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HALOWERK Price Elasticity Estimator
x402
HALOWERK Price Elasticity Estimator is a paid API for AI agents from handel.halowerk.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-10).
Fits a log-log (constant-elasticity) demand curve to historical price and sales data and returns elasticity, standard error, t-statistic, p-value, and Student-t confidence intervals, plus optional contribution-optimal price and price grid evaluation.
Price / call$0.005
Success—
StatusUnknown
How this service works
Fits a constant-elasticity demand curve to the price and sales series you supply and returns the elasticity with its standard error, t statistic, two-sided p value and a Student t confidence interval, not a normal one, because at eight observations the difference in interval width is double-digit percent and a too narrow interval is exactly the kind of number a machine reuses unchecked.
Output
Returns the estimated price elasticity coefficient, its standard error, t-statistic, two-sided p-value, and a Student-t confidence interval at the requested level. If unit_cost is provided, also returns the contribution-optimal price. If price_grid is provided, returns predicted units sold and contribution margin at each candidate price. Zero-unit observations are excluded and reported explicitly rather than silently dropped.
Example use cases
Retailer repricing a seasonal product
“I have 12 months of monthly price and units-sold data for our winter jacket line, prices ranged from €89 to €149 and volume from 40 to 310 units, with three of those months flagged as promotional campaigns. Fit an elasticity model controlling for promotions and tell me the elasticity at 95% confidence — currency is EUR.”
Category manager evaluating a price ladder
“We're considering five price points for our private-label pasta — €0.79, €0.89, €0.99, €1.09, and €1.19 — and I have 8 quarterly observations of actual sales. Our unit cost is €0.35. Can you fit the demand curve and tell me which of those five prices maximizes contribution margin, along with the elasticity and its confidence interval?”
Startup stress-testing demand sensitivity
“We ran a pricing experiment across 6 cities at different price points between $12 and $28 for our SaaS monthly plan, here are the subscriber counts for each. Fit a log-log demand model, give me the elasticity with its standard error and p-value at 90% confidence, and tell me if demand is statistically significantly elastic.”
Technical details
Endpoint
POSThttps://handel.halowerk.com/v1/price-elasticity?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a statistically rigorous, constant-elasticity demand estimate backed by Student-t (not normal) confidence intervals — especially critical at small sample sizes (under ~30 observations) where normal approximations meaningfully understate uncertainty. Prefer it over manual regression when you also need promotion-period controls, competitor price controls, a contribution-optimal price recommendation, or evaluation of a candidate price grid, all in a single call. It is purpose-built for single-SKU pricing analysis from your own historical data, not for cross-sectional market research or real-time demand signals.
Side effects
- Fewer than 4 observations supplied — minimum required for a meaningful fit
- All prices identical — log-log regression requires price variation
- Zero units_sold in all periods — no non-zero observations to fit
- Negative or zero prices supplied — logarithm is undefined
- Insufficient degrees of freedom for requested confidence level
- Competitor price or promotion flag provided but corresponding option flag not enabled in request
- Currency code not exactly 3 uppercase ISO 4217 characters
- Payment not confirmed via x402 — 402 Payment Required returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| currency | string | ISO 4217 code for every amount in this request. Not converted, only carried through. |
| unit_cost | number | Variable cost per unit. Required for the contribution-optimal price; without it only the elasticity is returned. |
| price_grid | array | Candidate prices to evaluate with predicted units and contribution. Evaluated at the fitted model, in the order you give them. |
| observations | array | Your own price and sales history for one SKU. Every amount is in the currency named at document level. |
| confidence_level | number | Confidence level for the elasticity interval. Student t, not normal. |
| include_promotion | boolean | Whether to add the promotion indicator as a regressor. |
| include_competitor_price | boolean | Whether to add the log competitor price as a second regressor and report the cross-price elasticity. Every observation must then carry competitor_price. |
Tags
x402
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