Pitney Bowes (PBI) Operating Expenses (2009 - 2026)
Pitney Bowes (PBI) reported Operating Expenses of $386.01 million for Q2 2026, down 8.7% from $422.64 million a year earlier and down 2.8% from the prior quarter.
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Analysis
Pitney Bowes (PBI) Operating Expenses (2009 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Pitney Bowes' Operating Expenses came in at $1.61 billion, down 18.4% year-over-year; for FY2025, it was $1.7 billion, down 18.2% from FY2024.
- Operating Expenses has declined for five consecutive years, with a five-year compound annual growth rate of -14.6% (FY2020 to FY2025).
- By year, Operating Expenses came in at $2.08 billion in FY2024 (-2.1%), $2.12 billion in FY2023 (-7.5%), $2.29 billion in FY2022 (-37.7%) and $3.68 billion in FY2021 (-1.5%).
- The Q2 2026 figure ranks as the lowest quarterly Operating Expenses since Q4 2022.
- Year over year, Operating Expenses has now declined in each of the last seven quarters, with an average decline of 10.1% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was Q3 2024 (growth of 14.1%); the low point was Q2 2024 (a decline of 46.3%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $396.94 million (Q1 2026), $439.25 million (Q4 2025) and $391.53 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Accenture | 122.50 Bn | 82.91 Bn | 6.13 Bn | 15.54 Bn |
| 2 | Cintas | 78.05 Bn | 77.24 Bn | 1.48 Bn | 792.12 Mn |
| 3 | Iron Mountain | 33.02 Bn | 32.53 Bn | 1.07 Bn | 1.66 Bn |
| 4 | APi | 16.68 Bn | 13.72 Bn | 703.00 Mn | 528.00 Mn |
| 5 | Rollins | 14.49 Bn | 14.04 Bn | 569.95 Mn | 877.22 Mn |
| 6 | Aramark | 14.11 Bn | 12.13 Bn | 430.34 Mn | 4.84 Bn |
| 7 | UL Solutions | 13.27 Bn | 12.05 Bn | 417.00 Mn | 267.00 Mn |
| 8 | Gartner | 11.78 Bn | 5.47 Bn | 1.19 Bn | 1.30 Bn |
| 9 | Rentokil Initial | 9.98 Bn | 3.32 Bn | - | - |
| 10 | Pitney Bowes | 2.24 Bn | 1.01 Bn | 254.70 Mn | 386.01 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 386.01 Mn |
| Mar 31, 2026 | 396.94 Mn |
| Dec 31, 2025 | 439.25 Mn |
| Sep 30, 2025 | 391.53 Mn |
| Jun 30, 2025 | 422.64 Mn |
| Mar 31, 2025 | 446.69 Mn |
| Dec 31, 2024 | 564.32 Mn |
| Sep 30, 2024 | 543.34 Mn |
| Jun 30, 2024 | 497.60 Mn |
| Mar 31, 2024 | 473.67 Mn |
| Dec 31, 2023 | 588.97 Mn |
| Sep 30, 2023 | 476.11 Mn |
| Jun 30, 2023 | 927.43 Mn |
| Mar 31, 2023 | 845.53 Mn |
| Dec 31, 2022 | -302.66 Mn |
| Sep 30, 2022 | 820.79 Mn |
| Jun 30, 2022 | 874.19 Mn |
| Mar 31, 2022 | 901.92 Mn |
| Dec 31, 2021 | 982.24 Mn |
| Sep 30, 2021 | 868.48 Mn |
API Access
Pitney Bowes 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=PBI&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "PBI", "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=PBI&period=max&api_key=YOUR_API_KEY");
const data = await res.json();