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Research Brief — Mobile App Paywall Design

Fact-check reference for the YouTube script

Confidence key SOLID — primary source located, figure verified CONTESTED — real source exists, but methodology or interpretation is disputed SHAKY — widely repeated, source is weak, absent, or circular BLACKLISTED — never repeat this claim

Cross-reference note: choice overload, defaults psychology (Madrian & Shea, Johnson & Goldstein), Hick's Law, 7±2, social login, and form-field research are already covered in decision-fatigue-research-brief.md. This brief does not rehash them; it points to them where the paywall application depends on that spine (plan defaults → that brief's §5; number of plans → its §1 Chernev moderators).

️ PART 1 — CORRECTIONS

Six popular claims in paywall content that are wrong, mis-told, or missing their caveats.

1. "Hard paywalls convert 5x better — so use a hard paywall"

The 5x number is real but the advice is a category error. RevenueCat's State of Subscription Apps 2026 (115,000+ apps, $16B tracked revenue, 1B+ transactions): median download-to-paid at day 35 is 10.7% for hard-paywall apps vs 2.1% for freemium — and coverage of the report adds an ~8x revenue-per-install gap at day 60. SOLID as telemetry — https://www.revenuecat.com/state-of-subscription-apps

But this is correlational, not causal. Apps don't randomize their access model. Hard-paywall apps self-select: they tend to be utility/health apps with instant clear value; freemium apps are chasing scale, network effects, and word of mouth. RevenueCat itself frames freemium as "the right call when free users drive word of mouth, network effects, or long-term brand scale," and notes year-one retention for the two models is nearly identical. No randomized test of hard vs soft paywall at the same app has ever been published. The honest statement: hard paywalls monetize a smaller funnel harder. Whether that's more total revenue depends on what free users are worth — which the metric doesn't measure. (Same category-vs-store trap as Borle et al. 2005 in the decision-fatigue brief §8.)

2. The Economist "decoy pricing" story — The Economist never ran it

The most repeated pricing story on the internet: web-only $59, print-only $125, print+web $125; the "useless" middle option supposedly shifted everyone to the $125 bundle and boosted revenue 43%.

What's claimed Reality
"The Economist's famous pricing experiment" The Economist ran no experiment. Dan Ariely saw the ad and ran a hypothetical classroom survey on 100 MIT students (Predictably Irrational, 2008). No purchases, no revenue, n=100 students.
"The decoy effect is settled science" The lab effect (Huber, Payne & Puto 1982) is real with abstract numeric stimuli. Frederick, Lee & Baskin, "The Limits of Attraction," JMR 51(4), 2014, found the effect largely disappears with realistic products — they "failed to replicate several of the results most frequently cited as evidence." Yang & Lynn (2014, same JMR issue) reported the same.

SOLID (that the correction is right) — https://journals.sagepub.com/doi/10.1509/jmr.12.0061 On camera: "add a decoy plan" is advice built on a classroom survey plus a lab effect that dies on contact with real products. If a decoy tier works on your paywall, that's your A/B test's finding, not psychology's.

3. "Longer trials convert better" — the telemetry and the RCTs point opposite ways

RevenueCat 2025 reported the highest trial-to-paid conversion for the longest trials (17–32 days: 45.7%). Creator content quotes this as "make your trial longer." But trial length in that dataset is chosen by the app, not randomized — confident apps with sticky products pick long trials.

The only randomized evidence says the opposite for acquisition:

  • Yoganarasimhan, Hema et al., "Design and Evaluation of Optimal Free Trials," Management Science (2023; working paper 2020). Major anonymous SaaS firm, n = 337,724 users randomized to 7 / 14 / 30-day trials, outcomes tracked ~2 years. 7-day trial was the best uniform policy: +5.59% subscriptions, ~+7.9% revenue, and better retention (~+6.4%) vs the 30-day baseline. 14-day was statistically indistinguishable from 30-day. Personalized assignment beat uniform only modestly (≈6.8% vs 5.6% lift) and was fragile. SOLID — https://arxiv.org/abs/2006.13420 · https://pubsonline.informs.org/doi/10.1287/mnsc.2022.4507
  • Zhang & Duan (2025), Frontiers in Psychology 16:1568868. Image-editing freemium SaaS, n = 680,588 users, 190 countries, 3-day vs 7-day randomized, 2-year window. 7-day: trial adoption +11.1%; immediate conversion +7.3% (n.s.); delayed conversion +42.4%; overall conversion +20.9%. Mechanism: longer trials increased premium-feature engagement, but users who finished their task during the trial were less likely to subscribe (satiation/demand cannibalization). SOLID — https://pmc.ncbi.nlm.nih.gov/articles/PMC12217587/

Reconciliation for the script: short trials force the subscribe decision while motivation is hot (and both RCTs are consistent with 7 > 30); the telemetry's "long trials convert great" is survivorship — apps that can afford a 30-day trial already have retention. Saying "the data says longer trials convert better" cites a selection effect as a causal one.

4. The Blinkist "transparent paywall" story — real, but get the provenance and the metric right

The famous case: Blinkist's paywall told trial users exactly when they'd be charged and promised a reminder before trial end.

Often said Reality
"Blinkist increased conversions 23%" It was +23% trial sign-ups (paywall → trial start), not trial-to-paid. Self-reported 4% higher trial retention is the only downstream number.
"A famous study" It's a self-reported internal A/B test, first written up by Blinkist designer Jaycee Day ("Solving Trial Conversions," UX Planet, 2021), popularized by Growth.Design's teaching case and Purchasely's interview with Blinkist's Eveline Moczko. No sample size, duration, or significance ever published.
Other numbers Push-notification opt-in 6% → 74%; customer complaints −55%; user research found "forgetting to cancel and being charged" was the #1 hesitation, and 33% of cancellations happened immediately after trial start.

CONTESTED — credible, consistent across three tellings, but vendor-published with no methodology. Say "Blinkist reports," not "a study found." — https://growth.design/case-studies/trial-paywall-challenge · https://www.purchasely.com/blog/using-transparency-to-increase-your-conversion-rate-with-eveline-moczko-blinkist

5. "Freemium converts at 2–5%" stated as a law

The range circulates with rotating attribution (sometimes pinned on Kumar's HBR piece, which is a strategy article, not a benchmark study). What's actually sourced:

Figure Source Confidence
Mobile freemium download-to-paid (D35) median: 2.1%; overall all-model median 2.0% RevenueCat 2026, 115k apps SOLID (vendor telemetry, huge n)
Dropbox ~4% of free users paying (called exceptional) outside estimates, never a filing CONTESTED
Evernote conversion by tenure: 1.5% month 1 → 7% year 1 → 11% year 2 Phil Libin conference remarks, ~2011 CONTESTED — self-reported, 15 years old
Kumar, "Making Freemium Work," HBR May 2014 framework: free tier too generous → no one upgrades; too stingy → no acquisition; treat the free/premium line as a dial you re-tune, not a launch decision SOLID as attributed argument, contains no benchmark table — https://hbr.org/2014/05/making-freemium-work

On camera: give the 2.1% median with the source and year; don't say "industry standard 2–5%."

6. "$0.27/day framing" — the research is real, the boundary condition never gets mentioned

The pennies-a-day tactic has an actual paper behind it (see §1 below) — but Gourville's own follow-up shows the effect reverses at higher magnitudes: "$1/day" beats "$365/year," but when the daily number gets big enough to stop resembling petty cash, the aggregate frame wins (Gourville 2003, Marketing Letters 14(2)). A $95.88/year app shown as "$0.26/day" is inside the effect; a $30/week app shown as "$4.29/day" is arguably outside it — $4.29/day retrieves comparisons like lunch, not pennies. SOLID — https://link.springer.com/article/10.1023/A:1025467002310

Also: Apple's review practice requires the actual billed price and term to be the prominent element — per-day framing can decorate the paywall but cannot legally be the price display (see §9). Creator advice that says "show only the per-day price" is a rejection waiting to happen.

PART 2 — THE VERIFIED SPINE

§1 — Temporal price reframing (the "$0.27/day" literature)

Gourville (1998), "Pennies-a-Day: The Effect of Temporal Reframing on Transaction Evaluation," JCR 24(4): 395–408. Framing a charitable donation as "85 cents per day" got 52% compliance vs 30% for the economically identical "$300 per year" — a 22-point swing from wording alone. Mechanism (two-step): the daily frame triggers retrieval of trivial daily expenses (coffee, parking) as the comparison set; the aggregate frame retrieves large infrequent expenses. SOLID — https://academic.oup.com/jcr/article-abstract/24/4/395/1797969

Boundary (Gourville 2003, Marketing Letters): effect reverses at high magnitudes — see Correction 6. SOLID

Provenance beat: the tactic predates the paper — Time magazine and competitors field-tested per-issue vs per-year framing in the early 1980s, with per-issue offers reportedly 10–40% more effective (recounted by Dholakia, Psychology Today, 2019, citing industry tests). CONTESTED — industry lore, but good for showing "$0.27/day" is a 40-year-old magazine tactic. — https://www.psychologytoday.com/us/blog/the-science-behind-behavior/201904/the-powerful-influence-pennies-day-price-offers

Atlas & Bartels (2018), "Periodic Pricing and Perceived Contract Benefits," JCR 45(2). Peer-reviewed follow-up: periodic (per-day) framing doesn't just shrink perceived cost — it shifts attention toward the ongoing stream of benefits, increasing willingness to maintain subscriptions. The modern academic footing for per-day framing on subscription products specifically. SOLID — https://home.uchicago.edu/bartels/papers/Atlas-Bartels-2018-JCR.pdf

Vendor cases (label clearly): Mailchimp reportedly +21% annual commitment from "$29.99/month billed annually" vs "$359.88/year"; Mojo (via RevenueCat's pricing-psychology writeup) +45% new revenue per paywall impression from monthly-equivalent framing of annual billing. CONTESTED — single-company tests, no methodology published. — https://www.revenuecat.com/blog/growth/subscription-pricing-psychology-how-to-influence-purchasing-decisions

Price endings — the solid one: Anderson & Simester (2003), "Effects of $9 Price Endings on Retail Sales," QME 1(1): 93–110. Three field experiments (real catalogs, real purchases): $9 endings raised demand in all three, famously including demand rising when a dress was repriced from $34 → $39, strongest for new items, weaker when "Sale" cues were present. The rare pricing-psychology claim with genuine field-experimental backing — and why $9.99/$29.99 endings on paywalls aren't just cargo cult. SOLID — https://link.springer.com/article/10.1023/A:1023581927405

§2 — The zero-price effect: why "free trial" is categorically different from "$0.99"

Shampanier, Mazar & Ariely (2007), "Zero as a Special Price: The True Value of Free Products," Marketing Science 26(6): 742–757. The clean pair of conditions: Hershey's Kiss 1¢ vs Lindt truffle 15¢ → 73% chose the truffle. Drop both prices by one cent — Kiss free, truffle 14¢ → 69% chose the Kiss. A one-cent change flipped the majority. Free isn't a low price; it's a different category — zero-price options get an affective benefit boost, not just a cost reduction, and the effect survives real (non-hypothetical) purchase conditions in the paper. SOLID — https://pubsonline.informs.org/doi/10.1287/mksc.1060.0254 · PDF: https://web.mit.edu/ariely/www/MIT/Papers/zero.pdf

Paywall translation: "Try 7 days free" vs "First week $0.99" is not a $0.99 difference — it's a category switch. This is the legitimate science behind trial-first paywall CTAs and behind why "$0.00 due today" phrasing (Blinkist-style) is potent: it keeps the transaction in the zero-price category while stating the truth.

§3 — Trial design: the causal evidence

(Full numbers in Correction 3.) The stack, in one table:

Study Design n Headline result Confidence
Yoganarasimhan et al., Mgmt Science 2023 RCT 7/14/30-day, SaaS 337,724 7-day best: +5.59% subs, +7.91% revenue, better 2-yr retention; 14 ≈ 30 SOLID
Zhang & Duan, Frontiers in Psych 2025 RCT 3 vs 7-day, freemium SaaS 680,588 7-day: +20.9% overall conversion, driven by +42.4% delayed conversion; satiation effect if users finish their task in-trial SOLID
RevenueCat 2026 telemetry 115k apps 5–9 day trials convert 37.4% vs 25.5% for ≤4 days; 46.5% of apps now use ≤4-day trials (share rising) SOLID as description, BLACKLISTED as causal claim

The uncomfortable detail worth airing: the industry is migrating toward ultra-short trials (≤4 days now the plurality) even though those show the lowest median conversion in the same dataset — and "55% of all 3-day trial cancellations happen on Day 0" (RevenueCat 2026): users start the trial and immediately cancel to avoid the forgotten-charge trap. That behavior is the Blinkist research finding showing up in telemetry, eight years later.

Trial start timing: across categories, ~78–90% of trials start on Day 0 (Business 89.9%, Health & Fitness 82.1%, Productivity 78.0%). The paywall decision effectively happens at install. SOLID (RevenueCat 2026)

§4 — Hard vs soft paywalls, and what hardening does to audiences

Mobile telemetry — Correction 1 numbers (10.7% vs 2.1% D35; retention converges by year one). SOLID as telemetry.

The news-industry natural experiments are the closest thing to causal evidence on hardening a paywall:

  • Chiou & Tucker (2013), "Paywalls and the demand for news," Information Economics and Policy 25(2): 61–69. A media publisher field-tested paywalls at local news sites; comScore panel data. Visits fell 51% after the paywall, with a far larger collapse among 18–24-year-olds (widely summarized as near-total loss of that cohort). SOLID — https://www.sciencedirect.com/science/article/abs/pii/S0167624513000097 · PDF: https://www.oxy.edu/sites/default/files/assets/Economics/Chiou/chiou_and_tucker_paywalls.pdf
  • Pattabhiramaiah, Sriram & Manchanda (2019), "Paywalls: Monetizing Online Content," Journal of Marketing 83(2): 19–36. NYT metered paywall (March 2011): unique visitors −16.8%; heavy users −57.2% vs light users −11.3% — the meter, by design, taxes your most engaged readers. Offsetting spillover: +1–4% print circulation (driven by print+digital bundling). SOLID — https://journals.sagepub.com/doi/10.1177/0022242918815163 · summary: https://www.ama.org/2019/03/07/before-you-put-up-a-paywall-read-this-study/

App translation (novel-angle material): paywall conversion is the category metric; installs, engagement, word-of-mouth are the store metric (see decision-fatigue brief §8, Borle et al.). Hardening reliably improves the first and taxes the second, and the news literature actually measured the tax: −51% traffic, youngest users first, heaviest users hit hardest.

§5 — Plan architecture, defaults, and badges

What paywalls actually look like (RevenueCat 2026, SOLID as telemetry): - Two plans is the most common configuration (41–60% of paywalls by category); 3+ plans is a minority (6–27%). The Chernev moderators (decision-fatigue brief §1) predict exactly this: uncertain preferences + time pressure → small sets with a dominant option. - Subscription revenue mix: monthly 42% · yearly 34% · weekly 20% — the "annual is where all the money is" line is false for mobile. Weekly dominates Gaming (82%); annual dominates Health & Fitness (68%); Productivity is 77% monthly. - Most common price points: $5/week, $10/month, $30–40/year (NA: $9.99/mo, $39.99/yr).

Preselected plan defaults: vendor guidance (Adapty, Superwall, RevenueCat) converges on preselecting the longest plan; circulating vendor anecdotes claim plan-mix inversions (e.g., ~37% → ~63% yearly selection; "82% of non-closing viewers kept the default"). CONTESTED — no published methodology for any of these numbers; the mechanism is the best-replicated finding in the adjacent brief (defaults read as advice — Madrian & Shea, §5 there). Use the peer-reviewed defaults literature for the claim, vendor anecdotes as illustration only.

"Most popular" badges: no peer-reviewed test on paywalls exists. The nearest systematic evidence cuts the other way: DoWhatWorks' review of ~15 large-company A/B tests found social-proof additions on plan-selection pages tended to lose (Dropbox logos, Glofox testimonials among the losers). CONTESTED — https://www.dowhatworks.io/blog/should-you-add-social-proof-on-pricing-pages. A "most popular" badge is better understood as a default/dominant-option marker (Chernev: a dominant option dissolves overload) than as social proof — which also predicts when it fails: when it contradicts the visible price logic.

§6 — Endowment, loss aversion, and the "reverse trial"

The honest state: the claim "having-then-losing features converts better than never-having (endowment effect)" has no direct peer-reviewed test in software. The endowment effect itself is real (Kahneman, Knetsch & Thaler 1990 — mugs, WTA ≈ 2×WTP), but every "free trials convert 2–5x better than freemium because loss aversion" article is blog arithmetic transplanting a coffee-mug lab result onto SaaS funnels. SHAKY as a mechanism claim for paywalls.

What actually exists: - Zhang & Duan (2025): more premium-feature engagement during trial → higher conversion, but users who completed their job-to-be-done in-trial converted less (satiation beats endowment when the need is episodic). SOLID — the closest peer-reviewed look at the mechanism, and it complicates the loss-aversion story rather than confirming it. - Trial vs no-trial telemetry gaps (RevenueCat) are confounded by everything in Correction 1.

On camera: frame reverse trials as "plausible, loss-aversion-flavored, untested" — an honesty beat no other creator will deliver.

§7 — What subscribers don't know they're paying (the transparency case)

  • C+R Research (May 2022, n=1,000 US consumers, self-report): asked to guess their monthly subscription spend, average guess $86; their itemized actual average $219 — ~2.5x underestimate. 42% said they'd forgotten they were paying for at least one unused subscription. CONTESTED — market-research vendor, small n, self-report; but directionally corroborated by banking-data studies. — https://www.crresearch.com/blog/subscription-service-statistics-and-costs/ · https://www.cnbc.com/2022/06/02/consumers-spend-133-more-monthly-on-subscriptions-than-they-realize.html
  • This is the demand-side context for Blinkist's finding (forgotten-charge fear = #1 trial hesitation) and RevenueCat's Day-0 cancellation stat. Three independent source types — user research, telemetry, consumer survey — triangulate the same anxiety.

§8 — The rules of the game (Apple) and the regulatory whiplash

Apple App Review Guideline 3.1.2 + review practice (SOLID for the guideline text; CONTESTED where noted): - Auto-renewable subscriptions must deliver ongoing value; minimum period 7 days, available across the user's devices. - Paywalls must show the actual billed price and period prominently; rejections for insufficient price prominence are routine (the specific "use ≥16pt" advice is developer-community lore, not written Apple policy — label it as such). - A "free trial" label must be backed by a real StoreKit introductory offer (duration, price, and what's lost at trial end disclosed) — not a home-rolled timer. - Terms of Use and Privacy Policy links must be in the app's paywall UI; a Restore Purchases path is expected. - Hard paywalls are allowed. "Apple rejects hard paywalls" is a myth; what gets rejected is hiding price, fake trials, and missing links. — https://developer.apple.com/app-store/review/guidelines/ · rejection patterns: https://www.revenuecat.com/blog/growth/the-ultimate-guide-to-app-store-rejections

FTC "click-to-cancel": the Negative Option Rule (finalized Oct 2024 — separate consent for auto-renewal, cancellation as easy as signup) was vacated in its entirety by the Eighth Circuit on July 8, 2025 — six days before its compliance deadline — on procedural grounds (FTC skipped the required preliminary regulatory analysis under FTC Act §22). ROSCA and state auto-renewal laws (notably California's) still apply, and the FTC has moved to restart rulemaking. SOLID — https://www.cooley.com/news/insight/2025/2025-07-11-click-to-cancel-just-got-cancelled-eighth-circuit-vacates-entirety-of-ftcs-negative-option-rule — check status again before publication; this is live.

PART 3 — HOOK CANDIDATES

  1. "One cent flipped the room." At 1¢ vs 15¢, 73% chose the Lindt truffle. Cut both prices by a single cent — free vs 14¢ — and 69% chose the Hershey's Kiss instead. (Shampanier, Mazar & Ariely 2007, Marketing Science.) SOLID
  2. "52% vs 30% — for the same $300." Ask for "85 cents a day" and half say yes; ask for "$300 a year" and less than a third do. (Gourville 1998, JCR.) SOLID
  3. "337,724 people were randomized into a trial-length experiment. The 7-day trial beat the 30-day — on subscriptions, revenue, AND two-year retention." (+5.59% subs, +7.91% revenue; Yoganarasimhan et al., Management Science.) SOLID
  4. "People guess they spend $86 a month on subscriptions. The real number averages $219." (C+R Research 2022 — label as a consumer survey, n=1,000.) CONTESTED but honest if labeled.
  5. "The news industry hardened its paywalls and measured what apps won't: traffic fell 51%, and the youngest readers almost entirely vanished." (Chiou & Tucker 2013.) SOLID
  6. "55% of 3-day-trial cancellations happen the day the trial starts." Users have learned to defuse your trial before it can charge them. (RevenueCat 2026, 115k apps.) SOLID (telemetry)

PART 4 — NOVEL-ANGLE CANDIDATES

  1. The RCT vs telemetry split-screen. Every creator quotes RevenueCat ("longer trials convert better"); the only two large randomized experiments ever run say shorter wins for acquisition. Teaching viewers why both are true (endogeneity/survivorship) is a signature-move segment — and no paywall video on YouTube currently makes it.
  2. The Day-0 cancellation arms race. Blinkist's 2018-era user research (fear of forgotten charges), RevenueCat's 2026 telemetry (55% of 3-day-trial cancels on Day 0), and C+R's $86-vs-$219 gap are the same phenomenon measured three ways across eight years: users now treat trials as adversarial. Transparency isn't ethics theater — it's the equilibrium response. Blinkist got there early and the numbers (even self-reported) moved in every direction you'd predict.
  3. Per-day framing is now a regulated design pattern. Gourville gives the psychology, Gourville 2003 gives the boundary where it backfires, and Apple's price-prominence enforcement legally caps how far you can push it. "The $0.27/day trick has a 1998 paper, a failure condition, and a compliance ceiling" is a three-act beat nobody else has assembled.
  4. The category-vs-store metric transplant. Borle et al. (decision-fatigue brief §8) showed cutting assortment improved every category and hurt the store. Paywall hardening is the same trap: conversion (category) up, audience formation (store) down — and the news-industry literature (−51% visits, −57% heavy-user engagement at NYT) is the measured version of the cost mobile apps never see in their dashboards.
  5. "Free" is a category, not a price — using zero-price theory to explain why trial-first CTAs, "$0.00 due today" microcopy, and the trial/no-trial paywall split behave so differently from a 99-cent intro offer, then landing the honesty beat that the endowment-effect justification for reverse trials remains untested.

PART 5 — THE BLACKLIST

Never say these on camera:

Claim Why
"The Economist tested decoy pricing" / "the decoy raised revenue 43%" Ariely's hypothetical classroom survey, n=100 MIT students. The Economist ran nothing. Decoy effect fails to replicate with real products (Frederick, Lee & Baskin, JMR 2014).
"Hard paywalls convert 5x better, so go hard" Correlational telemetry; access models are self-selected; year-1 retention converges; free-user value unmeasured.
"Longer free trials convert better" (as causal) Selection effect in vendor data. Both large RCTs found 7-day beats 30-day.
"Studies show multi-page paywalls convert 37% better" Superwall telemetry (40M opens, Feb–May 2026) is real but correlational; multi-page was 24% of opens, zero-transaction paywalls excluded, no app controls. "Superwall's data shows" is fine; "studies show" is not.
"Blinkist increased conversions 23%" (unqualified) It was trial sign-ups, self-reported, no n or significance ever published.
"Freemium converts at 2–5%, that's the standard" Rotating attribution, no primary benchmark. Use RevenueCat's 2.1% D35 median with the year.
"Loss aversion means reverse trials convert 2–5x better" No study exists. Blog arithmetic on top of a coffee-mug experiment.
"The endowment effect is why free trials work" (as fact) Closest peer-reviewed evidence (Zhang & Duan 2025) shows satiation can dominate — mechanism unsettled.
"Apple requires 16pt font for prices" Community folklore. The written rule is prominence/legibility; 16pt is a workaround heuristic.
"Apple bans hard paywalls" False. Compliant hard paywalls pass review daily.
"Delaying the close button boosts revenue by X%" No published test anywhere — vendor blogs recommend the tactic with zero data, and refund/churn/review costs are unmeasured. Also squarely dark-pattern territory.
"Consumers waste $133/month" (unattributed) Real number, but it's one 1,000-person self-report survey (C+R 2022). Attribute or drop.
"Annual plans are where all mobile revenue is" RevenueCat 2026: monthly 42% > yearly 34% > weekly 20%; weekly is 82% of Gaming.
"Click-to-cancel is the law now" Vacated by the Eighth Circuit, July 8, 2025. ROSCA/state law still bind — but the FTC rule is (currently) dead. Verify again pre-publish.
Any "average paywall converts at X%" without placement, trial, and category qualifiers Adapty/Superwall/RevenueCat figures vary 5–10x by placement and trial config; a single number is meaningless.

PART 6 — PRIMARY SOURCES

Pricing psychology Gourville (1998), Pennies-a-Day, JCR 24(4) — https://academic.oup.com/jcr/article-abstract/24/4/395/1797969 Gourville (2003), magnitude boundary, Marketing Letters 14(2) — https://link.springer.com/article/10.1023/A:1025467002310 Atlas & Bartels (2018), Periodic Pricing, JCR 45(2) — https://home.uchicago.edu/bartels/papers/Atlas-Bartels-2018-JCR.pdf Shampanier, Mazar & Ariely (2007), Marketing Science 26(6) — https://pubsonline.informs.org/doi/10.1287/mksc.1060.0254 · PDF https://web.mit.edu/ariely/www/MIT/Papers/zero.pdf Anderson & Simester (2003), $9 endings, QME 1(1) — https://link.springer.com/article/10.1023/A:1023581927405 Frederick, Lee & Baskin (2014), The Limits of Attraction, JMR 51(4) — https://journals.sagepub.com/doi/10.1509/jmr.12.0061 Huber, Payne & Puto (1982), original attraction effect, JCR 9(1)

Free trials Yoganarasimhan et al., Design and Evaluation of Optimal Free Trials, Management Science — https://pubsonline.informs.org/doi/10.1287/mnsc.2022.4507 · preprint https://arxiv.org/abs/2006.13420 Zhang & Duan (2025), Frontiers in Psychology 16:1568868 — https://pmc.ncbi.nlm.nih.gov/articles/PMC12217587/

Freemium Kumar (2014), Making "Freemium" Work, HBR 92(5) — https://hbr.org/2014/05/making-freemium-work · PDF https://vineetkumars.github.io/Papers/Freemium_May2014_Kumar.pdf

News paywalls Chiou & Tucker (2013), Information Economics and Policy 25(2) — https://www.sciencedirect.com/science/article/abs/pii/S0167624513000097 · PDF https://www.oxy.edu/sites/default/files/assets/Economics/Chiou/chiou_and_tucker_paywalls.pdf Pattabhiramaiah, Sriram & Manchanda (2019), Journal of Marketing 83(2) — https://journals.sagepub.com/doi/10.1177/0022242918815163 · AMA summary https://www.ama.org/2019/03/07/before-you-put-up-a-paywall-read-this-study/

Vendor telemetry (label as such on camera) RevenueCat, State of Subscription Apps 2026 (115k apps, $16B) — https://www.revenuecat.com/state-of-subscription-apps RevenueCat, State of Subscription Apps 2025 — https://www.revenuecat.com/state-of-subscription-apps-2025 Superwall, multi-page onboarding paywalls (40M opens) — https://superwall.com/blog/new-postmulti-page-onboarding-paywalls-convert-37-better-than-single-page-heres-why DoWhatWorks, social proof on pricing pages — https://www.dowhatworks.io/blog/should-you-add-social-proof-on-pricing-pages C+R Research, subscription cost survey (2022) — https://www.crresearch.com/blog/subscription-service-statistics-and-costs/ · CNBC coverage https://www.cnbc.com/2022/06/02/consumers-spend-133-more-monthly-on-subscriptions-than-they-realize.html

Case studies Growth.Design, Blinkist trial paywall — https://growth.design/case-studies/trial-paywall-challenge Purchasely × Blinkist (Eveline Moczko interview) — https://www.purchasely.com/blog/using-transparency-to-increase-your-conversion-rate-with-eveline-moczko-blinkist

Rules & regulation Apple App Store Review Guidelines (3.1.2) — https://developer.apple.com/app-store/review/guidelines/ RevenueCat, guide to App Store rejections — https://www.revenuecat.com/blog/growth/the-ultimate-guide-to-app-store-rejections Eighth Circuit vacatur of FTC Negative Option Rule (July 8, 2025) — https://www.cooley.com/news/insight/2025/2025-07-11-click-to-cancel-just-got-cancelled-eighth-circuit-vacates-entirety-of-ftcs-negative-option-rule