Keyword data

layoffs: daily Google search volume

EN-US · Latest daily volume 3,858 (as of 2026-11-12)

layoffs at a glance

Latest daily volume: 3,858 (as of 2026-11-12) · Locale: EN-US

  • vs ~7 days earlier: 1.9%
  • vs ~30 days earlier: 10.9%
  • vs ~1 year earlier: -32.2%

Topic groups: Economic Distress

  • In the 12 months to Nov 12, 2026, layoffs averaged 3,823 Google searches a day in the United States.
  • The busiest day was May 20, 2026, with 14,664 searches, 3.8× the daily average.
  • The 30 days to Nov 12, 2026 had 100,501 searches, up 27% from the 30 days before (78,983).
  • Wednesday is the strongest day of the week, averaging 4,949 searches, 30% above the average day.
  • The 45-day forecast expects about 1,841 searches a day, 45% below the last 30 days' average.

Every figure is calculated from this keyword's daily Google search volume series. API access is available for subscribers. Check another keyword with the free keyword search volume checker.

Overview

Track real-time interest in layoffs to monitor labor market sentiment in the United States. Yesterday's daily searches reached 3,858, while the average monthly volume is 131,178. Our dataset updates through 2026-11-12, enabling timely analysis for campaigns, product planning, market signals, risk management, PR, and forecasting.

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Why Is layoffs So Popular?

Layoffs refers to employer-initiated job terminations for economic, strategic, or organizational reasons. The term spans multiple contexts:

  • News and macroeconomics: Tracking mass job cuts, business cycles, and recession risk.
  • Company-specific events: Workforce reductions tied to earnings, M&A, restructuring.
  • Workforce and HR: Policies, WARN notices, severance, outplacement, and rehiring.
  • Career and personal finance: Job search, unemployment benefits, resume/interview help.

Search intent is predominantly informational (breaking news, affected companies, guidance), with commercial sub-intent around legal counsel, job boards, resume services, and SaaS workforce tools. Popularity rises because headlines from large employers, macro data releases, and social chatter trigger rapid surges in curiosity and need-to-know behavior.

Search Volume Trends

Interest in layoffs is highly event-driven. Daily data typically shows sharp, short-lived spikes aligned with:

  • High-profile announcements: Big Tech, finance, or retail rounds concentrate attention within hours.
  • Earnings and planning cycles: Q1/Q4 restructurings and post-earnings guidance often precede cuts.
  • Macro headlines: Rate changes, GDP prints, CPI, unemployment releases, or policy shifts move sentiment.
  • Sector contagion: One firm’s reduction can prompt searches across peers and supply chains.

The daily time series reveals volatility that a monthly average can mask—capturing the precise timing, amplitude, and decay of attention after announcements, and distinguishing isolated incidents from multi-week waves.

How to Use This Data

Daily search volumes turn noisy news cycles into measurable demand signals you can act on quickly.

For Marketing Agencies and Content Creators

  • Reactive content ops: Ship explainers, data visuals, and FAQs within the spike window to capture demand.
  • Editorial planning: Align calendar with earnings weeks and macro releases where spikes concentrate.
  • Channel mix: Time paid bursts as attention peaks; repurpose for SEO evergreen after the surge.

For DTC Brands

  • Messaging sensitivity: Adjust tone and offers during downturn chatter; emphasize value, upskilling, or savings.
  • Demand forecasting: Use spikes as leading indicators for categories like career tools, budgeting, or training.
  • Customer care: Proactively surface support resources when sentiment turns.

For Stock Traders

  • Event detection: Intraday jumps can preface headlines; corroborate with newsflow and alternative data.
  • Sector mapping: Cross-ticker spikes hint at read-through risk or efficiency drives across peers.
  • Regime context: Contrast spike magnitude and persistence against baseline to separate noise from signal.