Assignment Task
Dynamic Pricing Model
Machine Learning Pricing Project, Implementing A Retail Price Optimization Algorithm Using Regression Trees For Uncle Nenes Burger
Understanding the problem
Research Question
What is the impact of different pricing strategies on the sales of food items at UNCLE NENES Burgers and how can machine learning be used to optimize the prices?
Optimization based on price elasticity of demand is the use of analysis by a company to determine how their customers will respond to different prices of their products in different channels, such as online or offline stores. The objective of the analysis is to determine the prices that will help the company meet its objectives, which can vary and include increasing profit, revenue, or other goals. The data used to determine these prices can be survey data, previous sales data, or other data. The goal of price optimization is to use data to predict the behavior of potential buyers to different prices of a product or service.
In almost all industries or businesses, there are dedicated individuals or teams who are responsible for pricing and price optimization, including pricing analysts in big companies or business owners in smaller companies. The correct pricing is very important for the success of a business.
There are various strategies that can be employed to price products, including cost plus pricing, competition-based pricing, perceived value-based pricing, demand-based pricing, and understanding the price elasticity of products. The pricing strategy will depend on the end goal, such as increasing sales, footfall, revenue, or profit.
Using sales data to price products is important, as it allows for a more concrete pricing strategy, rather than relying on gut feel. Sales data is usually available, even in small stores. Products have a property called price elasticity, where some products are elastic and their purchase patterns change based on their price, while others are inelastic and their sales are not much affected by price changes. In this series, the focus will be on the elastic products, whose past sales data can be analyzed to determine the relation of how sales change based on their prices.
Pricing a product is a crucial aspect of any business. A lot of thought process is put into it. There are different strategies to estimate prices for different kinds of products. There are products whose sales are pretty sensitive to their costs, and as such, a slight change in their price can lead to a noticeable difference in their sales. At the same time, there are also products whose sales are not much affected by their worth – these tend to be luxury items or necessities (like certain medicines).
Price Optimization using machine learning algorithms is becoming increasingly popular in the business world. Companies use regression algorithms such as linear regression to estimate the price elasticity for each of their products using past sales data. The goal is to understand how customer behavior changes as the price of a product changes and to use this information to determine the optimal price for each product (Kotler & Keller, 2009).
Price elasticity of demand (EPD) is the degree to which customers’ desire for a product change as its price changes. It is calculated as the percentage change in quantity demanded in response to a one percent increase in price, holding everything else constant (Blackwell, Miniard, & Engel, 2006). The concept of price elasticity plays a critical role in determining price estimates, as it helps companies understand the sensitivity of sales to price fluctuations.
In this machine learning price optimization project, the focus will be on a specific fast-food chain, UNCLE NENES Burgers. The past sales data of the chain will be used to calculate the price elasticity for each food item and determine the optimal price. The data, stored in a PostgreSQL database hosted on Amazon RDS, will be processed, and analyzed using data visualization libraries in Python such as matplotlib and seaborn (Zikmund, Babin, Carr, & Griffin, 2013). The project will also involve merging the sales, transactions, and date datasets and preparing them for use with machine learning algorithms using Pandas data frames.
Machine learning algorithms such as regression trees and the ordinary least square method will be used to estimate the price elasticity for different products. The project will also cover the interpretation of statistical parameters like the r-squared value and how to improve the accuracy of the models by eliminating specific variable values. The end goal is to maximize profit by using the results of the price elasticities evaluation (Cohen, Cohen, West, & Aiken, 2013).
This project has applications in various industries beyond the fast-food sector. For example, it can be used in the medical, hospitality, and insurance industries to recommend changes to the prices of services offered. An analyst could use the results of this project to recommend changes to the prices of various services offered by a hotel based on previous residents’ feedback (Marketing Management, 14th Edition, Kotler & Keller).
In conclusion, this project is a comprehensive guide to using machine learning algorithms for price optimization. It covers the calculation of price elasticity, data processing and analysis, and the use of machine learning algorithms to determine the optimal prices for products. Companies can use the results of this project to improve their bottom line by better understanding customer behavior and adjusting prices accordingly.
Introduction: Discuss the background and context of the research problem, including the relevant historical and theoretical context. /Problem and Purpose/Objectives/scope/limitations?
Literature review: Provide an overview of the relevant literature on the research problem, including a critical evaluation of previous studies and a discussion of the gaps in the literature that the current study aims to fill.
Research Methodology: EDA/ARCHITECTURE/ Outline the research methodology that will be used to address the research problem, including the research design, data collection methods, data analysis techniques, and ethical considerations.
