 (estimate, labeled — needs real infra-cost-per-MAU pull) | not yet measured |
| All-in COGS/paying user/yr | ~$8 median → ~90% gross margin | derived |
| Price | Monthly $9.99 / Annual $79.99, annual pre-selected, 3-day trial once/user | CLAUDE.md, apps/web/messages/en.json |
| Store cut | 15% planning base (Apple Small Business + Google first-$1M); 30% sensitivity | Apple/Google policy |
| Blended payer LTV | ~$49 conservative / ~$70 mid / ~$85 optimistic (60/40 annual/monthly) | derived; renewal is the only free variable |
| Derived payer-CAC ceiling | target ≤ $20–25 (3:1), tolerate ≤ $35 (2:1), $50 = yr-1 breakeven wall | LTV ÷ ratio |
| Implied CPI for $23 payer CAC | ~$1.20 (at 13% install→trial × 40% trial→paid) | back-solve — why paid UA can't lead |
Key takeaway: cost is knowable to the cent; the only thing that moves the CAC ceiling is churn, so measure renewal early and stop refining cost guesses.
Funnel / product grounding (firm)
- Shipped first-session order: onboarding → Plan Loading → Food Lens Reveal →
Sample Meal Picker → Sample Meal Result (running totals, FREE, pre-paywall)
→ Paywall → First Scan Prompt (post-payment; OS-notif prompt fires here).
SampleMealResultScreen.tsxrenders macros + per-condition lens readings with no payment. The earlier draft's "paywall-first" recommendation was a regression. - Analytics events already exist (
apps/mobile/src/utils/analytics.ts): first_scan_completed, scan_result_viewed, close_gaps_viewed, second_scan_completed, paywall_viewed, condition_selected, cancellation_* with days_since_trial_start. Don't tell James to "add" these. - Attribution is the real gap:
sourcehardcoded'organic'(IntroCarouselScreen.tsx: "paid + referral sources will hook in when attribution lands"); no MMP. Creator CAC is unmeasurable until this ships. - GLP-1 companion is free forever (D93) → wedge audience == free tier == lowest-converting segment. Acquisition ≠ monetization segment.
- Platform/locale: ships iOS + Android (project notes: Android-first), 5 locales EN/zh-CN/zh-TW/KO/VI. Report assumed US-iOS — flag the scope.
- Prod AI provider = Gemini 3 Flash Preview (verified 2026-06-16).
.do/app.yamlsetsAI_PROVIDER=gemini+GEMINI_MODEL_OVERRIDE=gemini-3-flash-preview. The committedapps/api/.envsaysanthropicbut that's the LOCAL default — the DO deployment config is the prod source of truth. (Corrects an earlier in-session flag that read.envand worried prod was on Claude.) → the §5 Gemini COGS rate is correct, and the Asian-food advantage IS live. - Asian-food bench evidence (D66): Gemini 3 Flash Preview chosen because
Haiku failed on Asian foods; 6-photo bench cleared Korean gimbap-with-Spam (prior
"salmon" miss) + banchan; "lumping appeared on multi-bowl photos" (strict-decomp
prompt rule added). Breadth beyond Korean/Western dishes UNVALIDATED — spot-test
laksa/cai fan/pho/jjigae/dim sum/bubble tea per launch market.
ai.ts:8comment still says "Gemini 2.5 Flash" (doc drift vs the 3-Flash prod override).
GLP-1 ad-policy constraint (channel-defining)
- Meta: prohibits prescription-drug-implying ads + restricts weight-loss creative.
- TikTok: prohibits weight-management / Rx-adjacent ads; Spark Ads inherit review.
- ⇒ GLP-1 = organic/seeding/ASO/owned-web only. No paid amplification. Paid spend belongs to non-GLP-1 wedges. Verify current platform policy at launch.
Trial-copy debt (verified, already tracked)
- Dead "3 lifetime AI meal scans / no credit card" model still in
PRODUCT.md, flaggedPROGRESS.md:1863; orphanedlogMeal.trial*locale keys. Reconcile before launch.
Verification Results (web-checked 2026-06-15)
| # | Claim | Verdict | Correct value / caveat |
|---|---|---|---|
| 1 | Sensor Tower $167B / +10.6% / non-game surpassed games | AUDITED | exact |
| 2 | Hard paywall $3.09 vs freemium $0.38 D60 RPI | AUDITED | RevenueCat SoSA 2026, 115k+ apps |
| 3 | H&F 6.9% / 37.7% / 51.4%+ | AUDITED | but 37.7% is blended; ≤4-day trial = 25.5%, 5–9 day = 37.4%. Trace's 3-day trial → use ~25.5% |
| 4 | 55.4% of 3-day cancels on Day 0 | AUDITED | 84% by end of Day 1 |
| 5 | AppsFlyer $78B install spend 2024 | ADJUSTED | 2024 was ~$65B ex-China; $78B is a 2025 figure; ~$95B projected 2025 |
| 6 | Cal AI 15M dl / $30M ARR / acq Mar 2026 | AUDITED | company-reported revenue; deal closed Dec 2025, announced Mar 2 2026, ~$50M |
| 7 | Simple $160M ARR 2025 | AUDITED (self-reported) | founder-stated at Oct 2025 Series B; 700k subs; not independently verified |
| 8 | Yuka 80M users | AUDITED | cumulative registered users, not MAU; ~25M US |
| 9 | Liftoff UGC key differentiator | AUDITED | NO direct UGC-uplift %; the 16× stat is playables, not UGC — don't attach it |
| 10 | Tiimo AOTY / Strava-Runna / ChatGPT $3B | AUDITED | Strava = agreement Apr 2025, terms undisclosed; ChatGPT $3B cumulative Dec 2025 |
New sources added to the report this pass: trial-to-paid by length (25.5% ≤4d vs 37.4% 5–9d, RevenueCat/Adapty); AI apps +41% RPU / +30% churn (RevenueCat); 95% of annual cancellers never return (RevenueCat SoSA 2026); Adapty H&F benchmarks; ChatGPT $2.91/install RPI (Appfigures).
Tag convention now used inline in the report: [audited] / [self-reported] /
[estimated].
Competitor Teardown Data (web-checked 2026-06-15)
Pricing/positioning (annual / monthly): Trace $79.99/$9.99 · Cronometer ~$55/$10.99
· MyFitnessPal $79.99/$19.99 · Cal AI $29.99/$2.99 · MacroFactor $71.99/$11.99 ·
Lose It! $39.99/— · Lifesum ~$45/$9.99–12.99 · Shotsy $49.99/$9.99 · Noom
subscription+own-Rx $119–129.
- Trace sits at the TOP of the price band (tied with MFP $79.99/yr) — only the condition-lens interpretation justifies the premium; the scan alone can't.
- Cronometer = the one philosophical neighbor; measurement instrument (84 nutrients, manual targets) vs Trace's interpretation instrument. ~15M users (self-reported). No automated condition lens.
- Calorie-first pack (MFP/Cal AI/Lose It/Lifesum/MacroFactor) = wrong philosophy, but proves photo-scan is a commoditized front door + Cal AI is the GTM lesson (~15M dl, ~$35M ARR; TikTok creator-led + sequenced paid).
- Whitespace (condition-lens + running-totals + no-judgment) = real but narrow: positioning moat not technical; niche-of-niche TAM; distribution (demoing interpretation) is the hard problem.
GLP-1 Field Data (web-checked 2026-06-15)
- Shotsy — direct precedent for free-wedge→paid; ~100K dl (measured; its "1M+" is self-reported, unreliable). Paywalls the level curves Trace gives FREE; upsells dose tracking, not nutrition → Trace's nutrition bridge is differentiated.
- Noom — the one to watch: GLP-1 Companion + protein logging + Muscle Defense™; but weight-loss-framed, coaching-heavy, drug-bundled ($119–129 own Rx).
- Caloria — closest philosophical match (endocrinologist-built, metabolic steadiness) but ~20K dl (self-reported).
- Telehealth-bundled (Embla ~€150/mo+Rx · Voy £94–239/mo · Found $349–699/mo) = app is a retention wrapper around a prescription; different business.
- "Nutrition steadiness on GLP-1, interpreted, non-weight-loss" = genuinely open lane; Noom is the credible mover toward it.
- TAM: anchor on ~25–30M US adults who've tried GLP-1 (KFF-derived). No credible GLP-1-app market size exists; reject vendor "$Xbn / 280M users" figures (those reports measure supplements, not software).
Ad-policy (the organic-only thesis — confirmed + worse)
- Meta: Rx weight-loss ads need prior written permission + geo-restriction (Health & Wellness ad standard); 35-state-AG coalition (Dec 2025) pushing tighter.
- TikTok: prohibits weight-management / Rx ads; bans "GLP" in product branding; Spark Ads inherit review.
- NEW + load-bearing: TikTok May 2026 guidelines suppress weight-loss- medication content ORGANICALLY, not just paid → GLP-1 is doubly constrained. Frame GLP-1 around nutrition/protein/companion, never weight loss.
- Nuance: Trace advertises a free nutrition companion, not a drug — a nutrition-steadiness ad that never names the med/weight-loss may clear review; worth a small policy-probe, don't bet the launch on it.
GTM Benchmarks (web-checked 2026-06-15)
- Creator seeding: gift+affiliate (no upfront) converts nano/micro only; Cal AI
ran 150+ retained creators ~4 posts/mo (the paid version). UGC $150–300/asset;
health affiliate 5–30%; paid CPI N.America $2.50–5.00. FTC
#admandatory, enforcement up on small creators 2025–26; brief experience not outcomes. - ASO: condition nouns OK; verbs (manage/lower/diagnose) trip Apple 1.4.1; Google requires "not a medical device / consult a professional" disclaimer. NEVER drug brand names in metadata (Apple 5.2.1; Novo 130+ suits 2025) — use "GLP-1". Screenshots = top free lever (+9pp); store-metadata localization +26–30% (Trace already has in-app locales); CPP +156% on referred traffic (AppTweak sober +5.9–8.6%) — needs inbound; ASA popularity = free keyword tool.
- Web-to-app: H&F top performers 23%+ download-to-trial; Noom quiz = 113 screens, email gate ~33%, quiz IS the demo. Trace's free sample-demo is a cheaper show-don't-tell alternative — don't blindly copy Noom.
- Lifecycle: 55% of 3-day cancels Day 0; 82% of H&F trials start on download day; push+in-app carry it, email secondary (open ~48%/click ~1.5%); pause>cancel; 95% of annual cancellers never return → one win-back email only; ~5-min TTV rule.
- Referral: two-sided give-get (91% of successful programs); WhatsApp+SMS ~90% of sharing; mature 20–35% of installs, referred +37% D30 — but compounds AFTER a base; free month > cash.
- SEO: Trace's 976×12 data is a programmatic moat BUT YMYL needs a credentialed (RD/MD) reviewer byline (Sept 2025 update penalized missing author creds); ~30–40% of permutations have volume; 12–24 mo to mature; organic CAC ~3× < paid once mature.
- Highest-EV experiment: test 5–9 day trial (≈+10–15 pts trial-to-paid).
Key source URLs (for the report's source appendix)
- RevenueCat SoSA 2026: https://www.revenuecat.com/state-of-subscription-apps/
- Sensor Tower SoM 2026: https://sensortower.com/blog/state-of-mobile-2026
- AppsFlyer install spend: https://www.appsflyer.com/blog/trends-insights/app-install-ad-spend/
- AI apps +41%/−churn: https://ppc.land/ai-apps-earn-41-more-per-user-but-churn-30-faster-revenuecat-finds/
- 95% cancellers: https://9to5mac.com/2026/05/27/new-report-shows-annual-app-subscribers-rarely-return-after-they-cancel/
- Cal AI growth: https://growthcurve.co/three-engines-and-an-exit-the-cal-ai-growth-playbook
- Shotsy funding: https://siliconflorist.com/2025/02/20/shotsy-lands-2-million-for-weight-loss-shot-tracking-app/
- Noom Muscle Defense: https://www.noom.com/in-the-news/noom-adds-muscle-defense-to-its-glp-1-companion-program-to-help-prevent-muscle-mass-loss-while-taking-glp-1s/
- Meta health/wellness ad standard: https://transparency.meta.com/policies/ad-standards/restricted-goods-services/health-wellness/
- TikTok weight-mgmt ad policy: https://ads.tiktok.com/help/article/tiktok-ads-policy-weight-management
- TikTok May 2026 organic suppression: https://rollingstone.com/culture/culture-features/ozempic-influencers-tiktok-weight-loss-guidelines-1235029891/amp
- Apple CPP / ASO benchmarks: https://www.apptweak.com/en/aso-blog/aso-app-store-trends-benchmarks-report
- Google Play Health Content policy: https://support.google.com/googleplay/android-developer/answer/16679511
Known gaps (next pass)
- No clean public quiz-step visit→install→trial benchmark.
- No sourced health-app-specific organic-vs-paid dollar CAC (only the ~3× ratio).
- KFF GLP-1 user figure cited via aggregator — cite KFF primary directly.
- Non-AI infra cost/MAU still an estimate — pull real numbers for the §5.2 COGS line.
Open Questions For Draft
- Which first launch wedge gives Trace the best blend of pain intensity, creator ecosystem, low claims risk, and paid conversion potential?
- Is "GLP-1 nutrition companion" the best acquisition wedge, or should the free GLP-1 companion remain a lead magnet while paid nutrition is positioned more broadly? (Sharpened: GLP-1 is free forever — what is the explicit bridge from free companion to paid food-logging, and how is it measured?)
- How aggressive can Trace be with hard paywalling when the core food logging path is Pro-gated, without suppressing first value too early? (Note: the free sample-meal demo already provides pre-paywall proof; the real test is a "few free real scans" variant, which COGS makes economically free to run.)
- What CAC threshold is sane before six-month retention is known? (Derived: ≤ $20–25 payer CAC target; the binding unknown is renewal, not cost.)
- What is the platform launch order (Android-first per project notes?) and the geo/language scope, given 5 shipped locales incl. VI?
- Is the competitive set complete without Cronometer (closest philosophical comparator) and the GLP-1 field (Shotsy, Embla, Found, Simple/Noom pivots)?