Machine Learning to Personalize Order Preferences for Customers: A Privacy-Centric Approach for Small Businesses and Restaurants

Abstract

The change from traditional to online media has brought about new opportunities for small businesses and restaurants to improve people's experiences, especially through personalized services. This paper aims to discuss the utilization of Machine Learning (ML) in addressing the problem of identifying optimal order preferences for customers while adhering to privacy constraints that are a factor in the present generation. Small businesses benefit from ML since it enables them to understand customer inclinations and order history and provides ways of ensuring that the customers are satisfied and loyal to the business. Proprietary customization of services, particularly within a saturated business environment, ensures the loyalty of the customer base. However, privacy issues becoming a significant factor that organizations implementing ML approaches have had to contend with is the issue of data security. This paper explores different forms of ML that present their client data as secure and hosted within the business environment, such as federated learning,
on-premise models, and encrypted data processing. These techniques enable business firms to leverage internal customer data without disclosing them to third parties and without violating internal and external regulations such as GDPR and CCPA. It also provides awareness about various data security features, including encryption, data minimization, and customer consent, to gain long-term business with the customers. Using several examples of small companies, this paper illustrates the outcomes of secure ML personalization for clients, compliance, and business development. The specific instances involving predictive analytics and customer segmenting demonstrate the value of individualized suggestions without violating customer confidentiality. The last paper establishes that, through secure ML models, SMBs can provide customers with improved, secure, and enjoyable experiences, which will foster the development of trust and, consequently, lead to long-term SMB sustenance in the digital environment. The studies point to the importance of using ML for personalization in small businesses regarding customer data security so that they can be relevant and sustainably grow.