The so-called “ Internet of Behavior ” or IoB ( Internet of Behaviors ) is a concept that appeared for the first time in 2012, when Göte Nyman , a professor at the University of Helsinki, coined the term in one of his articles. Nyman's idea was to use technology to track people's behavior and build models that help determine and understand the factors that drive a given behavior or act. At that time, Nyman did not consider the potential of the new concept in its applications in product development, commerce, marketing or advertising system.
This concept, despite its enormous potential, fell into oblivion, remaining latent until 2021 when the consulting firm Gartner rescued said term, giving it a new business vision and placing it as number one in the Top-10 of strategic technological trends at the global level. world . This fact caused the IoB to capture the attention of the technological giants.
The correct understanding of this concept, as well as the appreciation of its enormous applicability, necessarily involves understanding two concepts, closely related to the IoB :
- the Internet of Things (IoT)
- the Internet of Everything (IoE)
The IoT , a technology that has become very relevant in the last decade, is a concept associated with control, communication and data exchange between devices. This technology can be understood as the natural extension of the “ Machine to Machine ( M2M )” concept and this has been produced, mainly, thanks to advances in wireless communications and cheaper manufacturing and miniaturization of electronic devices.
On the other hand, the IoE is a broader concept, as its name suggests, expanding the idea of the IoT. This concept describes a more complex ecosystem where machines, people and processes interact with each other at different levels. Thus, while the IoT deals with the connection between devices, the IoE is responsible for connecting these devices and their actions with people.
Based on this reference framework established by the IoT and the IoE , what the IoB seeks is to understand users, from a psychological and behavioral point of view, through the analysis of data generated by their interaction with the different devices and actions of the systems around you.
3rd - PartyAds is the most widespread advertising and digital marketing business model on the internet. Websites use this model to earn advertising revenue, and it also offers a simple and cheap way for small businesses, which cannot launch large advertising campaigns (which are less and less common due to their ineffectiveness), to get their ads to suitable consumers.
This model has three main parts:
- the company that wants to advertise
- the site (website or app) where the ad appears
- the ad server (server ads) with the learning algorithm.
Its operation is very simple. The company that wishes to advertise provides the advertisement in different formats (banners, pop-ups, etc.), establishing the profiles of its potential clients and specifying the types of web and/or applications where it wishes to display its advertisements. The website or application provides the place of the ad and access to user data (IP, name, etc.) as well as the data generated from the interaction (clicks, scroll, display time, cursor position, etc. .) users have with the website or app because of the ad.
Finally, the ad server collects all this data and the learning algorithm analyzes it, learning from user behavior and showing only those ads that are of most interest to the user. As you can see, this business model is based on the effectiveness of who the ad is shown to rather than the number of times an ad is shown.
It is easy to see that in this model a kind of primitive IoB emerges, applied on a small scale in a two-dimensional world (mainly through mobile or computer screens).
In the case of 3rd-PartyAds , the business model is limited to showing the most appropriate ads to users in terms of theme, form and time. This limitation is imposed by the type of data being accessed.
Now, let's imagine for a moment, the possibility of accessing a broader spectrum of data, those that come from devices that have a close interaction and/or relationship with the user: mobile phones, smart watches, home automation sensors, video, audio , etc. In this case, data analytics, from the point of view of a psychological and behavioral understanding of the user, would imply a deeper understanding of users and would open up new approaches in the development of products and services, as well as in marketing and advertising. For example:
- New perspectives are opened in the development of the User Experience (User Experience, UX), allowing to improve factors related to design, content quality, usability, usefulness, etc.
- A psychological and behavioral vision of the user would allow us to Optimize the User Experience (User Experience Optimization, UXO, or Search Experience Optimization, SXO), looking for the optimal form of interaction with a product or service.
- Applying the concept of IoB in the field of SEO ( Search Engine Optimization ) would allow the development of new SEO strategies to improve search engine positioning based on the interaction that the user presents with the search engine.
- Analyzing and understanding customer behavior in a store would help to lay out the store in a more efficient and customer-friendly way . The store could be arranged in such a way as to facilitate access and purchase of specific products or services.
Although the IoB has been postulated as a tool of great importance in the development and sale of products, this would only imply remaining on the surface of this novel concept, having other potential applications in very diverse fields. For example:
The IoB could help detect psychological problems through non-invasive techniques. For example, autism spectrum disorders have great difficulty in being detected if they are not very accentuated. The IoB opens the possibility of introducing people into controlled environments, where it is possible to measure all kinds of interaction of that person with the environment that surrounds him, which would allow patterns of behavior and understand the actions carried out, being possible to detect this type of disorder type with proper analysis.
- Safety in buildings and large areas
The evacuation of buildings and large areas in emergency situations is a critical situation where people behave very differently, behaving more as a group (a swarm) than as an isolated individual. The possibility of collecting data from a large number of individuals, coming from different devices: mobile phones, movement sensors, CO2 concentration, temperature, etc., would allow us to develop models of group behavior. This would allow us to develop building evacuation models in case of an emergency. Go even further, design buildings that favor certain behaviors in emergencies.
- Optimized augmented
reality Augmented reality is a technology that in the coming years will provide very useful tools in a large number of situations and hostile environments for users (handling of dangerous goods, metallurgical factories, high voltage installations, etc.). This augmented reality would allow tasks to be carried out remotely in this type of environment or situation, avoiding great risks for workers. Augmented reality systems contain a large number of devices that interact with the user. The application of the IoB in these environments would be a very useful tool to optimize these environments and improve their usability.
- Fraudulent detection in casino
A casino can be considered a controlled environment, to a greater or lesser extent, where you can observe how players interact. The analysis of the behavior of the players and their interaction during the game would allow the detection of unusual activities in a casino that could be associated with illicit and/or fraudulent activities.
As we can see, the IoB is presented as a disruptive concept, providing a new approach to data analytics and facilitating the development of a very useful tool in the study of human behavior.
The main problem with IoB is the fact of data, currently considered the new oil. The problem lies in how this enormous amount of data that is expected to be collected from individuals is going to be stored, organized, protected and accessed. The fact that this user information falls into the wrong hands opens a very thorny debate on privacy and questions the security in the use of the IoB.
Another issue that causes reticence in the use of the IoB is not the fact that data leaks or theft occurs, but in the ethical use of these. The companies, to which we have given permission in the collection and use of this data, would have a deep understanding of us with a proper analysis of that data. Companies may design products, services, or activities to influence our behavior and indirectly bias our actions or decisions. This has already happened, albeit on a smaller scale, with the Cambridge Analytics scandal, where it was concluded that the company had used the Facebook data leak and its analytics to influence public opinion and bias voting.
As can be seen, the IoB is a disruptive concept that opens up a large number of possibilities. However, it presents great risks if it is used improperly. For this reason, a correct regulation in how data is treated and used is of vital importance so that this concept can provide society with the large number of benefits it possesses.