The Internet of Behavior (IoB) is a concept that is emerging, revolutionizing behavioral tracking like we’ve never seen before. Gartner predicts that over half of the world’s population will be in contact with IoB technology by the end of 2025. The result? Estimates from Precedence Research say the global market will grow at a CAGR of 24.97%, hitting a massive $3.59 trillion worth by 2032.
Now, contributing to its massive adoption and market growth on the one hand is governmental influence. On the other hand, however, is its utilization for commercial purposes — its implementation by businesses like yours into different segments of business operations.
Keep on reading as we share what exactly the IoB is, how it benefits you, and how you may use this new technology to improve business outcomes.
What Is Internet of Behavior (IoB)?
The Internet of Behavior is considered a sub-application of the Internet of Things (IoT), a concept that involves analyzing and monitoring individuals or groups' behavior patterns through the use of the internet. It combines technology, human psychology, and behavioral analysis to understand users' behavior and predict their actions.
With nearly 3 billion people using the internet globally, IoB systems have access to a wealth of data that can be utilized to improve productivity and enhance product and marketing strategies. By tracking and analyzing users' online activities, businesses can gain valuable insights into consumer preferences, interests, and needs.
This information can then be used to tailor product offerings, improve customer experiences, and develop targeted marketing campaigns. IoB has the potential to revolutionize the way companies understand consumer behavior and make informed business decisions based on real-time data. However, it also raises important ethical concerns regarding privacy and data security, which must be addressed for the responsible implementation of IoB systems. .
IoT sensors and IoT-powered systems are used to collate, store, and analyze data that specifically presents insights into the behavior of device users. These behavioral insights may then be used to understand consumer psychology and preferences, and to generate patterns for predicting future business requirements.
How is IoB implemented?
IoB implementation follows the same flow of general IoT use. It involves collating data from multiple intelligence sources like smartphones, social media, and wearables. The collated data is transferred to the cloud for storage. Once in the cloud, it may then be processed through AI-powered big data analytics to generate insights.
The more advanced IoB applications involve the use of machine-learning algorithms to create contextual insights for more accurate behavioral analytics. An application may then be used to visualize data and insights for faster interpretation.
For businesses, the key utilization of IoB is in personalizing business efforts. As identified by IBM, it comes with tremendous financial benefits, as personalization sets businesses up for a potential 400% return on investment (ROI) from marketing campaigns.
In short, IoB is the use of IoT to understand behavioral psychology, knowledge of which businesses can use to drive operational efficiency and tap into new opportunities.
14 ways businesses can utilize IoB
From improving marketing results to bolstering cybersecurity and getting the most from employees, here are the top ways IoB can be applied in business.
1. Personalized advertising
On the side of advertising, IoB proves to be specifically effective for bolstering retargeting efforts. This is evidenced by an Adlucent survey showing that 71% of customers prefer to see ads that are tailored to personal interests and shopping habits.
Here, businesses collect, store, and analyze behavioral data on each customer’s behavior such as:
- Frequency of visits
- Most explored pages
- Time spent on web pages.
The goal is to understand what interests each customer and their levels of interest in it. This way, you create customer profiles based on these interests. Adverts are presented to customers based on the customer profile they fit in.
For example, do you run an e-commerce website and have certain customers visit the gaming section more? IoB will help you discover this and allow you to apply more relevant gaming-related adverts to retarget these customers.
2. Improving target audience reach
The target audience is the group of individuals expected to use a business’s products or services. It is the group to which marketing efforts are directed.
Sadly, there is a barrage of challenges regarding the target audience. Common challenges include:
- Identifying who uses certain products
- Identifying internet users that are similar to these consumers
- Segmenting target customers to fit different profiles.
IoB serves as a solution to these limitations. You can collect data to identify the traits of people who frequently patronize a product or certain products. You can then use this data to target internet users that have similar traits.
3. Personalizing marketing messages
SmartHQ observes that 75% of consumers only engage with personalized messages. Surveys from Hubspot go the extra step to tell us that personalizing messages is the most optimal email marketing strategy, while Salesforce says 52% of customers expect messages to be personalized.
Incorporating IoB allows you to abandon generic messages for more effective personalized campaigns. For instance, businesses can include names in messages or, for more advanced scenarios, remind customers of a landing page they spent a considerable amount of time on without making a purchase.
What’s more, collecting this all-important data for improving personalization isn’t expected to be a hard obstacle. Statista shows that customers are willing to share personal information to receive personalized messages and enjoy personalized experiences.
4. Customizing customer education
Now, diving deeper into personalized experiences, IoB also offers businesses a way to improve customer education and learning.
Here, businesses collect behavioral data on where users or visitors may seem to have problems getting past.
For instance, this may be data showing that a customer may be having trouble checking out a cart or linking a credit/debit card. With this, you immediately swoop into action, offering relevant help by educating on the specific steps to take and what may be the cause of the issues being faced.
5. Predicting demand trends
With historical behavioral data, businesses can predict what customers typically have an interest in at different times of the month or the year.
Combining this with data on changes in purchase frequencies allows you to comprehensively understand and forecast demand trends.
Businesses understand what drives general demand and also predict what product or service each customer is expected to engage in during a given period. This information can help with implementing dynamic promotional campaigns and inventory management, as we will discuss later on.
6. Change management
During change management, IoB can provide the right information to satisfy customers and their expectations. Data on aspects such as feature usage or navigational barricades gives you an idea on areas such as:
- What parts of your product or service are unnecessary
- What parts are crucial and need more attention
- How customer interaction is hindered and can be generally facilitated.
For instance, data showing reduced interaction from Samsung mobile devices can signal a problem specifically faced by these devices. AI-powered IoB analysis then helps to reveal the major pain point, which can be related to Samsung’s OneUI or visualization problems with curved displays. Improvements may then be made to the product or service based on behavioral findings.
7. Inventory management
IoB also allows you to maintain optimal inventory at all times. Through historical data on expected customer demand levels (both generally and individually), businesses can optimize inventory to meet demand requirements. You can prevent wastage and enjoy more efficient inventory management operations and outcomes. Data from Zebra shows that avoiding understocking and overstocking reduces inventory costs by 10%.
8. Supervision of employee work patterns
IoB also has a barrage of applications when it comes to businesses’ internal management of employees.
One of these applications is generating data and information to monitor employee workflows and gauge employee performance. Collecting and analyzing data on how employees go about work can help you understand inefficient or even dangerous work patterns.
An instance of this can be seen in Upwork’s Time Tracking app. The app uses mouse clicks and screenshots to monitor if a freelancer is indeed working and not just fraudulently building up work hours to charge more. The app is also equipped with auto-click detection.
IoB comes with such supervisory capabilities and allows you to keep employees within compliance standards.
9. Monitoring employee health
An innovation of IoT emerging from the healthcare industry, IoB can also be utilized to monitor employee stress levels and general health conditions.
This is made possible through, for instance, IoT-enabled wearables to monitor heart rates for stress and IoT thermal sensors to monitor temperature levels for fevers.
This application is especially useful during viral pandemic eras where diseases are spreading fast, or during high-workload periods where employee stress levels may soar.
10. Boosting employee acquisition
Recruitment software is becoming an increasingly popular tool for employee acquisition. More than 98% of Fortune 500 companies use it, 68% of hiring professionals regard it as the best employee acquisition investment over the next five years, and 94% of recruiters say it positively impacts the hiring process. The application of IoB in improving employee acquisition involves utilizing candidate behavioral data to filter out job applications.
Examples of IoB usage in employee acquisition include:
- Collation of social media data to identify a candidate’s interests and biases
- Analysis of problem-solving skills
- Natural language processing (NLP) to identify writing/communication styles
Businesses may also analyze data from successful/thriving employees to create optimal candidate profiles.
11. Monitoring internal data access behaviors
Moving to cybersecurity, IoB is also useful for both the internal and external protection of IT infrastructures and data. It majorly does this through ML-powered behavioral baselining.
For internal protection, IoB allows businesses to use ML baselines to monitor internal data/infrastructure access and usage.
When abnormal patterns are identified like, for instance, when an admin interacts with an archived database or when an important file is deleted, data from IoB allows businesses to take swift action to prevent further data abuses from a hack.
12. Monitoring external users for abnormal behavior
Businesses can use IoB data to protect customers against fraudulent access to in-app information.
This involves collecting biometric information for external access control and locking up accounts after failed biometric login attempts.
Through IoB, you can identify abnormal customer behavior, such as logins or credit card purchases from new locations, unusually large purchases, or changes in profile details.
13. Improving customer servicing
Just like using behavioral data to improve primary product offerings, businesses can also use IoB to improve customer service operations.
IOB data can be used to identify the root cause of individual complaints, assess the most common customer complaints, personalize customer servicing conversations, and identify the best channel for customer servicing.
14. Logistics: Route planning
IoB can also be implemented into your business’s midstream operations. More specifically, it is useful for route planning as historical data on past logistic operations can help indicate the best routes and third-party partners.
By optimizing route selection based on optimal past results, behavioral data can help reduce fuel consumption, transportation time, and overall logistic costs.
Which challenges to expect with Internet of Behaviors
Regardless of the multiple ways IoB may be applied and all the promise it brings to improving business outcomes, there are certain critical limitations to its adoption. These limitations are specifically centered around the storage and use of behavioral data collected from customers and employees.
1. Data privacy
There are reservations on the part of customers and employees about releasing sensitive data for business use. For instance, health-related data is one of the most sensitive across all industries and only doctors are typically authorized to see protected health information (PHI).
This is a barrier to utilizing health-related IoB technology to boost business operations. In the same vein, a whopping 92% of internet users in the US worry about their privacy online and 44% of Americans don’t trust businesses with their personal information.
2. Data exploitation
Data exploitation is all about the unethical use of data, and customers/employees are also wary of it.
Exploitation involves actions like commingling (repurposing data collated for another use), employees stealing data for personal benefit, and intentional ambiguity in the storage and use of data.
For instance, we see unethical use in Facebook’s 2018 case where personal data collated fromover 50 million profiles was sold to Cambridge Analytica for political advertising purposes. The use of data to influence addictive consumer behavior is also another reservation about the use of IoB.
3. Cybersecurity threats
Finally, there are concerns about the strength of cybersecurity measures and infrastructures against hacks and theft.
Worldwide, over 6 million records have been breached in 2023 alone. The fact that industries like the healthcare and finance sectors with the most sensitive information receive the most cyberattacks makes this even more concerning.
Ethical use of the Internet of Behavior is crucial
The limitations of IoB on data storage and usage can have massive financial effects, and this isn’t just for your customers and employees.
Facebook’s case on unethical data usage resulted in a $5 billion fine for Facebook and Cambridge Analytica closing down.
Businesses in the US may lose up to $6 million or even more per data breach. These hefty financial costs signify the importance of a business protecting IoB data in its pursuit of improved business outcomes.
For strong data protection, please apply these data loss prevention best practices.
Internet of Behavior FAQ
What is the Internet of Behaviors (IoB)?
The Internet of Behaviors, also known as IoB, is a technology concept that focuses on using data gathered from users' online and offline activities to understand human behavior.
How does IoB work?
IoB works by collecting data from various sources, such as IoT devices, wearable devices, and online platforms, and analyzing it using data analysis and machine learning algorithms. This data is then used to create insights and predictions about human behavior.
How can businesses implement IoB?
Businesses can implement IoB by leveraging the concept of the Internet of Things (IoT) and integrating at least one IoB program into their operations. This can involve collecting data on user behavior, analyzing it for valuable insights, and using these insights to drive decision-making and improve user experience.
What are some potential applications of IoB?
IoB can be used in various industries and domains. For example, it can be used by insurance companies to track driver's behavior and offer personalized insurance plans. It can also be used by healthcare providers to monitor patient behavior and provide tailored healthcare services. IoB can also be used in digital marketing to understand user behavior and create targeted marketing campaigns.
How does IoB change the way businesses operate?
IoB has the potential to change the way businesses operate by providing deeper insights into customer behavior and preferences. This enables businesses to create and promote products and services that align with these behavior patterns. The result is a more personalized customer experience.