# Towards Marketing 4.0: Vision and Survey on the Role of IoT and Data Science
## 4 Current State of the Art in IoT and Analytics (Underpinning the Pillars)
### State of the Art in Analytics  
IoT data can be used to obtain enriched information about customers to build just-in-time marketing strategies. These can be further combined with user profile data and can be used to generate effective recommendations. As a matter of fact, recommender systems are a major trend in IoT-based marketing. Recommender/Recommendation systems are information-filtering mechanisms that seek to feed users with suggestions based on their preferences in order to avoid users getting lost in information overload. These systems are rather present in fields such as healthcare, transportation, and agriculture; however it is in e-commerce and retail that they show their highest potential ([PERSON] et al., 2018). They can be divided into several classes depending on the source of the knowledge used for filtering. For instance, they can exploit demographic correlations between buyers, user similarity based on the purchase history and ratings, domain correlation between products and users' needs, and so on. While they are being extensively used for tracing online browsing behaviours, they have been seldom adopted in brick-and-mortar stores, as appropriate responses to in-store behaviours are rare. For instance, the work in ([PERSON] et al., 2018) presented a real-world experience of a recommender system in the fashion domain that uses data from both online and offline (i.e., in-person) shopping; however, it does not detail any feedback to the offline shopper. This gap can be filled by using IoT-enabled recommender systems, which are indeed in their rising phase ([PERSON] et al., 2018). One of these systems was designed and prototyped in ([PERSON] et al., 2018), where geofencing was used as a primary context in a tourism-driven use case. Another notable work is ([PERSON] et al., 2018), where the authors leveraged the concept of digital signage and used smart city IoT nodes that provide city dwellers with timely and localised information such as news and weather. Among such information, the authors suggested to treat it as a powerful marketing tool, which would allow for bi-directional communication with viewers for advertisement purposes through a recommender engine. In ([PERSON] et al., 2018), IoT technology was efficiently used in a study on physical stores, where the data about customers and purchases were collected through IoT devices and the profiling of trends took place through fuzzy neural network models. IoT analysis processes are vastly applied in tailoring advertisements through the use of the localised context of mobile users. As a matter of fact, some of the literature about MCS specifically targets marketing purposes. The work in ([PERSON] et al., 2018), for instance, used the crowd-sourced trajectories of a set of mobile users and clustered them by the longest common subsequences (LCSS) and commonalities in the visited hotspots. This allowed for a more focused marketing based on the habits of the groups of people. In ([PERSON] et al., 2018), the authors leveraged crowdsensing vehicle trajectory data to empower audience-targeted billboard advertising by studying the hypothetical advertisement spread based on the semantic topic. There are several other active fields of research that are capable of supporting MCS in providing individual or collective context awareness. In fact, regarding transportation mode detection, different conditions of need occur based on whether the user is driving, walking, or using public transportation; therefore, the tailoring market offers that are present in light of such conditions is highly desirable. A study on how market fragmentation occurs in urban commuting recently revealed how dedicated service offerings can be distributed to individual travellers [51]. Another powerful tool that can help determine trends in communities based on places are social networks; indeed, several studies have used local social networking for viral marketing, such as in [52], which builds upon selected influencers to promote initiatives in smart cities.