In e-commerce landscapes, online food ordering and delivery service is emerging as the fastest growing industry. Indian food delivery market size is expected to reach 7.5 to 8 billion US dollars in 2023. With the onset of COVID-19 pandemic, many dine-in restaurants are closed and online food ordering is affected enormously. This study is aimed to capture the impact of the COVID-19 pandemic on customers who order food online and analyze the various factors that influence the selection of restaurants online. The role of demographic variables of the respondents on the influencing factors, mode of payment, satisfaction, number of orders, and the most ordered meal is also studied. A self-administered questionnaire is used in this study to collect responses from 403 customers with a past experience of ordering food online. Two major factors emphasising on product and service are obtained as an outcome from Exploratory Factor Analysis that explains 67.72% of the total variance of choosing a restaurant online. Further, the measurement model fit is validated through Structural Equation Modeling. From the validated model, the product factor includes taste, price, discount and offer, the quantity of food, hygiene, and brand name. The service factor includes novelty, location of the restaurant, variety, packaging, nutrition, and promptness of delivery. Due to COVID-19, there is a 70.9% drop in the average frequency of ordering food online during the study period. Among the genders, men place higher food orders per month than women. Dinner is the most ordered meal and breakfast is the least ordered meal among all age categories. Taste is the most influencing factor that influences the customer in selecting a restaurant online, followed by hygiene, the quantity of food, discount and offers.
Market research is generally performed by surveying a representative sample of customers with questions that includes contexts such as psycho-graphics, demographics, attitude and product preferences. Survey responses are used to segment the customers into various groups that are useful for targeted marketing and communication. Reducing the number of questions asked to the customer has utility for businesses to scale the market research to a large number of customers. In this work, we model this task using Bayesian networks. We demonstrate the effectiveness of our approach using an example market segmentation of broadband customers.
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