---
title: "Lead Price Optimizer"
category: Bidding
canonical: https://plato.ai/tools/price-optimizer/
---

# Lead Price Optimizer

Given how acceptance falls as price rises, what price (or reserve floor) maximizes profit per lead?

Free interactive calculator at https://plato.ai/tools/price-optimizer/ (runs in the browser).

## How it works

A higher price earns more on each lead a buyer accepts, but fewer leads are accepted. This calculator takes a simple acceptance curve (the price at which half of leads are accepted and how sharply acceptance falls around it), what an unsold lead is still worth to you and your cost per lead, and finds the price or reserve floor that maximizes expected profit per lead. It compares that with your current price and charts profit and acceptance across prices. The default inputs are an illustrative example, not benchmarks.

The share of leads accepted at price p is modelled as a logistic curve that equals 50% at the half-acceptance price and falls more steeply the smaller the price sensitivity. An accepted lead pays the price; an unsold lead earns its fallback value (a secondary buyer, your own offers, or nothing). Expected profit per lead is acceptance × (price − fallback) + fallback − cost. The calculator searches prices numerically (a grid, then golden-section search) for the peak. At the peak, raising the price a little loses as much from rejected leads as it gains on accepted ones, which for this curve means price − fallback = sensitivity ÷ share rejected. Cost per lead shifts profit but not the best price; a higher fallback value raises it.

`a(p) = 1 ÷ (1 + e^((p − p50) ÷ s)); profit(p) = a(p) × (p − v) + v − c; optimal price p* solves p* − v = s ÷ (1 − a(p*))`

## Frequently Asked Questions

### Why is the most profitable price not the highest one buyers will pay?
Because profit is margin times volume. A higher price earns more on each accepted lead but sells fewer, and past the optimum the leads you lose cost more than the extra margin earns. The dynamic pricing article's example is the same trade-off: dropping a price from $50 to $45 can pay if it lifts accepted volume enough.

### How does the value of an unsold lead change the answer?
The more an unsold lead still earns, through a secondary buyer, re-pinging or your own offers, the less a rejection costs you, so the best price rises. Your cost per lead does not move the best price at all; it only shifts every profit figure down by the same amount.

### Can I use this to set a reserve price in an auction?
As a guide. The calculator treats the price as a take-it-or-leave-it offer, which is exactly how a floor works when one buyer clears it. In a second-price auction the winner often pays the second bid instead, so revenue is higher than shown, but for symmetric buyers the best floor follows the same rule when acceptance is read as one buyer's chance of valuing the lead above the floor (Myerson, 1981).

### Where do the curve's inputs come from?
From your own data, not from this page. Randomize prices or floors within a small range on a share of traffic, per segment, and fit acceptance against price. The defaults are an illustrative example, not a benchmark, and real curves shift as buyers hit caps and demand changes.

## Read the article

[The End of Fixed Bidding: Dynamic Pricing for Lead Gen](https://plato.ai/md/dynamic-pricing-lead-gen.md)