Lifetime Brands (LCUT) Operating Expenses (2010 - 2026)
Lifetime Brands (LCUT) reported Operating Expenses of $41.52 million for Q2 2026, up 10.7% from $37.5 million a year earlier and up 7.0% from the prior quarter.
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Analysis
Lifetime Brands (LCUT) Operating Expenses (2010 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Lifetime Brands' Operating Expenses came in at $154.14 million, up 2.1% year-over-year; for FY2025, it came in at $142.77 million, down 10.7% from FY2024.
- Operating Expenses has a five-year compound annual growth rate of -1.8% (FY2020 to FY2025).
- By year, Operating Expenses came in at $159.81 million in FY2024 (+4.1%), $153.5 million in FY2023 (-1.6%), $155.97 million in FY2022 (-0.3%) and $156.45 million in FY2021 (+0.2%).
- The Q2 2026 figure ranks as the highest quarterly Operating Expenses since Q4 2024.
- Year over year, Operating Expenses gained in three of the last eight quarters, with an average year-over-year change of 0.0%.
- The high point for year-over-year Operating Expenses in five years was Q1 2026 (growth of 23.4%); the low point was Q1 2025 (a decline of 20.4%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $38.82 million (Q1 2026), $38.02 million (Q4 2025) and $35.79 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | SharkNinja | 25.65 Bn | 23.31 Bn | 860.34 Mn | 680.96 Mn |
| 2 | Somnigroup International | 13.10 Bn | 12.65 Bn | 817.20 Mn | - |
| 3 | Hni | 3.38 Bn | 2.95 Bn | 647.20 Mn | 550.90 Mn |
| 4 | Newell Brands | 2.32 Bn | 1.47 Bn | 812.00 Mn | 529.00 Mn |
| 5 | Sonos | 2.13 Bn | 1.02 Bn | 189.31 Mn | 157.78 Mn |
| 6 | Whirlpool | 2.04 Bn | -1.44 Bn | 442.00 Mn | 412.00 Mn |
| 7 | Corsair Gaming | 1.46 Bn | 1.00 Bn | 104.29 Mn | 96.68 Mn |
| 8 | Millerknoll | 1.38 Bn | 756.09 Mn | 395.60 Mn | 344.20 Mn |
| 9 | Arhaus | 1.36 Bn | 443.23 Mn | 172.07 Mn | 117.81 Mn |
| 10 | Lifetime Brands | 211.96 Mn | 176.25 Mn | 93.24 Mn | 41.52 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 41.52 Mn |
| Mar 31, 2026 | 38.82 Mn |
| Dec 31, 2025 | 38.02 Mn |
| Sep 30, 2025 | 35.79 Mn |
| Jun 30, 2025 | 37.50 Mn |
| Mar 31, 2025 | 31.47 Mn |
| Dec 31, 2024 | 43.17 Mn |
| Sep 30, 2024 | 38.77 Mn |
| Jun 30, 2024 | 38.33 Mn |
| Mar 31, 2024 | 39.54 Mn |
| Dec 31, 2023 | 38.66 Mn |
| Sep 30, 2023 | 40.21 Mn |
| Jun 30, 2023 | 35.86 Mn |
| Mar 31, 2023 | 38.76 Mn |
| Dec 31, 2022 | 41.76 Mn |
| Sep 30, 2022 | 36.46 Mn |
| Jun 30, 2022 | 38.26 Mn |
| Mar 31, 2022 | 39.49 Mn |
| Dec 31, 2021 | 40.07 Mn |
| Sep 30, 2021 | 42.04 Mn |
API Access
Lifetime Brands Operating Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=operating-expenses&ticker=LCUT&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "LCUT", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=LCUT&period=max&api_key=YOUR_API_KEY");
const data = await res.json();