USING PREDICTIVE ANALYTICS TO IMPROVE RESTAURANT PERFORMANCE IN ONLINE FOOD DELIVERY PLATFORMS (OFD) IN QATAR

Abstract

Online Food Delivery is becoming an integral part of restaurant operations and profitability because online delivery platforms are increasing the reach and accessibility of restaurants through the digitalization of the restaurants' menu and providing customer traffic through their applications. Moreover, customer behaviors have shifted to becoming more digitalized irrespective of their age because of older demographics becoming more technologically competent and connected. This will have an impact on the restaurants in two ways: reach new customers, improve the restaurants' customer acquisition, and increase their customer base; the second impact will be the switch of existing customers who prefer dine-in to order through online delivery platforms because of the speed and convenience that OFD can provide. The increase in revenue contribution of OFD to the restaurants' profitability can also cause a negative impact because of increased marketing expenses to enable the restaurant to compete in a highly competitive market, and the commission charged by OFD platforms for the use of their service. In this paper, the factors that are controllable to the restaurant in terms of operational and promotional activities will be explored to understand their overall impact and a predictive model will be generated to help in predicting the most optimal operational and promotional activities. The results show that the predictive models generated could help predict the optimal operational and promotional activities and conditions that could maximize their overall conversion in OFD platforms.