Skillsoft (SKIL) Amortizatization of Intangibles (2021 - 2026)
Skillsoft (SKIL) reported Amortizatization of Intangibles of $21.54 million for fiscal Q2 2027 (quarter ended Jul 31, 2026), down 31.5% from $31.44 million a year earlier and down 30.2% from the prior quarter.
Skillsoft (SKIL) Amortizatization of Intangibles (2021 - 2026) Analysis & Trends
Over the twelve months ended Jul 31, 2026, Skillsoft's Amortizatization of Intangibles came in at $116.73 million, down 8.0% year-over-year; for FY2026 (ended Jan 31, 2026), it was $127.35 million, up 0.1% from FY2025.
- Amortizatization of Intangibles has a four-year compound annual growth rate of 7.3% (FY2022 to FY2026).
- By fiscal year, Amortizatization of Intangibles came in at $127.22 million in FY2025 (-16.6%), $152.51 million in FY2024 (-13.7%), $176.69 million in FY2023 (+84.2%) and $95.92 million in FY2022.
- The fiscal Q2 2027 figure ranks as the lowest quarterly Amortizatization of Intangibles in data going back to fiscal Q1 2023.
- Year over year, Amortizatization of Intangibles gained in two of the last eight quarters, with an average decline of 7.9%.
- The high point for year-over-year Amortizatization of Intangibles in five years was fiscal Q3 2026 (growth of 0.9%); the low point was fiscal Q2 2027 (a decline of 31.5%).
- Per Business Quant data, the three fiscal quarters before Q2 2027 came in at $30.87 million (Q1 2027), $32.23 million (Q4 2026) and $32.1 million (Q3 2026).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Amort. of Intangibles (Qtr) |
|---|---|---|---|---|---|
| 1 | Bryn | 945.00 Bn | 945.00 Bn | - | - |
| 2 | Veeva Systems | 44.24 Bn | 16.49 Bn | 695.95 Mn | - |
| 3 | Samsara | 24.06 Bn | 20.83 Bn | 392.58 Mn | - |
| 4 | Toast | 17.18 Bn | 9.85 Bn | 516.00 Mn | - |
| 5 | Ptc | 15.95 Bn | 14.76 Bn | 490.47 Mn | - |
| 6 | Duolingo | 13.49 Bn | 8.67 Bn | 216.74 Mn | - |
| 7 | Trimble | 13.41 Bn | 12.48 Bn | 674.90 Mn | - |
| 8 | Manhattan Associates | 11.90 Bn | 10.90 Bn | 168.33 Mn | - |
| 9 | Costar | 11.09 Bn | 5.06 Bn | 728.00 Mn | - |
| 10 | Skillsoft | 52.02 Mn | -322.55 Mn | 83.10 Mn | 21.54 Mn |
Historic Data
| Date | Value |
|---|---|
| Jul 31, 2026 | 21.54 Mn |
| Apr 30, 2026 | 30.87 Mn |
| Jan 31, 2026 | 32.23 Mn |
| Oct 31, 2025 | 32.10 Mn |
| Jul 31, 2025 | 31.44 Mn |
| Apr 30, 2025 | 31.61 Mn |
| Jan 31, 2025 | 32.02 Mn |
| Oct 31, 2024 | 31.83 Mn |
| Jul 31, 2024 | 31.80 Mn |
| Apr 30, 2024 | 31.60 Mn |
| Jan 31, 2024 | 36.43 Mn |
| Oct 31, 2023 | 38.62 Mn |
| Jul 31, 2023 | 39.22 Mn |
| Apr 30, 2023 | 38.25 Mn |
| Jan 31, 2023 | 42.15 Mn |
| Oct 31, 2022 | 43.44 Mn |
| Apr 30, 2022 | 43.85 Mn |
| Jun 11, 2021 | 50.90 Mn |
Skillsoft Amortizatization of Intangibles 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=amortizatization-of-intangibles&ticker=SKIL&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "amortizatization-of-intangibles", "ticker": "SKIL", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=amortizatization-of-intangibles&ticker=SKIL&period=max&api_key=YOUR_API_KEY");
const data = await res.json();