An application of operational analytics: For predicting sales revenue of restaurant

Abstract

Operational analytics improves existing operations of a firm by focusing on process improvement. It acts a business tool for resource management, data streamlining which improves productivity, employee engagement, customer satisfaction and provide investment opportunities. Crucial insights into the problem can be obtained which aids to determine key business strategy through various stages of data analysis and modeling, such as exploratory data analysis, predictive modeling, documentation and reporting. In this work, a real world dataset is considered for the study, where the sales revenue of restaurant is predicted. A second stage regression model built upon base regression models which are linear regression, ridge regression, decision tree regressor. Based on the results obtained, the following findings are reported: (i) annual sales revenue trend, (ii) food preference in cities, (iii) demand variability i.e. effect of first week and weekend, and (iv) comparison against ensemble methods in terms of prediction accuracy. This work also suggest avenues for future research in this direction.