Internet of things, big data, and augmented reality used in the Facebook platform play a vital role in their advertisement. Facebook challenges involve revolutionizing its large number of customers and gaining trust among its users. However, the technological structures have helped in solving the problems by enabling the social media platform to move away from their traditional role of providing social network services. Through the creation of simple and enticing methods for its clients, Facebook has dramatically improved on their advertising business. In this assignment, one will discuss how big data, IoT, and augmented reality has facilitated the achievement of the value chain in their commercial activities.
Facebook uses big data for up-close, understanding of their clients, and on-the-ground perspective. Facebook holds one of the most crucial databases related to personal information. Big data is used in revolutionizing how people communicate and connect. Through its acquisition of the largest virtual platform in the world, the firm deals mainly in virtual. Facebook also faces the challenges of gaining the trust of its users. Facebook has been affected by privacy issues caused by the leaking of people's private information ( Najafabadi et al., 2015) . Similarly, the social media platform has been characterized by immense issues concerning its ability to access large amounts of data since it was established. The collection of data on the platform is done through their software. The use of big data has improved Facebook practices of selling their space to the advertisers. This has led to an increase in their profits. Facebook servers operate 24 hours throughout the week. This allows customers to make their promotions on Facebook, regardless of where they reside. For example, in 2014, the firm generated $53billion from the sale of ads in its 24% share that existed in the US ( Najafabadi et al., 2015) .
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The creation of IoT in Facebook devices on Facebook was done purposely to support the applications that are connected with it. The development was created to support the devices that the Facebook user uses. Facebook uses a Parse platform that allows people to incorporate data from multiple devices ( Lee & Lee, 2015) . Such has dramatically increased the number of customers on Facebook due to the reduction in data usage that one is accustomed to while handling multiple devices. Parse allows a Facebook user to use a single application connected to various devices. The developers view the idea as a way that necessitates different elements to communicate based on the core representation of IoT. The launching of Parse technology for Facebook has enabled the developers to build applications without relying on storage and server infrastructure. Initially, the program was called Mobile Backend, and during its launch, Facebook realized an increase in the number of clients, especially startups in business. However, the formalization of the Parse in the initial stages in Facebook did not please the client. The Facebook users were skeptical due to the failure of StackMob, which was competing with eBay and the primary competitor of Parse ( Karagiannis et al., 2015) . Despite the challenge, Facebook engaged in efforts that were meant to innovate the technology. The developers' creativity led to the addition of features to Parse, such as the building of casual games and consumer applications. Parse has compelling features such as Cloud Code that allows developers to execute business logic and workflows that might be complex in the cloud. Offloading of heavy cloud through Parse allows users of Facebook to connect to other online platforms such as SalesForce, Twilio, and Twitter. Parse also has push notification that enables the clients in mobile applications to engage the device. Through the development of the features as mentioned above, the Parse IoT device has been created into a powerful platform connecting objects, things, and people.
Facebook has also been utilizing the use of augmented reality in the advertisement. Facebook allows its customers to incorporate augmented reality in their ads on Facebook mobile News Feed, encouraging customers to interact and engage with their products in new ways (Bonetti, Warnaby & Quinn 2018). Such allows the target consumers not only to experience the product but also to find more information regarding the branding. Consequently, the strategy drives consumers to make a purchase. The development of augmented reality is characterized by shareability. As soon as the Facebook user taps on augmented reality advertisement and successfully interacts with the brand, they are legible to take photos or videos and share with others in the News Feed. Augmented reality provides share effect and instant experience, properties that entice Facebook users.
The main takeaways based on the use of big data augmented reality, and IoT highlights on the stretch that Facebook has made in enhancing advertisement in their platform. Thus, companies that desire to promote their products require to invest in such technological strategies. Facebook has been able to acquire information about its users' personal lives through the applications. The use of big data, IoT, such as Parse and augmented reality, limits the costs of advertisement by reducing the cases of wrong exposure by linking various applications of their customers.
References
Bonetti, F., Warnaby, G., & Quinn, L. (2018). Augmented reality and virtual reality in physical and online retailing: A review, synthesis and research agenda. In Augmented reality and virtual reality (pp. 119-132). Springer, Cham.
Karagiannis, V., Chatzimisios, P., Vazquez-Gallego, F., & Alonso-Zarate, J. (2015). A survey on application layer protocols for the internet of things. Transaction on IoT and Cloud computing , 3 (1), 11-17.
Lee, I., & Lee, K. (2015). The Internet of things (IoT): Applications, investments, and challenges for enterprises. Business Horizons , 58 (4), 431-440. https://doi.org/10.1016/j.bushor.2015.03.008
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data , 2 (1). https://doi.org/10.1186/s40537-014-0007-7
