Application of Artificial Intelligence and Machine Learning in Omnichannel Marketing

Abstract

In this project, we are scrapping the data from Swiggy's portal for all the cities and close to ~50,000 restaurant partners to understand the macroeconomic relationships between the costs, cuisines, ratings and the population and GDP of the different states. We first analyze the average cost of restaurants across states and their relationship with the nature of the state economies and special characteristics like tourism. Then, we analyze the relationship between the cost of restaurants and the number of outlets with the population, area and geographical location of cities to understand the macroeconomic impact of such factors. Regarding the different variety of cuisines offered by different restaurants, we also analyze the difference in food palates across different regions and how factors like migration and the close relationship of people in certain states with other countries play an important role in the offerings available. Based on the different cuisines available, we have then tried to analyze its relationship with the average costs of the restaurant and any potential impact on the ratings of the restaurant, which gives business insights regarding the type of cuisine that should be offered in different parts of the country for higher revenue realization as well as higher customer satisfaction reflected by ratings. Finally, we also aim to draw insights from any potential relationship between the ratings of different restaurants and their costs.