Michael Steep’s scientific contributions

Academics and businesses alike tend to fail at understanding how the IoT revolution is monetized. We outline three main categories of how IoT will impact business models: (a) improved customer matching and tracking of marketing returns, (b) individualized offers and pricing when consumer demand and price elasticities can be identified, and (c) smart device and usage monitoring that allows for outcome-based contracts and servitization. Data convergence creates context-based-intelligence, which enables a shift from using consumer profiles for targeted advertising to individualized offers and pricing. The required depth of both consumer data and understanding of context will require collaborative efforts between companies and blur the lines between industrial- and consumer-IoT applications. Outlining concerns for privacy and cybersecurity, we find that consumer demand for decision-simplicity and relevant content aligns with the business model of “free” services in return for data, despite consumer concerns relating to data collection.

We analyze mobility within cities from the perspective of data acquisition and how location-based-services enable companies to set demand and preferences in a geographic context. A key aspect is that movement creates context-based-intelligence when it becomes possible to adjust advertisement and offers based on location, activity and social-context. In terms of impact on on business modeling, we find that a primary impact is that on value proposition and marketing. Smart-devices enables a constant connection and a two-way dialog between company and customer. It will be increasingly important for retailers to effectively reach customers with offers that are continuously updated to maximize the likelihood of a purchase through behavioural-based pricing. Data driven business models are transforming the insurance industry as data from wearable devices and social-media activity determine life-insurance premiums, and car insurance is set by where and when a car is driven. Similarly, credit-risks are now determined through an understanding of purchasing patterns and account-flows, rather than credit-scores. Another category of business impact is that created by the ability to measure device performance and usage. This impacts the entire corporate value chain – notably through outcome-based contracts and servitization when large data and increasingly advanced analytics makes it possible decrease risks associated with guarantees, insurance and leasing contracts. Incrasingly, concerns are raised over both the impact on privacy and cybersecurity, in addition to fairness when pricing is becoming increasingly individualized. We cover the risks, implications and the challenge associated with the fact that even as consumers state that they are concerned about privacy, they also value getting relevant content that is enabled by consumer profiling.

Urban environments are composed of urban population, urban infrastructure, city governance and commercial markets within cities. The rapid growth of emerging technologies for sensing and communicating data is being leveraged by commercial companies to create digital applications where machine learning applications analyze multiple kinds of data now available from instrumented infrastructure, public and private urban transactions and citizens' mobility to transform urban environments. This kind of transformation is our view of what enables a "digital city". Commercial markets are at the heart of this concept, with commercial applications of digital infrastructure rapidly developing, because data from multiple sources are more easily available and analyzed across multiple data layers drawn from different sectors and regions of the city. It is now possible to visualize multiple kinds of outcomes across an entire city and its markets, and to do "What if?" analysis using predictive analytics to generate new insights and financial models across a wide range of vertical urban services. The ability to visualize real time data and insights drawn from that data about the urban environment that surrounds real estate and identify its connection with real estate value provides an unprecedented potential for enhancing real estate development decisions, primarily through better forecasts for building utilization, more accurate assessment of the purchasing power of users of real estate, and by better risk assessment of real estate users. This article presents an analysis of the potential benefits of digital cities for real estate development decision making.