The iPhone can be considered the cornerstone of the models we're talking about. The first major enabler of thousands of digital services that have figured out how to leverage data from its sensors to capture - and not just create - value. It's interesting to see how in this story, Apple has an even more primordial role, with one of its first - and most iconic - partnerships back in the days of the iPod: Nike+. The sensor and the iPod kit were revealed on May 20th, 2006. The kit stores information such as the elapsed time of the workout, the distance traveled, the pace, and the calories burned by the individual. Nike+ was a collaboration between Nike and Apple. The platform consisted of an iPod, a wireless chip, Nike shoes that accepted the wireless chip, an iTunes membership, and a Nike+ online community membership. On September 7th, in 2010, Nike released the Nike+ Running App on the App Store, which used a tracking engine powered by MotionX that does not require the separate shoe sensor or pedometer. This application works using the accelerometer and GPS of the iPhone and the accelerometer of the iPod Touch, which does not have a GPS chip. During our research into the business models of free apps - such as Nike+ - we studied privacy policies as a mean of understanding whether and how these companies might use the data collected by the apps. And in the case of Nike+, we found a pleasant surprise. Unlike many other cases, in this case there was explicit mention that the data collected would NOT be used for advertising purposes or given to third parties, but could be used within the company as a whole, and not just in the digital subsidiary Nike Digital. As Stefan Olander, Nike’s Vice President of Digital Sport, told an interviewer, “The opportunity that is presenting itself right now is completely different, regarding the relationships we have with the customer, and this is something we learnt from Nike+ already. It is so much more impactful when someone comes back to the brand two or three times a week to sync a run, versus what it used to be which was convince someone that we had something amazing—which we do—then you buy it and you have a great experience with the product but we don’t know anything about that experience”. In other words, user data is a precious source of valuable insights into users’ habits, needs, and interactions with the company’s products; user data streams allow for large scale observation of thousands or even millions of user interactions with a product. Further, these observations are non invasive, since they are embedded in the user’s experience. Where the app supports a physical product, the benefits can extend far beyond the app itself. Nike uses its Nike+ Running customers to understand how people use its core product: shoes. App users provide a huge amount of information about their workout habits that allows the company to profile its customers and understand critical attributes. For instance, the company knows that men in their late 30s run an average of 37.8 km/week, with an average single run distance of 6.2 km at an average pace of 6:56 per mile. Most people run mainly on Sundays and the most frequently selected “power song” is “Eye of the Tiger.” All of these insights were collected from data gathered through the app. The relevance this data can have for an established company can explain the absence of direct revenue sources for some of the apps we studied—these apps may exist primarily to provide support for the core business, rather than to generate direct revenue, by providing data from which the company can glean valuable insights about how its customers actually interact with the physical product. In other terms, the platform consists in Nike+, the mobile app released and distributed by the digital division of Nike, and has two customers: the end users, the runners, and the overall company itself that uses the data. In this case, the strategy is purely Client-as-a-Source and it is basically within the firm, involving different functions or part of the organization, but still generating cross-side network externalities, since the value of these data is directly related to the number of data points. This strategy takes the name of e-ethnography, inspired by the world of ethnography, the methodology born in the anthropological field, observing the various populations to understand their behaviors. In business we talk about applied ethnography and it's a methodology commonly used in market-push innovation, observing the users to understand how they use a product in order to propose innovations that tend to be incremental and satisfy emerging needs. This is exactly what happens in this type of platforms, observing - albeit in the background, hence the name e-ethnography - the user and having the possibility to develop new products, in the core area of the company, based on the insights collected. This mechanism, the information collected on the usage of a product such as in this case shoes, allows you to capture value without direct monetization, justifying the free service. It's not easy to find examples of companies using apps in this way, as there's often no clear evidence of it from the outside, but it's important to consider it among the strategies we can use to capture value from the data in a platform. Another example? It seems that Netflix uses data on its users' habits and what programs they actually watch, when they pause, how long it takes them to finish a series, and what basket of movies and TV series a person sees not only to decide whether to renew a series or not, but also to understand what kind of products users are looking for, turning them into actual inputs for generating new content.