The role of personal data in co-creation
I work with a lot of analytically-minded people. These people can be colleagues or client, often both. Because analytically-minded firms tend to hire analytical consultants, they often lock in a powerful embrace that makes it hard for either of them to view the world through the lens of co-creation. These are the workshops from hell. Their first objection is often that co-creation feels like a random process of human discovery void of any data. Real men don’t do co-creation, they say. They formulate a hypothesis, gather some analytical data through research that proves the hypothesis is right, may test the idea with some customers or other relevant stakeholders, and then go build whatever needs to be built, be it a product, a process, or a strategy. Data drives the hypothesis, and humans are there to validate the hypothesis.
Co-creation follows a different logic. In co-creation, the hypothesis is the result of the engagement process, and insights are in fact generated jointly by the company and the people it engages (this is the uncomfortable part, the “letting go” part). What is even harder for analytical people to see is that co-creation is equally data-driven as the more traditional company-centric process, product or strategy design, but the data is of a different nature because it comes from the people themselves.
In co-creation, what comes first is the platform, typically a very crude prototype in the early stages. The primary role of the platform is to generate data that can be analyzed and structured, which will then guide the next iteration of design, in effect making the early users of the crude platform into co-creators of the next iteration. When Mark Zuckerberg created the first Facebook site, he devised a blunt instrument that allowed obnoxious Harvard students to rate the attractiveness of female students at Harvard. Was it the ultimate design of the platform? Of course not (and mercifully so!). But what he did was engage a group of campus students on a topic that defined a community (Harvard male students) and a basic platform concept that could evolve from this humble beginning. The first draft of Facebook allowed Zuckerberg and his team to engage into a creative dialogue that generated subsequent interactions, leading to new features such as communicating one’s social status, or sharing photos. Since then, the platform has continuously morphed as a result of a natural Darwinian process where users co-evolve the platform through use.
So what is the role of data in this process of co-creation? Simply put, data is everything. The platform is a data machine. It records who comes to the platform, how long people stay on it, what people do on the platform, and what features they utilize. Platforms can be of a physical nature, like a store, or virtual kind, like a web site, but the metrics tend to be comparable. We’re all by now familiar with the “eyeballs” and “stickiness” metrics of interactive sites. Since co-creation requires the development of scale and efficiency (this is one of the main differences between collaboration and co-creation), co-creative firms need to develop scale and efficiency measures for the platform itself, e.g., how many interactions take place, how quickly they unfold, how many of them complete successfully, etc…
The most important data on a co-creation platform, however, is the constant qualitative bending of the performance model. Users continuously stretch the limits of the platform, imagining new interactions that would be of value to them. It typically starts by offering unfiltered feedback in exchanges between users (the site becomes the market research department). In some cases, on electronic platforms, this evolution occurs through downright hacking. The development of the Lego Mindstorms operating system (Lego robots) was one such case of hacking (ultimately grandfathered by Lego designers), and the insertion of Google maps into the Nike + runners community site is another case where two technologies were “mashed up” by users, rather than orchestrated by Nike web designers.
There are however a few fundamental differences in the data required by a co-creation strategy vs. a classic strategy. The first difference is that in co-creation, the data is not resident in some data base of trends or financial analysis of competitors which any analyst can access. This data is by definition much richer than any “study data” because it is only accessible to the company that curates the platform. It is original data generated by real people using the platform who find it valuable to share their data. This data is often quite intimate. Nike users will share weight and running patterns. Patients on the Patients like Me site share personal health and effectiveness of treatment data on profoundly debilitating diseases such as cancer or depression, because they believe insights and potentially new treatments will emerge from the sharing of this data. (Of course, no doctor or health insurance company would have the right to share this data, but patients willingly contribute it because they have a vested interest in it). People on mint.com find such value on the platform that they give the platform access to a large amount of personal financial information, because mint.com offers them insights they cannot get anywhere else.
Even more importantly, the data on co-creation platforms is alive rather than static. It is associated with individuals who are interested in continuously interacting with the company on new topics of mutual interest. At any one time, the company or the individuals can initiate the development a new branch in the tree of co-creation. They can generate a new algorithm on how to look at the data itself, e.g., suggest a new way of looking at how to train for Nike, a new treatment for Patients Like Me, or a new financial strategy insight for mint.com. Not only is the data co-created, but so is the analysis and the insights that come from this data.
The possibilities become endless when data and the development of associated insights are driven by the self-interest of passionate people combining with the professionalism of a co-creative staff at a company. The future of analytics in business is not company-created algorithms accumulated through CRM or other third-party data bases. It lies in the co-creation of both data and insights by willing individuals interested in working with companies of their choice to generate new experiences for themselves.