Cardlytics (CDLX) Operating Expenses (2017 - 2026)
Cardlytics (CDLX) reported Operating Expenses of $42.32 million for Q2 2026, down 37.4% from $67.61 million a year earlier and down 0.6% from the prior quarter.
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Analysis
Cardlytics (CDLX) Operating Expenses (2017 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Cardlytics' Operating Expenses came in at $267.99 million, down 38.1% year-over-year; for FY2025, it came in at $335.09 million, down 29.3% from FY2024.
- Operating Expenses has a five-year compound annual growth rate of 6.8% (FY2020 to FY2025).
- By year, Operating Expenses came in at $473.8 million in FY2024 (+6.5%), $444.87 million in FY2023 (-41.2%), $756.32 million in FY2022 (+94.1%) and $389.72 million in FY2021 (+61.8%).
- The Q2 2026 figure ranks as the lowest quarterly Operating Expenses since Q1 2022.
- Year over year, Operating Expenses has now declined in each of the last seven quarters, with an average decline of 16.2% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was Q4 2022 (growth of 336.9%); the low point was Q4 2023 (a decline of 58.7%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $42.59 million (Q1 2026), $62.19 million (Q4 2025) and $120.89 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Alphabet | 3,555.16 Bn | 3,290.06 Bn | 76.16 Bn | 87.00 Bn |
| 2 | Meta Platforms | 1,645.02 Bn | 1,309.54 Bn | 51.23 Bn | 51.61 Bn |
| 3 | Netflix | 259.98 Bn | 221.27 Bn | 6.80 Bn | 1.50 Bn |
| 4 | Alibaba Group Holding | 242.36 Bn | 241.21 Bn | - | - |
| 5 | Shopify | 192.37 Bn | 171.18 Bn | 1.78 Bn | 1.21 Bn |
| 6 | Uber Technologies | 142.90 Bn | 113.72 Bn | 6.42 Bn | 13.32 Bn |
| 7 | AppLovin | 130.75 Bn | 119.27 Bn | 1.94 Bn | 475.85 Mn |
| 8 | Spotify Technology | 122.74 Bn | 105.49 Bn | - | - |
| 9 | Booking Holdings | 122.04 Bn | 55.79 Bn | - | 5.48 Bn |
| 10 | Cardlytics | 10.61 Mn | -155.90 Mn | - | 38.30 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 38.30 Mn |
| Jun 30, 2026 | 42.32 Mn |
| Mar 31, 2026 | 42.59 Mn |
| Dec 31, 2025 | 62.19 Mn |
| Sep 30, 2025 | 120.89 Mn |
| Jun 30, 2025 | 67.61 Mn |
| Mar 31, 2025 | 67.55 Mn |
| Dec 31, 2024 | 82.31 Mn |
| Sep 30, 2024 | 215.60 Mn |
| Jun 30, 2024 | 85.45 Mn |
| Mar 31, 2024 | 90.43 Mn |
| Dec 31, 2023 | 192.09 Mn |
| Sep 30, 2023 | 99.66 Mn |
| Jun 30, 2023 | 101.02 Mn |
| Mar 31, 2023 | 52.10 Mn |
| Dec 31, 2022 | 465.14 Mn |
| Sep 30, 2022 | 61.19 Mn |
| Jun 30, 2022 | 197.72 Mn |
| Mar 31, 2022 | 32.27 Mn |
| Dec 31, 2021 | 106.46 Mn |
API Access
Cardlytics 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=CDLX&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "CDLX", "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=CDLX&period=max&api_key=YOUR_API_KEY");
const data = await res.json();