What is the Internet of Behaviour (IoB) and why is it the future?

The Internet of Things (IoT) is a network of interconnected physical objects that collect and exchange information and data over the Internet. The IoT is constantly expanding and evolving in the scope of its complexity, i.e. the way in which devices are interlinked, the computations that can be processed by these objects autonomously and the data that is stored in the cloud evolve in a more complex way. Data collection (BI, Big Data, CDPs, etc.) provides valuable information about customer behaviours, interests and preferences, and this has been referred to as the Internet of Behaviour (IoB). The IoB attempts to understand the data collected from users’ online activity from a behavioural psychology perspective. It seeks to address the question of how to understand the data, and how to apply that understanding to create and market new products, all from a human psychology perspective.

IoB then refers to a process by which user-controlled data is analysed through a behavioural psychology perspective. With the results of that analysis, it informs new approaches to designing a user experience (UX), search experience optimisation (SXO), and how to market the end products and services offered by companies. Consequently, for a company to conduct IoB is technically simple, but psychologically complex. It requires statistical studies to be conducted that map everyday habits and behaviours without fully disclosing consumer privacy for ethical and legal reasons.

In addition, IoB combines existing technologies that focus on the individual directly such as facial recognition, location tracking and Big Data. It is therefore a combination of three fields: technology, data analytics and behavioural psychology.

What does the IoB mean and contribute?

The purpose of the IoB is to capture, analyse, understand and respond to all types of human behaviours in a way that allows tracking and interpreting those behaviours of people using emerging technological innovations and developments in machine learning algorithms. People’s behaviours are monitored and incentives or disincentives are applied to influence them to perform towards a desired set of operational parameters. What is really relevant about IoB is that it is not only descriptive (analysing behaviour), but proactive (detecting which psychological variables to influence to bring about a certain outcome).

The IoB influences consumer choice, but it also redesigns the value chain. While some users are wary of providing their data, many others are happy to do so as long as it adds value – data-driven value. For companies, this means being able to change their image, market products more effectively to their customers or improve the Customer Experience (CX) of a product or service. Hypothetically, information can be collected on all facets of a user’s life, with the ultimate goal of improving efficiency and quality.

Fundamentally, for a company to make use of IoB in its marketing department, the following aspects are necessary requirements. Firstly, before the creation of the application, it is important to imagine the user’s interaction patterns and touch points. Involve them in the creation process, understand their needs, keep the app experience unified and cohesive, make navigation simple and meaningful so that the app is relevant and useful. Once the app is up and running, convey its purpose, create a user guide and reward by gamifying the CX in the app. Strong user engagement is required.

Secondly, solid tools are required such as multi-format support platforms (XML, JSON, PHP, CVS, HTML, etc.), that can connect to any API, that can upload data to the cloud, i.e. the fundamental features of platforms such as Google or Facebook. Platforms should allow for multi-channel personalisation, centralised updates that are replicated, sending unique notifications that turn users into contributors to the personalisation of the app, allow for social media integration and maintain an interactive interface.

Finally, it is the data captured through the app that serves to model user behaviour. And in turn, this is the actionable data that can be sent in the form of pop-ups and notifications to the customer to encourage and incentivise them to adhere to a desired behaviour. Analytics are necessary so that essential information can be extracted from all the data.

 The value of IoB and its ethical use

Through Big Data, information can be accessed from multiple points of contact. This makes it possible to explore the CX from start to finish, to know where the customer’s interest in a product begins, their journey to purchase and the methodology used to make the purchase. This provides the ability to create more touch points to positively engage with the consumer. This personalisation is key to the efficiency of a service. The more efficient a service is, the more the user will continue to interact and even alter their behaviour as a result.

The specific benefits of IoB are:

• Analyse customer buying habits across all platforms.
• Study previously unattainable data on how users interact with devices and products.
• Obtain more detailed information about where a customer is in the buying process.
• Provide real-time POS notifications and targeting.
• Resolve problems quickly to close sales and keep customers happy.

The problem that can occur with this technology is not of a technical nature. The IoB is confronted with the adversity of how data is collected, stored and used. Its level of access is difficult to control and therefore all companies need to be aware of the liability of IoB use. Google, Facebook or Amazon continue to acquire software that potentially brings the user from a single app to their entire online ecosystem, without their permission. This presents significant legal and security risks to privacy rights, which also vary between jurisdictions around the world.

Behavioural data can allow cybercriminals to access sensitive data that reveals customer patterns, collect and sell property access codes, delivery routes and even banking codes. These cybercriminals could take phishing to another level by generating more advanced scams, tailored to the habits of individual users, and thus maximising the likelihood that users will be scammed. It is therefore important to have a secure platform, storage and execution of data with the use of tools such as Confidential Computing, E2E encryption or SDP tools.

Case studies

It is not difficult for companies to link a mobile phone with a laptop, with a voice assistant, with a smart home or with their vehicle. Marketing research from Google, Facebook or Amazon is becoming more and more comprehensive. The algorithms of these companies are configured so that they can anticipate customer desires and behaviours. The B2B sector is developing faster than B2C in IoB but it is only a matter of time before it becomes ubiquitous.

For example, software company BMC has developed a health app for smartphones that tracks diet, sleep patterns, heart rate or blood sugar levels. The app can alert to adverse situations in the user’s health and suggest behavioural modifications towards a more positive outcome. Health Passport (with apps such as Aarogya Setu in India, and The Health Code in China) and Social Distancing Technologies are partners in this emerging health technology.

In relation to transportation, Uber, for example, uses IoT data on drivers, passenger locations and preferences to reinvent CX. Also, large companies, such as Ford, have joined other start-ups, such as Argo AI, to design autonomous cars that vary their behaviour in each city based on vehicle traffic, pedestrians, bicycles and scooters.

Another software company, GBKSOFT, has carried out a project that also implements the IoB concept. The essence of the project was to help golfers improve their playing skills with the help of a mobile application and tracking of wearable devices, namely correcting existing ball striking technique and learning new techniques.  Making use of a handheld device connected to the mobile phone, each hit on the golf ball is recorded in the app and analysed (stroke force, trajectory, angle, etc.). As a result, the player can see their mistakes and get visual recommendations on how to improve their swing and stroke.

Conclusions

Undoubtedly, A/B testing, SWOT analysis and many other techniques have helped companies for years to build their product and marketing strategies to create and promote that users would want to buy. The IoB will take this trend to the next level, and is set to generate considerable momentum in the development of the sales industry. According to Gatner, the technology may still be in its early days, but by the end of 2025, more than 50% of the world’s population will be exposed to at least one IoB programme, either from the government or a private company. It will be the ecosystem that defines human behaviour in an increasingly digital world.

For this reason, it will be essential to strike a balance between personalised offerings and intrusiveness to avoid adverse consumer reaction. Any company that chooses to adopt an IoB approach to its strategies must ensure that it has robust cyber security in place to protect all that sensitive data.

IoT-harvested data leveraged with IoB technology can be used to sell, but it’s not all targeted advertising. Organisations will be able to test, for example, the efficiency of their campaigns, both commercial and non-profit. Also, healthcare providers can measure patient activation and engagement efforts. In conclusion, its catalogue of applications is already extensive, but it will continue to expand as it becomes established in society.