Find the Price That
Maximizes Your Revenue
Most businesses set prices by gut feel and competitor benchmarking. MCP Analytics uses your actual sales data to calculate price elasticity, model demand curves, and find the exact price point where revenue is maximized—for every product, backed by statistics.
Most Businesses Are Leaving Money on the Table
Competitor benchmarking tells you what others charge. It doesn't tell you what your customers will pay — or what price maximizes your specific revenue.
Price Elasticity Analysis
How much does your demand drop when prices rise? Elasticity quantifies the tradeoff between price and volume for each product — so you know exactly how much room you have to raise prices before hurting revenue.
Demand Curve Modeling
Regression modeling on your historical sales data builds a demand curve for each product. The curve shows you expected sales at any price point — and identifies the revenue-maximizing price that sits at the top of the curve.
Discount Impact Analysis
Do your discounts actually pay for themselves? Promotion analysis quantifies whether discount-driven volume increases offset the margin reduction — and identifies which discount depths are worth running.
Export. Upload. Optimize.
Three steps from sales data to the right price
Export Your Sales Data
Download order data from Shopify, Stripe, your POS, or any sales platform as CSV. Include product, price, quantity sold, and date. Historical price changes and discount data make the analysis more accurate.
Upload Your CSV
Drop the file into MCP Analytics. The system auto-detects price, quantity, product, and revenue columns. Shopify order exports, Stripe payment data, and any standard sales format all work directly.
Get Your Pricing Report
Receive an interactive report with price elasticity by product, demand curve charts, revenue-maximizing price recommendations, and projected revenue impact of price changes — all backed by your actual data.
Pricing Analyses Available Today
Every analysis runs on your actual sales data and produces a shareable report
Price Elasticity
Measures how sensitive demand is to price changes for each product. Identifies elastic (price-sensitive) vs. inelastic (price-tolerant) products in your catalog.
Demand Curve Modeling
Regression model of price vs. quantity sold. Produces a demand curve and identifies the revenue-maximizing price point on that curve.
Optimal Price Point
The specific price that maximizes total revenue (price × quantity), with confidence intervals. Compare to your current price and see projected revenue impact.
Discount Impact Analysis
Do discounts pay for themselves? Compares revenue at full price vs. discounted price, accounting for volume lift and margin reduction.
Price Sensitivity by Segment
Do different customer segments (geographic, demographic, channel) have different price sensitivities? Identifies opportunities for segment-specific pricing.
Seasonal Price Optimization
How does price sensitivity change by season, month, or day of week? Identifies when you can charge more and when discounting is most effective.
Subscription Tier Analysis
For SaaS and subscription businesses: conversion rates, upgrade patterns, and churn rates by pricing tier. Identifies where tier boundaries are creating friction.
Price Change Simulation
Model the projected revenue impact of raising or lowering prices by 5%, 10%, or 20% — based on your estimated demand curve, before you make the change.
Bundle Pricing Analysis
Which products are bought together? Bundle pricing analysis identifies natural groupings and models the revenue impact of package pricing strategies.
MCP Analytics vs Manual Pricing Analysis
What you get beyond gut feel and competitor benchmarking
| Capability | MCP Analytics | Manual / Spreadsheet |
|---|---|---|
| Price elasticity calculation | Requires econometrics expertise | |
| Demand curve modeling | ||
| Revenue-maximizing price point | ||
| Discount ROI analysis | Basic only | |
| Statistical confidence intervals | ||
| Price change simulation | ||
| Basic revenue totals and averages | ||
| Requires no technical expertise | Upload CSV, done | Excel + econometrics |
Pricing Optimization FAQ
What is price elasticity and why does it matter?
Price elasticity measures how much demand changes when price changes. A product with high elasticity loses significant sales when the price rises. A product with low elasticity maintains sales even at higher prices — meaning you're likely underpriced and leaving revenue on the table. Knowing your elasticity tells you exactly how much room you have to raise prices without hurting revenue.
What sales data do I need for pricing analysis?
The minimum is a CSV with product name, price, and quantity sold. Adding dates enables time-series analysis. Adding discount information reveals price sensitivity during promotions. The more price variation in your data — from historical price changes, discount events, or sales — the more accurate the elasticity estimates. Shopify order exports and Stripe payment data both work directly.
How accurate is price elasticity analysis from historical data?
Accuracy depends on how much price variation exists in your data. If your price has never changed, there's nothing to model. But most businesses have discount events, seasonal sales, or historical price changes that create the variation needed. The report includes confidence intervals so you know how precise the estimates are — and flags when the data is insufficient for reliable modeling.
Can this help me decide whether to raise or lower prices?
Yes. The revenue-maximizing price calculation shows the theoretical optimal price based on your demand curve. If your current price is below the optimum, raising it would increase revenue. If it's above, you may be losing volume that would more than compensate for the lower margin. The analysis shows both the optimal price and the projected revenue impact of moving to it.
Does this work for subscription pricing or only one-time purchases?
Both. For subscription businesses, the analysis focuses on plan conversion rates, upgrade/downgrade patterns, and churn at different price points. For one-time purchases, it models the direct relationship between price and purchase volume. Stripe subscription data is particularly well-suited since it captures both pricing tier and conversion/churn outcomes.
Pricing Optimization Resources
Guides and tools for data-driven pricing decisions
Revenue Analytics
Analysis Methods
Ready to Find Your Revenue-Maximizing Price?
Upload your sales CSV and get price elasticity analysis and optimal pricing recommendations in under 3 minutes. No credit card required.