# Dynamic Pricing Method in the E-Commerce Industry Using Machine Learning
## 3 Materials and Methods  
The method developed by the authors aimed to inform whether the initial price of a product should be lowered, maintained at the same level, or increased. The proposed dynamic pricing method thus served as a decision support system for managers in making pricing decisions for individual products. The developed method can be presented as a procedure consisting of four steps. Step 1. Determine the list of dynamic pricing criteria in the e-commerce industry for the machine learning model and prepare the database. The literature identifies several criteria that may influence price within the dynamic pricing concept [12; 34]. These criteria were classified into three groups: customer-related, enterprise-related, and market-related. This classification was purposeful as it reflected different perspectives, allowing for a comprehensive understanding of the factors influencing price, considering customer expectations, strategic enterprise goals, and market dynamics. The list of criteria was been adapted to the capabilities and needs of e-commerce store managers. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Strengths** & **Limitations** \\ \hline Dynamic Pricing for & \(-\) & utilizes surrogate price elasticity models & \(-\) & primarily designed for online ticketing \\ Online Ticket Systems & \(-\) & for accessible dynamic ticket pricing & \(-\) & systems, limiting broader applicability \\ (DPRank Model) & \(-\) & enhances understanding of & \(-\) & requires accurate demand elasticity \\  & & demand variability & & estimation, which can be complex \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._All values that would eventually be included in the machine learning database needed to cover a uniform period. It was proposed that this period be 100 days (although different periods can be considered). The proposed set of criteria is presented in Table 2.  
The values for criteria \(C_{1}\) to \(C_{4}\) were stored in the database of the owner/manager of the selected e-commerce store. They were collected for users who had an account for the selected e-commerce store. The values for enterprise-related criteria were gathered based on any enterprise management system that contained information about inventory levels for individual products (a percentage scale for each product can be used), specified product margins (sale price-purchase price), and included storage costs for individual products (on any scale, e.g., semantic). The price relative to competitors could either be determined subjectively (e.g., on a semantic scale) or quantitatively through automatic price analysis of competitors on well-known e-commerce portals (software could be developed to automatically find the prices of competing products from other manufacturers without significant difficulty). Product seasonality was best defined in binary terms. If a product was in season, it was assigned a value of 1 (e.g., Christmas trees in December). If it was not, it was assigned a value of 0. Demand dynamics refers to the growth in sales of given products on the market. The value of this criterion could be determined in several ways. First, software could be developed to automatically analyze the demand for a given product on selected e-commerce portals (e.g., by reading the number of offers for a given product). Second, demand dynamics could be determined subjectively by experts (e.g., on semantic scales). Third, if the first two solutions were not feasible, the demand dynamics could be analyzed by examining the sales dynamics of the given product within the company/online store itself. Next, a database needed to be developed consisting of two elements. The first element was the values for the various criteria for a certain number of transactions. The literature indicates that such a database should contain no fewer than 50 records. However, it can be generally assumed that the more records the database contains, the better. The second element of the database consisted of labels.