Analyzing Key Factors Influencing Coffee House Revenue: A Predictive Modeling Approach

Abstract

Nowadays, understanding and predicting revenue trends is highly competitive, in the food and beverage industry. It can be difficult to determine which aspects of everyday operations have the most impact on income, especially for coffee shops. Transactional and behavioral data are readily available, but numerous small businesses lack the data-driven models necessary to convert these insights into predictions that can be put into action. By using linear regression techniques to forecast daily income based on important business parameters, this study seeks to close the gaps. In order to investigate feature distributions and correlations, exploratory data analysis performed including statistical summaries, box plots, and scatter plots with regression lines. A correlation study determines the most important parameters and are “Number of Customers Per Day”, “Average Order Value”, and “Marketing Spend Per Day”. Secondary elements like location foot traffic, the number of employees and operating hours come after this. A linear regression model is trained using these characteristics, yielding an R 2 score of 0.89, a Mean Absolute Error (MAE) of 244.13. The model’s efficacy is validated by comparing actual and projected revenue. This method provides a useful foundation for forecasting revenues and making well-informed decisions in small-scale retail businesses, such as coffee shops.