Optimizing Food Ordering App Data Analysis Using Support Vector Machines (SVM): A Machine Learning Approach for Customer Behaviour Prediction and Sales Forecasting

Abstract

Robust growth in food delivery services creates a demand for a restless analytical instrument. The present research study applied SVM for the analysis of data obtained from the food ordering application, mainly on customer behaviour prediction and sales forecasting. SVM has been trained on transaction data from the past, user preference, and external sly factors, such as weather and time, in order to appropriately glean ordering behaviour. This allows businesses to predict customer response proactively and therefore protect business efficiency as well as profitability. The performance of SVM methodology has been evaluated against other machine learning algorithms with respect to their capability to capture non-linear relationships in food-ordering data. The results obtained ratify the potential for SVM in improving recommendation systems through providing accurate predictions for user preferences, thereby increasing their rate of order and turnover. In relation to the forecasting of demand, this creates another factor of great importance in resource management since it minimizes wastage and optimizes the logistics of deliveries. Beyond mere foretelling, SVM’s predictive capabilities help in customer retention through various promotional attempts to target different market segments. By learning consumer behaviour, a business can tailor its services, breeding customer loyalty and enhancing customer lifetime value. The present research builds SVM as a rugged application in food delivery for data-driven decision-making leading to profitability maximization and improved customer service. The findings in this work highlight the SVM as a useful tool, providing actionable insights and competitive advantages in the fast-paced food delivery context.