Optimizing product price using regression

Optimizing product price using regression

A Python implementation of price optimization for maximizing revenue. Price and quantity are two fundamental measures that determine the bottom line of every business.

Price and quantity are two fundamental measures that determine the bottom line of every business, and setting the right price is one of the most important decisions a company can make. Under-pricing hurts the company’s revenue if consumers are willing to pay more and, on the other hand, over-pricing can hurt in a similar fashion if consumers are less inclined to buy the product at a higher price.

So given the tricky relationship between price and sales, where is the sweet spot — the optimum price — that maximizes product sales and earns most profit?

The purpose of this article is to answer this question by implementing a combination of the economic theory and a regression algorithm in Python environment.


1. Data

We are optimizing a future price based on the relationship between historical price and sales, so the first thing we need is the past data on these two indicators. For this exercise, I’m using a time series data on historical beef sales and corresponding unit prices.

## load data
import pandas as pd
beef = pd

## view first few rows
beef.tail(5

data-science business-intelligence regression economics data analysis

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