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Customer Churn Rate IQR Outlier Flags Calculator
x402
Customer Churn Rate IQR Outlier Flags Calculator is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).
Flags customer churn rate observations that fall beyond 1.5× the interquartile range as statistical outliers across a supplied dataset
Price / call$0.01
Success—
StatusUnknown
How this service works
Customer churn rate - IQR outlier flags: Flag observations beyond 1.5 times the interquartile range; statistical flags are not errors or fraud findings. Metric: Customer churn rate. Supplied figures only; returns JSON with formula, units and limitations. One of 50 calculators with 20 analysis modes. No live feed, financial advice or trade execution. 0.01 USDC includes one bounded iqr outlier flags report.
Output
Returns a JSON object containing IQR outlier flags for each submitted churn rate observation, along with the formula used, units, and stated limitations of the statistical method. Flags indicate which rows fall beyond 1.5× the interquartile range of the computed churn rates.
Example use cases
Monthly churn anomaly detection for SaaS
“I have 12 months of churn data for our SaaS product — can you run IQR outlier detection on it? Lost customers each month were 5, 6, 5, 55, 5, 6, 5, 5, 6, 5, 5, 6 and opening customers were 100, 110, 100, 105, 100, 110, 100, 100, 110, 100, 100, 110.”
Quarterly churn review for retail chain
“We track customer churn across our retail stores by quarter — can you identify which quarters look like statistical outliers using the interquartile range method? Here are 8 quarters: lost_customers 200, 210, 195, 800, 205, 198, 210, 202 and opening_customers 5000, 5100, 4900, 5050, 5000, 4950, 5100, 5000.”
Cohort churn outlier screening for analyst report
“Before I include these churn rate figures in my analysis report, can you flag any data points that look statistically extreme using IQR outlier flags? My cohorts had lost_customers of 10, 11, 10, 10, 90, 10, 11 and opening_customers of 200, 210, 200, 205, 200, 200, 210.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-finance-customer-churn-outliers?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, low-cost statistical outlier screen on customer churn rate time-series or cohort data using the standard IQR method. It is ideal for pre-analysis data quality checks, anomaly triage in churn dashboards, or preparing clean datasets before deeper modeling. It does not provide fraud detection, financial advice, or real-time data — use it purely for bounded statistical flagging of supplied figures.
Side effects
- Fewer than 2 rows submitted (minItems violation)
- More than 200 rows submitted (maxItems violation)
- opening_customers value of 0 (exclusiveMinimum violation causing division-by-zero)
- Negative values for lost_customers or opening_customers
- Non-numeric values in required fields
- Payment not included or insufficient USDC balance
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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