2025 U.S. Black Friday Online Sales Record—Growth Drivers, Risks, and Context

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December 1, 2025

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2025 U.S. Black Friday Online Sales Record—Growth Drivers, Risks, and Context

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Integrated Analysis

On November 28, 2025, U.S. Black Friday online sales hit a record $11.8 billion, marking 9.1% year-over-year (YoY) growth per Adobe Analytics (tracking over 1 trillion retail site visits) [1]. Salesforce reported total Black Friday spending (online + in-store) of $18 billion (3% YoY), with luxury apparel/accessories as the top-performing category [2]. Adobe also projected $5.5 billion and $5.9 billion in online sales for the following Saturday and Sunday (3.8% and 5.4% YoY, respectively) [3].

Online sales growth (10.4% YoY, eMarketer [4]) outpaced in-store performance (1.7% YoY [4]), driven by mobile devices (55% of online sales, $6.5 billion, 10% YoY increase) and AI-assisted deal hunting (805% YoY AI tool traffic [5]). The luxury category leadership aligns with data showing the top 10% of U.S. households (earning ≥$250k) account for 48% of consumer spending (up from ~35% in the mid-1990s, Moody’s Analytics [10]).

For real growth context, October 2025 everyday goods inflation was 2.68% YoY (Numerator CPI [6]), suggesting ~6.42% real online sales growth. However, official November CPI is unavailable due to a U.S. government shutdown [7]. Additionally, U.S. credit card balances hit a record $1.23 trillion in Q3 2025 (5.75% YoY [8]), and “buy now, pay later” (BNPL) usage is projected to rise 11% YoY to $20.2 billion in holiday online sales [9], indicating some consumers rely on credit to fund purchases.

Key Insights
  1. Digital transformation momentum
    : Mobile shopping and AI-driven deal hunting are accelerating online retail growth, signaling tech’s expanding role in shaping consumer behavior [4], [5].
  2. Demand concentration risk
    : Spending concentrated among the top 10% of households makes retail sales vulnerable to shifts in high-income consumer sentiment—Deloitte reported even top earners ($200k+) plan 11% lower holiday spending [10].
  3. Credit as a spending enabler
    : Record credit card debt and BNPL growth suggest many consumers are leveraging credit to participate in holiday sales, which could strain post-holiday finances [8], [9].
  4. Data gap impact
    : The BLS shutdown delaying November CPI data creates uncertainty in real growth estimates, highlighting systemic risks to economic data accessibility [7].
Risks & Opportunities
Risks
  • Credit risk
    : High credit card debt and BNPL usage may lead to post-holiday delinquencies, especially if real income growth stagnates [8].
  • Concentrated demand vulnerability
    : Spending reliance on top earners makes sales fragile if this group cuts back [10].
  • Inflation uncertainty
    : Sticky inflation could erode disposable income for lower- and middle-income households, limiting future spending [7].
  • Post-holiday underspending
    : Reddit discussions and Deloitte data suggest potential weakness in holiday spending after Black Friday [10].
Opportunities
  • AI/tech adoption
    : AI-driven tools and mobile optimization can enhance retail efficiency and customer engagement, offering growth opportunities for tech-focused retailers [5].
  • BNPL expansion
    : Rising BNPL usage presents growth prospects for providers in the payment services sector [9].
  • E-commerce growth
    : The continued online shift benefits e-commerce platforms and digital-native retailers [4].
Key Information Summary

2025 U.S. Black Friday online sales reached a record $11.8 billion (9.1% YoY), with online growth outpacing in-store performance. Drivers include mobile shopping (55% of online sales) and AI-assisted deal-hunting. Real growth is estimated at ~6.42% based on October inflation data, though November CPI is unavailable due to a government shutdown. The record sales are accompanied by concerns about credit dependence, wealth inequality, and potential post-holiday underspending. Critical data gaps include official November inflation figures and post-holiday spending trends.

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Insights are generated using AI models and historical data for informational purposes only. They do not constitute investment advice or recommendations. Past performance is not indicative of future results.