Warby Parker (WRBY) Operating Expenses (2020 - 2026)
Warby Parker's Operating Expenses came in at $133.29 million for Q2 2026, up 12.8% from $118.13 million a year earlier and up 3.0% from the prior quarter.
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Analysis
Warby Parker (WRBY) Operating Expenses (2020 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Warby Parker reported Operating Expenses of $496.93 million, up 6.7% year-over-year; for FY2025, it was $475.92 million, up 4.2% from FY2024.
- Operating Expenses carries a five-year compound annual growth rate of 10.6% (FY2020 to FY2025).
- Going back by year, Operating Expenses was $456.95 million in FY2024 (+4.5%), $437.22 million in FY2023 (-3.3%), $452.27 million in FY2022 (-2.0%) and $461.41 million in FY2021 (+60.5%).
- The Q2 2026 figure represents the highest quarterly Operating Expenses since Q3 2021.
- Year-over-year, Operating Expenses has increased for seven consecutive quarters, with growth averaging 4.6% over the last eight quarters.
- The fastest year-over-year change in Operating Expenses over five years came in Q4 2021 (growth of 73.8%), and the weakest in Q3 2022 (a decline of 37.0%).
- Business Quant data shows WRBY's Operating Expenses at $129.37 million (Q1 2026), $117.9 million (Q4 2025) and $116.38 million (Q3 2025) in the three quarters before Q2 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Nike | 53.14 Bn | 19.13 Bn | 5.39 Bn | 4.08 Bn |
| 2 | Tapestry | 22.87 Bn | 18.83 Bn | 1.56 Bn | 1.12 Bn |
| 3 | Ralph Lauren | 21.39 Bn | 13.49 Bn | 1.44 Bn | 1.10 Bn |
| 4 | Deckers Outdoor | 11.06 Bn | 3.93 Bn | - | - |
| 5 | Lululemon Athletica | 10.32 Bn | 4.58 Bn | 1.46 Bn | 1.01 Bn |
| 6 | Levi Strauss | 7.62 Bn | 4.28 Bn | 979.10 Mn | 856.90 Mn |
| 7 | Gildan Activewear | 6.55 Bn | 5.54 Bn | 459.76 Mn | 283.86 Mn |
| 8 | Birkenstock Holding | 6.07 Bn | 4.38 Bn | 493.89 Mn | -37.80 Mn |
| 9 | Crocs | 5.90 Bn | 5.31 Bn | 700.71 Mn | 415.03 Mn |
| 10 | Warby Parker | 3.39 Bn | 2.24 Bn | 136.46 Mn | 133.29 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 133.29 Mn |
| Mar 31, 2026 | 129.37 Mn |
| Dec 31, 2025 | 117.90 Mn |
| Sep 30, 2025 | 116.38 Mn |
| Jun 30, 2025 | 118.13 Mn |
| Mar 31, 2025 | 123.51 Mn |
| Dec 31, 2024 | 112.54 Mn |
| Sep 30, 2024 | 111.48 Mn |
| Jun 30, 2024 | 114.34 Mn |
| Mar 31, 2024 | 118.59 Mn |
| Dec 31, 2023 | 108.64 Mn |
| Sep 30, 2023 | 112.50 Mn |
| Jun 30, 2023 | 108.87 Mn |
| Mar 31, 2023 | 107.22 Mn |
| Dec 31, 2022 | 102.36 Mn |
| Sep 30, 2022 | 108.09 Mn |
| Jun 30, 2022 | 118.43 Mn |
| Mar 31, 2022 | 123.39 Mn |
| Dec 31, 2021 | 122.15 Mn |
| Sep 30, 2021 | 171.64 Mn |
API Access
Warby Parker 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=WRBY&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "WRBY", "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=WRBY&period=max&api_key=YOUR_API_KEY");
const data = await res.json();