Combination of Pricing Strategies with Machine Learning: Case Study on Gela Dairy Company

In past years, having a consistent pricing strategy was considered logical. However, today, due to the rapid changes in the market structure, severe price fluctuations, intense competition and the changing needs of customers, having a fixed strategy may not be able to meet the needs of companies to stay in the market. The purpose of this research is to provide a solution based on machine learning so that it can provide a model based on the existing pricing strategies so that it can simultaneously benefit from the benefits of several strategies. In this research, by using the opinions of industry experts and background studies, parameters affecting the industry were extracted and data were collected according to that. The data were analyzed after refining in Python software using machine learning algorithm. The data were collected by the census method and in a period of almost five years from the Gela dairy products company. The relationship obtained from the range of data was evaluated by the Coefficient of determination and reached a number 98.6% that is desirable. This shows that the model presented in this research using the 4th degree polynomial regression algorithm can be useful for producing a competitive price that can meet the needs of customers and at the same time bring a good profit margin to the company.