Welcome to a journey of data-driven pricing excellence! In this machine learning pricing project, we leverage cutting-edge regression tree algorithms to implement a retail price optimization model. This marks the initial stride toward constructing a dynamic pricing paradigm.
In the intricate landscape of business, pricing products demands meticulous consideration. This project zeroes in on products with sales sensitivity to price variations, exploring the nuances of price elasticity of demand (Epd). Epd measures the degree to which desire for a product changes with price fluctuations. Economists employ elasticity to quantify this sensitivity, expressed mathematically as the percentage change in quantity demanded divided by the percentage change in price.
Mathematical Representation: e = (%ΔQ / %ΔP)
As data scientists, our mission is to harness the power of past sales data from a cafe to identify optimal prices for items, namely burgers, coke, lemonade, and coffee. Striking the delicate balance is paramount: set prices too high, and sales dwindle; set them too low, and profit margins suffer. The quest is to identify the sweet spot for maximum profitability.
The dataset is encapsulated in three CSV files:
- Cafe - Sell MetaData.csv: Details about cafe sales. Columns: Sell ID, Sell Category, Item ID, Item Name.
- Cafe - Transaction - Store.csv: Transaction and sale receipt information. Columns: Calendar Date, Price, Quantity, Sell ID, Sell Category.
- Cafe - DateInfo.csv: Date-related information for transactions. Columns: Date, Year, Holiday, Weekend, School Break, Temperature, Outdoor.
Delving into customer behavior through sales data is imperative for business growth. This project employs previous sales data to estimate the cost of food items, exploring methods such as cost-less pricing, competition-based pricing, perceived value pricing, and demand-based pricing.
Before modeling, dataset processing is crucial. This project works on a burger cafe dataset, utilizing three datasets related to sales, transactions, and dates. Data stored in a PostgreSQL database on Amazon RDS is fetched using the psycopg2 library. Visualization is facilitated using Python libraries such as Matplotlib and Seaborn.
Departing from traditional statistical methods, the project employs machine learning for price optimization in Python. Regression trees and the ordinary least square method estimate price elasticity for different products. Statistical parameters like the r-squared value are scrutinized for analysis, with accuracy enhanced by eliminating specific variable values. The exploration extends to maximizing profit based on the results of price elasticities evaluation.
This project's focal point is optimizing prices in a burger cafe. However, its solutions transcend industries. Experts in medical, hospitality, insurance, and more can adapt the Python-based pricing optimization to their specific domains. For instance, an analyst can fine-tune service prices in a hotel based on previous resident feedback.
- How do you do Price Optimization?
- Various techniques can be applied, including reducing cannibalization for intra and inter products, and strategically reducing costs.
- What is Price Optimization Machine Learning?
- Regression machine learning algorithms, like linear regression, play a pivotal role. Price elasticity estimation precedes optimization using past sales data coefficients.
Embark on this journey of data-driven pricing mastery, where every algorithmic decision paves the way for a profitable future. #DataScience #MachineLearning #PriceOptimization