# Towards Marketing 4.0: Vision and Survey on the Role of IoT and Data Science
## 6 Future Research Directions  
In this section, we identify the key research directions that require further investigation towards the realization of the vision of Marketing 4.0.  
* **Privacy:** IoT-based marketing and advertising is fundamentally based on the massive gathering of data about users. At such a scale, much like in MCS scenarios, this will pose the threat of sensitive information disclosure. Privacy is in fact considered a major challenge nowadays due to the structural limitation imposed by personal data collection scenarios [62]. The introduction of privacy policies is imperative for IoT-based marketing, which often means taking a path involving _k-anonymity_ or _differential privacy_ policies [63], or even location perturbation and cloaking [64]. However, the trade-off between precise and timely information and the quality of the recommendation is very delicate and challenging. On the other hand, we could look into introducing strategies aimed at collecting just as much data as needed as dictated by the context. This would unburden the computation on the edge and, possibly, provide enough warranty in favour of information disclosure [65].
* **Real-time delivery:** The real-time delivery of advertisements in an IoT-enabled digital marketing ecosystem is dependent on the real-time extraction of related context information from IoT data. These applications, which are dependent on real-time context about external entities, are often referred to as context-aware applications. The introduction of IoT-triggered context-aware computing as services to the real world is in its infancy because of universal acceptance, standardisation, and accessible technologies [25; 66]. The usability of the context information is dependent on various parameters such as the freshness, latency, availability, etc., of the IoT data because of its constant temporal movement [25]. This leads to the research requirement of an efficient and adaptive context caching for fast concurrent access in IoT applications [67].
* **Generation of fine-grained recommendations:** Existing recommender systems can accurately enable group-level recommendations; however, designing a personalized recommender system is a challenging task. To address this issue, IoT devices and their users need to be actively monitored, the customers inside and outside the shopping areas need to be correctly identified, and the detection of their activities is essential. Moreover, potential buyers and customers are targeted to be attracted to the products right after the delivery of advertisements. Therefore, the recommendation engine must be able to personalize the advertisements at the fine-grained level and the IoT advertisement applications should capture the data at the finest-grained level to establish the right context and determine the exact situations and modes for advertisement delivery.  
* **Monetization of IoT-based digital marketing:** Unlike online advertising models in which marketing revenue is generated through pay-per-click business models, no such models exist for IoT-enabled digital marketing. While user interest in advertisements are estimated based on web browsing activities [68] for online advertising, it is challenging to do so for physical environments. The existing literature has looked to leverage IoT to measure usage behaviour [69] and shopper intention in retail environments [70]. A set of metrics for digital marketing in offline retail was proposed in [53], which relied on shopper intent based on movement patterns. For the widespread adoption of IoT-based advertising, it is necessary to develop a generic set of metrics to measure various kinds of shopper behaviour.