title: ElasticityLab | Pricing Intelligence Studio
emoji: 📈
colorFrom: indigo
colorTo: blue
sdk: static
app_file: index.html
pinned: false
license: mit
short_description: Pricing optimization and demand simulation.
tags:
- business-intelligence
- pricing-strategy
- revenue-optimization
- data-visualization
- simulation
ElasticityLab | Pricing Intelligence Studio
Hugdrigo portfolio · Project #89
An interactive, browser-only price elasticity and gross profit simulator for exploring product pricing choices and their financial implications. It is built as a single static HTML application, with no paid APIs, network calls, or account requirements.
Features
- Executive overview: revenue, gross profit, constrained optimization opportunities, and product-level contribution comparisons.
- Price laboratory: constant-elasticity price/demand curves, configurable permitted price movements, minimum gross-margin guardrails, and side-by-side recommendations.
- Portfolio management: add, edit, archive, and restore products with persistent local settings.
- Scenario planning: demand shock, variable-cost inflation, channel lift, four scenario presets, and 600 reproducible synthetic uncertainty draws.
- Interoperability: export spreadsheet-safe CSV, export complete JSON backups, import validated JSON workspace backups.
- Usability: responsive desktop and mobile layouts, dark mode, accessible form labels and navigation.
Methodology and assumptions
Demand is approximated using constant-elasticity demand, Q(p) = Q0 * (p/p0)^(-epsilon) * demandMultiplier * channelMultiplier. Revenue is price * Q, and gross profit is (price - variableUnitCost) * Q. Optimized prices are chosen by checking 1,001 points in the permitted price interval and enforcing the minimum margin constraint. An infeasible product retains its current price in portfolio summaries and is marked clearly. The 600 simulation draws perturb scenario outputs using a seeded pseudorandom generator. The reported percentiles reflect chosen perturbation assumptions, not calibrated confidence intervals.
All seeded data are fictional. This educational decision-support simulator excludes competitor reactions, dynamic multi-product substitution, taxes, fixed costs, seasonality, and empirically measured elasticity. It must not be presented as investment advice or as a validated revenue forecast.
Usage
- Create a Hugging Face Space using Static as its SDK and Blank as its template.
- Upload
README.md,index.html, andLICENSEto the repository root. - Open the app and explore the fictional demo products or enter your own inputs.
- Export a JSON backup before clearing browser data or moving computers.
All changes remain local to the browser; they are not uploaded to a server. If browser storage is restricted, use JSON downloads for persistence. No login is needed.
Repository settings
| Setting | Value |
|---|---|
| Space owner | Hugdrigo |
| Space ID | elasticitylab-pricing-intelligence |
| SDK | static |
| App file | index.html |
| License | MIT |
| Visibility | Public (recommended) |
QA checklist
- Functional navigation and responsive desktop/mobile layout
- Product creation, update, archive and restore
- Sensitivity sliders, constraints, and scenario recalculation
- CSV and JSON exports; valid/invalid JSON import
- Numerical checks against closed-form expected outputs
- Browser console checks for errors
License: MIT. Project: 089. Portfolio: Hugdrigo.