Three Ways the Internet of Things Is Shaping Consumer Behavior
The interconnection of devices within the “Internet of Things” (IoT) creates new data sources. Companies can now better observe people’s choices and test the effectiveness of different mechanisms to activate and retain more customers. It may also help policymakers overcome one of the most frequent problems of policy design: the lack of personalized content. We argue that the IoT not only disrupts the way we track our actions and monitor our goals, but also allows the identification of effective methods to alter our behavior. This is optimized by the combination of IoT, data analytics and behavioral science.
One of the main contributions of behavioral economics to the study of consumers is its empirical focus on observed behavior. Hence, behavioral specialists in the areas of marketing, economics and public policy should be aware of the possibilities that new technologies create for the analysis of consumer behavior. Today’s consumers produce (directly and indirectly) an abundance of data. Optimal commuting decisions and advertisement locations can now be inferred from call details and “over-the-top” digital records produced by more than a billion cell phones that emit 18 exabytes (1 billion gigabytes) of data every month. The web and mobile apps, which can track anything from dietary choices to banking transactions, have the potential to replace a large number of existing self-reported consumer surveys. Data collection capacity is added to more and more objects found in the physical world. Sensors are now positioned in our cars, (smart) homes, and even our clothes (wearables). This movement from the world of the Internet of People (IoP) to the Internet of Things (IoT) exponentially increases the data that is being generated.
The IoT records and transmits personalized information. This means that providers can collect observational data from their users’ everyday behavior and, by experimentation, identify which techniques and interventions are more effective. Next, we describe three ways in which the IoT will continually influence consumers’ choices through these data collection channels.
A number of machine learning algorithms deal with predictive modeling. For example, a Fitbit tracker measures data such as the number of steps walked, heart rate, quality of sleep, steps climbed, and other personal metrics involved in fitness. While health apps are not able to replace a doctor just yet, they can accurately tell us when we need one. Wearables of this kind can feed a cloud-based machine learning algorithm which would help us notice fluctuations in our pulse that correspond to a specific health condition. Other types of wearables can monitor levels of blood sugar and analyze its metrics with cloud applications, and send immediate notifications to the user reminding her to take a medication or adjust daily sugar intake. In short, IoT data allows us to identify the major predictors of an outcome using metrics that are essentially linked to behavior.
A noticeable business application of machine learning to affect consumer behavior is the dynamic offer of travel products and services. Providers of travel services, such as Booking.com, have recommendation systems trained with data from users’ socio-demographic characteristics and past online behavior. With the newly launched Booking Experiences app, a traveler can get instant booking access to participating venues and attractions at a specific destination. Travelers do not need to book in advance or wait in line to buy tickets. They simply need to scan a QR code with their smartphone that is linked to their credit card of choice.
The Booking Experiences app learns over time and combines this knowledge with geo-location data to provide a traveler with increasingly personalized just-in-time suggestions to enhance the in-destination experience.
