Delivery service and omni-channel online-and-offline for retail collaborative recommendations

Abstract

From the perspective of e-commerce, delivery and retail operators can join in discovering valuable data on the platform through interactive data on consumer preferences for delivery service and online-and-offline purchasing. These operators can then summarize the information to make collaborative recommendations more accurately, thus increasing consumer purchasing. The delivery service business model is the final link in logistics for both online-and-offline business. An omni-channel, the online-and-offline business model, provides a complete and uninterrupted consumption experience by combining data and marketing content, acting as an offline physical channel and online with consistent services and information. Online-and-offline business models combine e-commerce and physical commerce. A recommendation system filters information to recommend information, services, or products that users may need based on their preferences, interests, behaviors or needs. Recommendation systems include collaborative filtering, content-oriented recommendation, and knowledge-oriented recommendation. Regarding retail collaborative recommendation, that is, a recommendation mechanism involves two or more parties, such as logistics, retail firms and e-commerce operators, working together to obtain necessary consumer information and knowledge, such as profiles and preferences, as the basis for personalized product recommendations. For example, when consumer A purchases mountaineering equipment on a website, the shipping fee is included after discounts, and A then chooses the transaction method of electronic payment and home delivery service. This transaction record involves three-party operators of products, logistics, and cash flows. Through this transaction, the multi-party platform not only understands consumers' purchasing behavior, but also specifically understands consumers' intentions, including outdoor sports, mountain climbing, online discount preference, electronic payments, home delivery, etc. Through collaborative recommendation, multi-party operators can analyze the specific profile of consumer A and further promote information that may interest the customer. This information is not just about specific products, but also includes information related to backpacks, such as weather, map routes, sports news, blog articles, etc. Thus, collaborative recommendation is an approach that seeks to understand consumers' lives and context. In these regards, this study investigates Vietnamese consumer behaviors with delivery services and omni-channel online-and-offline (n=2,354). Data mining analytics, including clustering analysis and association rules, reveal knowledge clusters/patterns/rules for investigating delivery service and omni-channel online-and-offline for retail collaborative recommendations. Finally, with information technologies and business applications developments, such as artificial intelligence, data computation, business intelligence and machine learning, the theoretical and practical applications of retail collaborative recommendations can be more completely developed for human interaction and emerging technologies.

Keywords: Delivery service, Omni-channel, Online-and-offline, Data mining analytics, Retail collaborative recommendations.

DOI: 10.54941/ahfe1006742