Key details

  1. Published October 27, 2026; sample 4,964 apps, baseline September 15–October 12, 2025.
  2. Black Friday median new payers/day: +20% with detected promotions versus -16% without, relative to baseline.
  3. Six-month revenue per Black Friday buyer was about 8% lower, while six-month revenue per acquisition day rose 12% at the median promotion app.
  4. About half the average new-payer lift came from users who had installed more than three months earlier.
  5. Christmas-only promotions showed no clear acquisition advantage; New Year offers correlated with +14% versus -4%.
  6. Offers were inferred partly from successful purchases, which can miss unsuccessful campaigns; no causal conclusion.

What builders should take away

  1. Treat a Black Friday promotion as a cohort experiment, not a one-day revenue target.
  2. Segment long-time free users and lapsed subscribers rather than focusing only on new installs.
  3. Track six-month net revenue per acquisition day and refunds as well as conversion rate.
  4. Test annual-versus-monthly mix and account for discounted annual renewals that cannot yet be measured.
  5. Do not assume these observational averages predict your own app's uplift; use holdouts where possible.

What changed

On October 27, 2026, RevenueCat published an analysis of 4,964 iOS/Android subscription apps from the 2025–26 holiday season. During Black Friday week, the median app with a detected promotion acquired 20% more new paying customers per day than its September 15–October 12 baseline, compared with 16% fewer for apps without detected promotions. Six-month revenue per Black Friday buyer was about 8% lower than for baseline buyers, but the larger cohort lifted sale-week revenue per acquisition day by 12% at the median participating app. Christmas-only offers showed no clear relative advantage. New Year offers correlated with a 14% increase in new payers, versus 4% fewer for apps without detected offers. Around half the Black Friday increase came from existing free users.

Why it matters

A small subscription app deciding whether to run a holiday discount needs to know whether discounted sign-ups translate into durable revenue rather than just a spike in conversions. This study adds cohort revenue, user-age and plan-mix context missing from simple sales anecdotes. It suggests testing offers against existing free users and measuring revenue over months, while warning that the detected-promotion cohort is self-selected and promotions may coincide with higher ad spend. It does not establish that discounts caused growth.

More buyers during Black Friday, but lower value per buyer

RevenueCat observed a +20% median change in daily new paying customers at apps with detected Black Friday offers versus -16% at apps without them, relative to each app's September–October baseline. Black Friday buyers generated roughly 8% less six-month revenue per customer than baseline buyers, but more buyers meant +12% six-month revenue per acquisition day for the median participating app. Across the broader November 10–December 14 window, the median with promotions was still 1% below baseline, illustrating how the sale-week peak can overstate a full campaign's result.

Existing free users are an overlooked acquisition source

About half of the average increase in Black Friday first-time paying customers came from people who installed more than three months earlier. Annual plans represented 49% of new purchases during the sale week versus 41% in the baseline. Annual renewal outcomes were not observable within the 180-day follow-up, so the long-term value of the discounted annual cohort remains unknown.

Christmas-only discounts were weaker than New Year

Apps with offers detected only at Christmas saw a 24% drop in daily new payers against baseline, similar to the 22% drop without detected offers. Apps with New Year offers saw +14% over the period versus -4% without offers, with stronger demand in Health & Fitness and Gaming. This is an association shaped by seasonal demand, not evidence that discounting itself produced the increase.

Methodology makes this useful, but not causal

The sample contains 4,964 apps with at least 30 new payers per week in the baseline. iOS and Android versions count separately. Offers were inferred from redemptions, sale-labelled products, price changes and holiday paywall text; unsuccessful promotions can be missed, biasing the detected-offer group toward winners. Revenue is net of refunds and before store commission, with 180-day follow-up. The analysis does not randomise apps, control all ad spending or observe annual renewals.

What to watch next

  • Longer-term renewal and churn of discounted annual cohorts beyond six months.
  • Independent replications that control for advertising spend and selection bias.
  • Category-specific outcomes for utility, fitness and gaming subscription apps.

Still unclear

  • Promotion detection depends on purchase patterns, so low-performing offers may be missed.
  • Apps with offers may also have higher advertising budgets or different acquisition channels.
  • Annual renewals fall outside the six-month follow-up.
  • Sample selection excludes apps with fewer than 30 new payers a week during baseline.

Sources

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