People generally believe that they make logical, well-informed choices. But user behaviors are rarely as rational as traditional economics suggests. Emotions, the environment, and social expectations often influence behaviors in ways that people do not immediately recognize. This is where behavioral economics really matters. Behavioral economics helps us understand the gap between what people believe they will do and what they actually do. The Internet of Behavior (IoB) takes this understanding further, bridging the gap between psychology and technology, powering businesses with experiences that are personalized in real time.
Let me tell you a story about Steve, a 35-year-old marketing professional who prides himself on making well-informed decisions. However, unseen influences often affect his choices. Today, he’s facing what seems like a simple decision to make: choosing between two shirts his wife has presented to him. Traditionally, his decision would be simple: He would choose based on which color, pattern, or material he preferred. According to traditional economics, Steve would make a rational, straightforward decision that is based solely on these factors.
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But what if Steve’s thought process went deeper, involving additional considerations beyond pure logic. He might think about which shirt was on sale, whether his choice would please his family, or whether it would help him look presentable in front of his guests. He might even wonder whether he could return it if his family didn’t like it.
Behavioral economics reveals that people’s choices are shaped by a blend of rational and irrational influences such as their values, social expectations, emotions, and environmental cues. So Steve’s decision was not just about color or material but about how he felt, how he thought others would react, and how he valued each factor.
Shifting to the Internet of Behavior
The Internet of Behavior (IoB) helps us apply the behavioral economics that influenced Steve’s decision at a much larger scale and in real time. IoB benefits businesses by enabling them to build a robust brand or product. IoB would analyze Steve’s behavioral data such as his purchase patterns, search history, social-media feedback, location, time of day, environmental cues, and various other internal and external factors. IoB combines these data-driven insights with behavioral factors to uncover why users make certain choices and helps us reshape Steve’s purchase experience into a favorable one. We can use IoB to offer Steve a tailored experience and present guided choices to help meet his needs as well as business objectives. Figure 1 illustrates IoB as the intersection of psychology, economics, and the Internet of Things (IoT), where IoB leverages insights from these domains to understand and shape user behaviors precisely.
Figure 1—IoB at the intersection of psychology, economics, and IoT
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Flow of Data and Insights
Built on top of IoT, IoB extends beyond IoT’s limitations. While IoT is limited to data gathering, processing, and sharing, IoB elevates its capability by integrating psychological frameworks. This approach transforms real-time data into insights and wisdom that helps us build dynamic, responsive predictive models with personalized interventions that influence user behaviors. This continuous adaptation provides an advantage by enhancing engagement, boosting the user experience, and influencing user behaviors that align with business goals. Figure 2 illustrates the flow of data and insights from IoT devices to IoB applications.
Figure 2—Flow of data and insights in IoB
The Role of IoT as a Data Source
IoT collects raw data from various sources—for example, gathering data from consumer applications for industrial and agricultural ecosystems—which makes it a powerful tool for generating real-time insights. IoT can collect raw data from the following sources:
sensory devices—Through sensory devices, IoT can gather environmental data such as weather, temperature, pressure, humidity, pollution, motion, and proximity.
wearables—From wearables such as fitness trackers, smart watches, and medical devices, IoT can monitor and capture data about user behaviors, usage patterns, physical and mental activities, health metrics, location, and infrastructure.
industrial and agricultural equipment—From industrial and agricultural equipment such as smart irrigation systems, precision farming tools, and predictive maintenance systems, IoT can gather data regarding equipment location, health and performance, soil condition, animal health, activities, location, and more.
Contribution to IoB
IoT continuously generates data from various sources, serving as the basis for IoB applications’ ability to understand and influence user behaviors. In response, IoB contributes to IoT operations by feeding them with user insights that support optimization.
The Role of Big Data as the Analytical Backbone
Data-analysis technologies such as artificial intelligence (AI), machine learning (ML), and big-data analytics play a pivotal role in processing and extracting insights. Within the context of IoB, big-data analytics play a key role. The vast amounts of data that IoT gathers can be high volume, unstructured data that flows at high velocity. Big-data tools receive this unmanageable data and clean up, process, analyze, and convert the data into valuable insights with a recognizable structure, patterns, identifiable trends, risks, correlations, and anomalies.
Contribution to IoB
Big-data tools such as machine learning, advanced analytics, data visualizations, and real-time processing frameworks can analyze the big data that IoT generates and convert the data into manageable insights. These insights enable IoB to identify trends and personalize user experiences effectively.
The Role of IoB in Generating Behavior-Focused Outcomes
While IoT generates data and big-data tools convert the data into insights, businesses can leverage these insights to predict, adapt to, and influence users’ decisions in real time. IoB applications integrate behavioral science with technology to ensure decision alignment with evolving user behaviors and expectations.
Contribution to IoB
IoB uses tools such as behavior-analytics platforms, customer-data platforms, predictive analytics, personalization engines, AI-driven recommendation systems, and contextual-computing platforms to drive user-centric strategies in various industries such as automobile manufacturing, healthcare, retail, and smart cities.
The Role of IoB in Enhancing Behavioral Economics
Behavioral economics is a combination of economics and psychology and helps us understand how people make decisions under various circumstances. Behavioral economics digs deeper into human’s mental models, biases, and the environmental factors that influence user behaviors to explain why we do not always act as expected.
While behavioral economics relies on past studies, controlled experiments, and feedback, IoB takes this further. It uses real-time data from various touchpoints such as Web sites, applications, and IoT devices and analyzes that data. It identifies patterns in user behaviors based on how people behave in different contexts such as locations, times of day, surrounding events, and influencing factors. IoB uses this data to create easy-to-use experiences, influence decision-making more precisely, and shape our behaviors in ways that feel natural to us. IoB operates on real-time data at scale by leveraging behavioral principles. It helps build healthier habits in people, optimize business’s market strategies, craft highly adaptive user experiences, and tailor interventions for instant decision-making. Figure 3 illustrates how IoB’s interventions benefit various industries.
Figure 3—IoB Interventions in various industries
Conclusion
Although IoB is revolutionizing how businesses interact with their customers, it also raises this question: “Is it possible to adapt this technology without encountering ethical concerns?”
The ability to access people’s personal data such as age, location, medical records, and behavioral data can pose major privacy concerns. With the ability to access, analyze, and handle sensitive data comes greater responsibility. Guarding the fine line between using and misusing or overusing this vast amount of data requires constant surveillance, transparency, and user control. Because of the power of this data, it becomes easier to manipulate users unintentionally, pushing them toward certain desired outcomes that might not be in their best interests. Therefore, it is essential that businesses follow responsible design principles to avoid ethical pitfalls, as follows:
transparency—Be transparent about data usage with users.
user consent and control—Get users’ consent before collecting their data and provide the ability for them to manage or delete the data, if necessary.
fairness and bias audits—Conduct regular audits, both internally and externally, to check for fairness in the data and avoid any discrimination.
data security and privacy—Implement strict security policies and encryptions to prevent data from breaches and misuse.
By balancing IoB and ethical principles, you can potentially enhance the user experience in ways that drive business success and positive behavioral outcomes, without compromising on privacy or trust. When you handle IoB responsibly, it can foster long-term user engagement, build brand reputation, and engender social benefits.
Betina has more than twelve years of professional experience as a UX designer, working on digital media for various services. Her broad range of experience lets her create visually compelling, highly usable designs. She optimizes products by creating designs that meet user needs and address business challenges. Betina cares about both design details and discovering new possibilities. She enjoys working with developers to build out her ideas. Read More
