Recently we observe a boom of e-shopping and e-marketing, with plenty of tools, systems, and improvements to encourage customers to purchase more goods and services and to cut off the costs. One may enumerate here the recommendation systems, JIT strategy, instant shopping channels connected to the advertisement, personal targeting, and many more. To facilitate the implementation and operation of such systems, we collect several data related to users’ behavior by means of cookies, server logs, link and timing analysis, etc. Up to now, limited attention has been paid to apply all of these mechanisms to real stores and marketplaces. To fill the gap, in this paper we propose a multi-level system to (1) contextually analyze customer behavior at a shopping space by means of Internet-of-Things devices and services, (2) process this information at server-side to compute some instant purchasing recommendations and incentives, and (3) immediately send these recommendations to the customers, either in a form of classical marketing message, or as a personal advice, possibly linked with a discount, “special offer”, etc. In such a way, each customer is served personally and thus has more motivations to buy the recommended or discounted items.