The Internet of Behaviors (IoB) is a label for collecting data about what people do, online and in the physical world, and analyzing it to understand, predict or influence their behavior. The data comes from websites, apps, purchases, connected devices, wearables, vehicles and workplace systems; the analysis uses familiar analytics and machine learning (ML) tools. IoB is not a product or a standard. It is a way of describing behavioral data programs, which is useful mainly because it puts the business opportunity and the privacy questions in the same frame.
At a glance
- IoB describes the practice of turning data about people’s actions into decisions; it is not a specific technology.
- Data sources include web and app activity, transactions, contact center interactions, IoT devices, wearables and vehicle telematics.
- Common uses are personalization, churn prediction, usage-based pricing and improving customer journeys.
- Behavioral data is often personal data, so privacy laws, consent and data governance shape what is allowed.
- Benefits depend on clear goals and data quality; poorly governed programs risk customer distrust and regulatory problems.
What problem it solves
Most organizations already hold a great deal of data about how customers and users behave: what they browse, buy, click, call about and use. It usually sits in separate systems, so decisions are made on partial information, and customers get offers that ignore what they just did or service that doesn’t reflect their recent problems.
IoB thinking connects those signals to decisions. A customer who has visited the cancellation page twice and called support this week can be routed to a retention specialist. An insurer can price a policy on actual driving behavior. A digital team can see exactly where users abandon a form. The aim is to act on what people actually do, not on broad segments or assumptions, while staying within what customers have agreed to and what the law allows.
How it works
Collection. Behavioral data is gathered from many sources: website and app analytics, purchase and billing records, CRM and contact center interactions, Internet of Things (IoT) devices, wearables and fleet telematics. Each source should have a clear purpose and, where required, notice or consent.
Integration. The data is linked to a person or account in an analytics platform, data warehouse or customer data platform (CDP). Identity matching is often the hardest step and the one with the most privacy implications.
Analysis. Analytics and machine learning models look for patterns, predict likely actions such as churn or purchase, and flag unusual behavior. Models need checking for bias and accuracy, especially when they affect pricing, eligibility or employment.
Action. Insights feed back into systems that act: personalized content, next-best-action prompts for agents, routing rules, pricing or alerts. Results are measured, and the loop repeats.
Governance. Data governance defines who may use which data for what, how long it is kept and how individuals’ requests are handled. Behavioral data frequently counts as personally identifiable information (PII).
When it matters for buyers
- Customer experience programs. Using recent behavior in routing, agent screens and offers can make service more relevant; our analytics and business intelligence overview covers the platforms that turn behavioral data into reports and predictions.
- Privacy compliance. Laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) set rules on notice, legal basis, opt-outs and individual rights. Requirements vary by jurisdiction; involve your privacy team or counsel early.
- Employee monitoring. Using behavioral data about staff raises additional legal and trust issues, and rules differ by country and state.
- Vendor data use. Check whether analytics and platform vendors use your customers’ data for their own purposes, such as training models.
- Trust. Personalization that feels intrusive can damage relationships even when it is legal.
Questions to ask vendors
- What behavioral data does your platform collect, from which sources, and where is it stored and processed?
- How do you support consent, opt-outs and individuals’ requests to access or delete their data?
- Can we limit which data is used for which purpose, and is that enforced in the product?
- Do you use our data, or our customers’ data, to train models or for any purpose beyond our service?
- How do you test models for bias and accuracy, and can we see why a prediction was made?
- How long is behavioral data retained by default, and can we change it?
How it differs from the Internet of Things (IoT)
The Internet of Things (IoT) is the network of connected devices and sensors, such as thermostats, meters, cameras and trackers, that collect and exchange data. IoB is about people rather than devices: it uses data on human actions to understand and influence behavior. Some of that data comes from IoT devices, such as a car’s telematics unit or a fitness tracker, but much of it comes from websites, apps, transactions and service interactions that have nothing to do with IoT. An IoT project can run without any behavioral analysis, and an IoB program can run without any connected devices.
Problems this helps with
- We collect lots of customer and device data but can't turn it into decisions
- Want to personalize service without crossing privacy lines
- Unsure what behavioral tracking our privacy obligations allow
