1 800 Flowers Com (FLWS) Operating Expenses (2010 - 2026)
1 800 Flowers Com (FLWS) recorded Operating Expenses of $158.18 million in fiscal Q4 2026 (quarter ended Jun 28, 2026), down 9.5% from $174.84 million a year earlier and down 17.6% from the prior quarter.
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Analysis
1 800 Flowers Com (FLWS) Operating Expenses (2010 - 2026) Analysis & Trends
For FY2026 (ended Jun 28, 2026), 1 800 Flowers Com reported Operating Expenses of $698.45 million, down 18.5% from FY2025.
- Annual Operating Expenses has a five-year compound annual growth rate of -1.3% (FY2021 to FY2026).
- Across earlier fiscal years, Operating Expenses came in at $857.09 million in FY2025 (+16.3%), $736.83 million in FY2024 (-7.0%), $792.54 million in FY2023 (+1.7%) and $779.64 million in FY2022 (+4.3%).
- Quarterly Operating Expenses has ranged from $127.28 million in fiscal Q1 2026 to $588.91 million in fiscal Q2 2022 over the past five years.
- On a year-over-year basis, Operating Expenses has declined for four consecutive quarters, with growth averaging 1.6% over the last eight quarters.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 79.1% in fiscal Q3 2025, against a decline of 35.7% in fiscal Q3 2026 at the low end.
- Per Business Quant, the preceding three fiscal quarters came in at $191.85 million (Q3 2026), $221.14 million (Q2 2026) and $127.28 million (Q1 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Amazon Com | 2,686.58 Bn | 2,203.28 Bn | 104.83 Bn | 173.15 Bn |
| 2 | Home Depot | 283.87 Bn | 277.11 Bn | 16.12 Bn | 9.28 Bn |
| 3 | Tjx Companies | 145.82 Bn | 123.36 Bn | 5.07 Bn | 3.09 Bn |
| 4 | Lowes Companies | 103.43 Bn | 96.39 Bn | 8.58 Bn | 4.46 Bn |
| 5 | Ross Stores | 74.61 Bn | 57.54 Bn | 2.12 Bn | 1.02 Bn |
| 6 | Target | 71.16 Bn | 65.74 Bn | 8.94 Bn | 5.73 Bn |
| 7 | O Reilly Automotive | 69.71 Bn | 68.80 Bn | 2.52 Bn | 1.53 Bn |
| 8 | Carvana | 68.78 Bn | 60.38 Bn | 1.38 Bn | 704.00 Mn |
| 9 | Autozone | 46.23 Bn | 45.13 Bn | 2.52 Bn | 1.60 Bn |
| 10 | 1 800 Flowers Com | 159.78 Mn | -103.37 Mn | 101.81 Mn | 158.18 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 28, 2026 | 158.18 Mn |
| Mar 29, 2026 | 191.85 Mn |
| Dec 28, 2025 | 221.14 Mn |
| Sep 28, 2025 | 127.28 Mn |
| Jun 29, 2025 | 174.84 Mn |
| Mar 30, 2025 | 298.43 Mn |
| Dec 29, 2024 | 244.52 Mn |
| Sep 29, 2024 | 139.30 Mn |
| Jun 30, 2024 | 166.23 Mn |
| Mar 31, 2024 | 166.65 Mn |
| Dec 31, 2023 | 264.45 Mn |
| Oct 1, 2023 | 139.51 Mn |
| Jul 2, 2023 | 171.99 Mn |
| Apr 2, 2023 | 225.08 Mn |
| Jan 1, 2023 | 252.64 Mn |
| Oct 2, 2022 | 142.82 Mn |
| Mar 27, 2022 | 180.35 Mn |
| Dec 26, 2021 | 262.72 Mn |
| Sep 26, 2021 | 145.84 Mn |
| Mar 28, 2021 | 185.01 Mn |
API Access
1 800 Flowers Com 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=FLWS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "FLWS", "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=FLWS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();