- Do the children of NBC Olympic executives watch the delayed broadcasts at 8 pm?
- Have executives at the soon-to-be bankrupt Best Buy ever visited an Apple store?
- What would it take for my main course at Not Your Average Joe’s restaurant to not arrive on the first bite of my appetizer?
- What would it take for Skype to stop moving the “end of call” red phone on my screen so that I can cleanly conclude conversations with my mother (who never hangs up before I do)?
- Why is the freezer compartment on my GE refrigerator designed to break my back and why is it separated into bins that do not match the size of any commercially available food package?
- How can I get my GE washing machine to stop beeping at me when I load clothes into it?
- Why hasn’t any major oil company come up with a comfortable tire inflater at their gas stations (I often go to Exxon Mobil)?
- Could Microsoft and Dell tell me when my Caps Lock is on?
- Why do small TVs automatically have a bad sound (mine is a Toshiba)?
- Why do I receive two or three credit card offers from Capitol One every week, but my Bank of America small business banker never calls me?
We think of weathermen and stockbrokers as the two “often in error, but never in doubt” professions. Let me nominate a third: economists.
If stimulus or austerity policies worked, we’d know by now. If Friedman and Keynes were right, our governments would long have adopted their policies, and our economies would be roaring like Formula 1 cars in the Monaco grand prix. The predictability of tax cuts or stimulus spending on economic growth has the reliability of Paul the Octopus forecasting soccer game outcomes: sometimes it works, and most of the time it doesn’t. Yet politicians everywhere hang on to these disproven theories as economic gospel.
What’s wrong with economics? To paraphrase Mitt Romney in one of his awkward statements, economics is people. Instead, we think economics is policies. From government to universities, we teach economics as a massively aggregated database from which we extract insights, then policies, at the level of a state or a country. This leads to lame assertions about interest rates, monetary mass, jobs, trade deficit, and vague concepts of rational expectations reputedly anticipating economic behaviors. If we understood the true causes and effects in the economic system, our Presidents would not be sweating the job numbers every month: they would tell us beforehand what to expect.
Do you wake up in the morning thinking about interest rates, inflation and trade deficit? Do you actually decide to buy a car, a house, or go grocery shopping on the basis of interest rate and inflation? Do you look at your Turbotax statement to decide whether the rise in the marginal tax rate just passed by Congress will authorize you to go to Wholefoods and buy the fresh organic tomatoes that day, instead of going to the regular grocery store where the tomatoes are cheaper? Of course not. Yet this is the micro-level at which the economy works. Unless we can begin to comprehend decisions at this individual level, we have nothing of value.
If I were an economist, I’d start by diving deep into understanding how five or ten of my neighbors experience the economy. I’d try to build a model of their income statements and their balance sheet, and figure out how they decide to patronize five or ten local businesses (say, restaurants, grocery store, day care, etc.), or why they decide to save and for what. I’d try to understand the economics of the five or ten local businesses my neighbors buy from, why these small businesses decide to expand and hire, or why they scale or shut down. If there were one or two big businesses in my local area (corporate headquarters, big hospitals, etc.), I’d try to understand how the success of those large businesses contributes to the local economy through local taxes and jobs. I’d then try to model how the local township or municipality benefits from all this, and what impact local people and businesses have on the finances of my town (school, public funds, etc.). If we could just model this microcosm of economic interactions, we’d have data on a real, living ecosystem of actual people and entities and begin to understand how a local economy is co-created through their interactions.
Would this be representative of the economy as a whole? Of course, not. There would be massive biases linked to local industrial fabric and wealth levels. To roll up this data into a state, national, or global economy, one would have to empower people to build their own model at the local level. Providing the structure and platform that allows this local modeling would be a great role for government, instead of pretending that it owns the economy or creates jobs. The role of government in economic policy should not be to build top-down expert models of the economy as a whole, but to empower local folks to build models of their local market and learn from their interactions.
Even more importantly, if we began to understand causes and effects at the local economy level, the “economic agents” involved would be able to do something about the economy, rather than passively describe it. Individuals could change their relationship to local businesses, for example, by forming communities around them. Local businesses could mobilize those communities by setting up platforms that better connect them to their local customers (for example, I’d love to rally a few of my local friends to help a local hotel improve a few things in their menu and rooms, and we’d collectively bring them our out-of-town business). If local officials were to facilitate this dialogue, this would do more to create jobs and get them reelected than repeating hackneyed Republican or Democratic theories of austerity or stimulus.
Unfortunately, the scarcest commodity in economics is humility. Witness for example the recent Business Week article on the discussion between Paul Krugman and the President of Estonia and on the value of austerity in running a country. I will not venture an opinion about who’s right or wrong in this debate, but getting rid of the condescension conveyed in this dialogue seems to me to be job 1.
I, for one, would like to understand the economics of my village.
Yes, there is such a thing as business methodology fashion. Big data and social enterprise are hot. They’re the Kate Perry and Lady Gaga of business concepts, drawing huge crowds to seminars everywhere. Innovation is not far behind, but like Justin Bieber, it’s hot, rising, and in need of growing up. Customer experience is still high on the charts, but its Eminem alter ego, the Net Promoter Score band, is on the way down. Process design and quality, those Led Zeppelin and Pink Floyd of the 70s, now belong on NPR fund-raisers for middle-aged corporate types. Organization and strategy have long gone punk and disco and only get rotation on oldies but goldies stations featuring specialty acts by aging professors. Leadership, like Jimmy Buffet, is still drawing huge crowds of parrot heads to executive education seminars at Harvard Business School. Operations is like hip-hop, more or less always in fashion, changing form all the time, sometimes Ice T gangsta rap, sometimes Black Eyed Peas mainstream.
So where does this leave co-creation, you might wonder? I think we’re like Beyoncé. A little r’n’b, a little hip-hop, a little pop. We’re a cross-over genre. Co-creation, through its communities aspect, is often listed by Billboard as HR and transformation (employee communities), sometimes as product development (customer communities). Because engagement platforms require technology, the charts have us as an IT act. When we rock on experience, we become Marketing artists. When we sing about interactions as the new process, we end up in Quality, 6 Sigma and Lean concerts. And when we show the cost effectiveness of co-creation, we end up on the financial charts.
We sometimes confuse our public, but heck, if it works for Beyoncé…
The Massachusetts Institute of Technology just announced the launch of a new Big Data initiative. For those of you who have lived under an analytic rock for the last few years, Big Data is the name given to the movement that involves mining large volumes of consumer and social data in an attempt to identify behavioral patterns usable by corporations to market more stuff to you. Not surprisingly, the Big Data movement is largely financed by technology firms who see an opportunity to sell expensive equipment to CIOs and general managers, in the hope that algorithmic inspiration will magically arise from the fumes of the data landfill. If you’re old enough to remember the data warehouse debacle of the 70s and 80s, or the unfulfilled promised of Customer Relationship Management software (CRM), welcome back to the future.
I actually like the Big Data movement, but think it is largely misguided in its arrogant assumption that a few analytic experts can generate insights from large amounts of data through the sheer power of their brilliance, while all evidence points to the fact that these expert-driven approaches repeatedly fail. If one more person quotes Moneyball to me as evidence of the virtue of business intelligence, I think I will puke (or better yet, direct them to the latest American League standings where the Boston Red Sox occupy the last place, thanks to the aforementioned Moneyball approach).
To be clear, I very much believe in the power of analytics, as long as we understand who generates insights from data. And in most cases, it ain’t the experts, but the users of the data.
Insights come from the motivation of self-interested individuals confronted with the reality of their own data, measured against the backdrop of an entire population’s data, and hoping to discover new patterns of actions for themselves. Data itself is inert, and rarely produces action, except for a few left-brained people who teach at MIT. Most of us need to convert left-brained data into a right-brained hypothesis that we can only appropriate if we have participated in its development in some fashion. Few of us believe in universal truths to the point where we can put them into action (if this were the case, we would all be eating the right food all the time and exercising several times a day). This conversion from left-brained understanding to right-brained-driven action requires the co-creation of a personal hypothesis based on some objective evidence from the known data, and a unique act of creativity about what will work for us. This co-created hypothesis will lead to a willingness to experiment on a small scale. The experimentation on a small-scale will then lead to a more ambitious exploration of new causes and effects in the hope of figuring out new things selfishly helpful to us. Over time, the sum of all those self-generated experiments will generate population-wide hypotheses which can then be tested analytically, using big data sets (and perhaps a handful of experts from MIT).
For example, let us say I want to reduce the glucose level in my blood because I have been diagnosed as pre-diabetic. Of course, I will be told from day one by my doctor that I should reduce the intake of certain foods and exercise more (medical research has proven that broccoli is generally better than a hot fudge sundae to reduce cholesterol, so I might as well put that known fact to good use, but as already said, this will only carry me so far if I love hot fudge sundaes). What will motivate me is finding the ultimate combination of food and exercise that works for me. To get there, I will need to formulate hypotheses that apply uniquely to me (for example, by keeping hot fudge sundaes on my diet, perhaps a bit less frequently), and letting me create my own set of relevant data and measuring consequences of my personal food and exercise choices. In other words, I will want to generate my own set of data and devise my own algorithm as to what works for me.
The question I would ask is the following: given what is already known about cholesterol, from a clinical standpoint, is society more likely to make progress on the cholesterol issue by:
a. Looking for a killer predictive algorithm that predicts who will get diabetic from the pre-diabetic stage, using a Big Data approach (classic medical research and development approach)?
b. Distributing a user-friendly test kit and data log to the pre-diabetic population that allows them to test in real time their glucose level, encourages them to figure out what specific food raises their glucose level in their own body after each meal, and measures the impact of exercise on their individual glucose level after each work out (the co-created approach to research and development)?
I’ll leave it to the National Institutes of Health to spend my tax dollars on scenario a, and I’ll personally put my money on scenario b. Why? Because you will get a lot more engagement from linking personal data to individual courses of action for each patient. If we can get millions of pre-diabetic patients to self-create their own clinical experimentation – imperfect as this “clinical trial” would be from a statistical standpoint– we will learn a lot more than by having three scientists set up a double-blind, exquisitely narrow hypothesis and spend the next ten years collecting that data, complete with double-blind set-up and T statistics. Beyond the obvious advantage of collecting data on a large scale, the moment where patients start tracking their own data, they will start experimenting with new approaches that interests them and them only. This will create a wide field of distributed experimentation that can then be aggregated into wider insights, making the personally co-created data and insights into usable research data for the whole population.
The point here is that patients are contributing a lot more than data. They are also contributing insights, by formulating hypotheses about what could work for them, and by setting up personal experiments to test those hypotheses. Their motivation for doing so is not analytic (they’re not looking for a Nobel Prize of Medicine), but self-serving (they want to get healthy). Right-brained motivation for self-improvement is the currency of true research.
Of course, a lot of the hypotheses formulated by individuals will be dead ends, and they will naturally weed themselves out. Co-created research is a messy game where a few insights hide in a forest of marginal or even useless ideas. Experts could in many cases have elegantly dismissed these naïve or erroneous out of hand through their a priori knowledge, but it does not matter in the end because the sheer volume engendered by self-interested people will always trump the expertise held by a few . There is a legitimate role for experts, but it involves coaching patients into investigating some areas rather than ours and structuring the aggregation of both data and algorithms at the larger population level, not claiming a monopoly in generating those insights as the current Big Data approach suggests.
MIT, let the Big Data bird out of its expert cage.
Living outside France allows me to return to my country of birth with a foreigner’s sensitivity to quaint language developments there. The French proclivity for multisyllabic concepts is unmatched. If you can think of a concept, there’s a French word for it.
One of my favorites is primo-accédant, which refers to people who buy real estate property for the first time (not necessarily primo real estate, I might add). How many other languages have coined a term for such a person? French people also have a word for people who have multiple banking relationships, yielding the pentasyllabic multi-bancarisé. Multi-bancarisé is opposed to mono-bancarisé when they have only one bank. As for sex, however, it turns out the French prefer multiple relationships, yielding a sizable segment of primo-accédants multibancarisés for whose attention bankers fight. I am told from admittedly less than reliable sources that the new French President François Hollande was heard warming up for his victory speech at La Bastille by repeating primo-accédant multibancarisé a hundred times at fast pace .
“Why would one do simple when one can do complicated”, was the wisdom offered by an old French TV cartoon called Les Shadoks. This exemplifies French people’s approach to language. A problem used to be un problème, but has now migrated to une problématique, with sounds like a much larger headache (moving from a masculine to a feminine also appropriately connotes of greater complexity). It was appropriately used by the hotel maintenance staff at my Paris hotel this week to describe a leaky toilet in my room (la problématique de la chasse d’eau). Things also used to last (elles durent), but they now hyper-last (elles perdurent), which, best I can see, is the new watered-down definition of eternity given the increased secularism of France. Any kind of work-related injury used to be commonly referred to as un accident du travail (a work accident), but employers are now invited to prevent troubles musculo-squelettiques. As a modest French employer, I have spent many sleepless nights imagining employee body parts flying all over the hexagone and being held responsible for such human implosion.
The political language of the French presidential campaign has also co-created its share of new words, most of them quite divisive. Islamophobie, while elegantly polysyllabic, refers to the sad reality of immigration-related tension and the racist feelings it engenders. I even heard an interview on television where a young woman referred to herself as an anti-islamophobe (that’s seven syllables if you’re counting), which, she explained, is a lot stronger than being an islamophile. The presidential candidates have spent a lot of time trying to diaboliser their opponents (paint the other guy into a devil), while taxing the other one of angélisme, the art of attributing angel-like feelings to people who should clearly be diabolisés instead. There is a trend toward the désacralisation of everything (literally take the sacramental portion out things), most notably marriage, paying taxes and les institutions.
The French are also world-class when it comes to metaphoric expressions. When they mean to convey “let’s not worry about that”, hip French people like to say “on ne va pas se mettre la rate au court-bouillon”, which literally translated means something like “let us not make spleen soup out of it”. In case you wondered about ingredients in cuisine nouvelle. Another popular expression to convey “it’s not even close” is “y’a pas photo”, literally “there is no need for a photo-finish on this one”. This one appears to have originated with horse racing, and the celebrated tiercé et quarté du dimanche. French people used to eat horses in boucheries chevalines, but the trend is clearly toward less eating and more betting. Some uncharitable commentators have referred to the new French President François Hollande as “il n’a pas fait l’école du rire” (literally, “he did not graduate from laughing school”), because of his serious demeanor. I can’t wait for the slap fest at the next sommet franco-allemand with Angela Merkel. given her own barrel of laugh approach to things.
While I increasingly need an interpreter to decipher those new expressions, a few concepts have remained reassuringly the same (ils ont perduré, as it were). The dame pipi, still available at most French railroad stations in Paris, is still à son poste, collecting coins before letting you faire vos besoins (attend to your needs, or do your business). For good measure, the French have added some automation equipment (the French are big on infrastructure, particularly when it comes to bathrooms and fast trains), such that you now not only have to produce at least 50 centimes d’euro (or risk the wrath of madame pipi in a surprisingly polyglot tirade), but also have to jump turnstiles with suitcases, encouraged by a crowd of lookers-on inspired by the popular TV game show Fort Boyard. Engineers at the French railroad organization SNCF were clearly never told that train travelers use suitcases, in full illustration of the motto of the French elite engineering school Polytechnique which stipulates: we know some things works in practice, but do they work in theory?
Of course my timing is particularly bad here. The French are at a serious juncture in their political history, having just elected François Hollande to become Président de la République Française, and here I am, participating already in his désacralisation and his diabolisation. Cher Monsieur le Président, I present you with my best wishes in addressing la problématique of France, such that my former country can perdurer, with a special thought for the primo-accédants, whether of the monobancarisé or the multibancarisé variety, and whether they are prone to troubles musculo-squelettiques or not. Vive la République. Vive la France.
From Walter Isaacson’s biography of Steve Jobs, I have extracted the following ten principles of leadership.
- Start drugs early
- Screw the friends that got you started
- Tell people they’re assholes
- Steal the ideas of the two or three people who are not
- Occupy handicapped people’s parking spaces
- Ignore your father, abuse your girlfriends, abandon your daughter
- Cartelize industries
- Post-date corporate options
- Despise philanthropy
Will anybody ever want to teach leadership after Steve Jobs?
(HBR article by Isaacson)
Periodically, I ask myself: “who are the most effective change agents when it comes to implementing co-creation inside a corporation?” Here is my list, in descending order of effectiveness:
1. Chief Financial Officer (CFO)
- Good news: The CFO’s source of power comes from controlling financial resources, often including IT money required for the development of co-creation platforms. They are often frustrated line managers who see co-creation as a means to gain influence over the operational side of the business.
- Bad news: their analytical bias can overpower the human side of co-creation.
- Good first step: issue cost reduction challenge to one of the businesses; suggest co-creation may be the way to reach that goal (get external people to do work for free that was previously done inside).
2. Chief Information Officer (CIO)
- Good news: CIOs get to co-creation through the funding of engagement platforms. The role of CIO in co-creation is legitimized by the app store phenomenon (co-creation with third-party developers).
- Bad news: CIOs often struggle with developing the human community part of co-creation (they can be too tool-focused).
- Good first step: find a few APIs and open up some aspect of your customer-facing sites to third-party developers. Start connecting customers and developers.
3. Chief Purchasing Officer (CPO), Directors of Supply Chain
- Good news: There is a new breath of fresh air with procurement departments; they increasingly recognize that they should be developing supplier networks rather than consolidating them. Supply chain people are often pushed to co-creation through the need to create transparency in their emerging country plants (often due to labor and sustainability issues).
- Bad news: Supply chain people can get confused on the difference between collaborative supply chain tools that have been around for several years, and the actual development of co-creative supply chain communities that allows the constant reinvention of those supply chains.
- Good first step: pick a particularly risky part of your supply chain (e.g., Chinese plant with labor issues), and demonstrate that you can remove some operational and reputational risk through co-creation.
4. Research and Development (R&D) Managers, Heads of Product Development
- Good news: Many product development people know that co-creation is coming to product development and product design (also often referred to as open innovation, or crowd-sourcing).
- Bad news: They often do not yet know how to involve their own people in co-creation and avoid the NIH syndrome. They often jump too fast to third-party platforms to generate product ideas, but fail to engage their own people in the dialogue.
- Good first step: start inside. Assemble your R&D people and see where they would welcome the engagement of external people. Only when you have their views will it become meaningful to engage external contributors.
5. Chief Experience Officer
- Good news: more and more companies have experience officers. Experience officers are natural sponsors for co-creation.
- Bad news: many of them focus on measuring “as is” experience rather than trying to change it.
- Good first step: pick a narrow segment (a single customer in B2B), engage the mini ecosystem involved in serving this narrow segment/single customer and see what co-creation can bring.
6. Chief Marketing Officer (CMO), Head of Market Research
- Good news: CMO and market research people understand experience.
- Bad news: they think of themselves as experience experts, and therefore see no reason to co-create any of that experience with anyone (since they know better).
- Good first step: open up one of the brand management processes to customers and employees, e.g., advertising, and see what you get.
7. Chief Sustainability Officer (CSO)
- Good news: sustainability is one of the best fields of application for co-creation because of the multi-stakeholder nature of the problem.
- Bad news: CSOs don’t typically have access to senior people and may not know how to engage them.
- Good first step: team up with the sales force to embed sustainability in the sales message.
8. Performance Management, Quality, Reengineering, 6 Sigma, Lean, Transformation Officers.
- Good news: Performance management people naturally gravitate toward co-creation as “the new tool kit.”
- Bad news: The concept of process can be so engrained that moving to platforms and self-configured interactions can represent a mental challenge. Many struggle with the notion that the transformation path can/should itself be co-created, rather than established by experts.
- Good first step: pick a customer-facing process, e.g., sales or customer service, and show how moving from process thinking to co-creation changes the outcome.
9. Strategy Officers
- Good news: A few strategy officers understand the power of human experience in generating insights.
- Bad news: most prefer an information-gathering and analytical approach.
- Good first step: pick a self-contained strategy issue, and ask customer-facing people and a few customers how they would frame and solve the issue. Compare to the answer an analytical approach would have provided.
10. Human Resources Officers, Diversity Head
- Good news: Of course, senior HR development people should be major players in co-creation.
- Bad news: In practice, they rarely have access to the proverbial strategic table.
- Good first step: co-create HR processes (e.g., training, hiring, career development) rather than tackling line processes.
It has been widely reported in the last few days that some players on the New Orleans Saints football team developed a home-grown bounty system whereby players would reward each other with personal money for inflicting injuries onto opposing players. While the National Football League is investigating the New Orleans Saints specifically, there are indications that such a system might be in existence across the league, along a continuum from the clearly legal (players rewarding a punt return) to the apparently illegal variety (the NFL seems to have rules that prohibit intentionally putting a quarterback on a stretcher).
The New Orleans Saints have developed a perfect system of co-creation we should write up in Harvard Business Review, not decry in the New York Times. The system developed by the players has all five ingredients of co-creation:
- A community. The players who decided they were going to build a kitty to reward injury-causing hits on opposing players set themselves up as a community. Had the NFL not intervened in ill-advised fashion, the player community might have expanded into allowing investment from fans into the bounty scheme. A “Knock Tom Brady cold” Super PAC could not have been far behind, supported by Libyan or Syrian capital.
- An engagement platform. The platform was an organized spreadsheet where players kept tabs on bets and rewards. The spreadsheet was further institutionalized when an assistant coach started keeping score on behalf of the players. The next expansion would have included an idea generation web site open to the public (myinjuryideas.com), with an injury pricing site and rotisserie league to follow.
- Continuously expanding interactions. The platform was originally developed as an incentive system to reward legal plays (e.g., causing a fumble), but started sprouting injury-causing moves over time. The community and platform in place could have been further expanded into player gambling on football games, sponsoring dog fights, or financing armed robbery by young deserving football players.
- New win-win experiences for all parties. We’re told the bounties helped young players round off their modest paycheck, allowing them “to buy shoes” with the proceeds. I understand Zappos and Nike were eager to become involved in the Saints co-creative ecosystem. Elder players enjoyed the developmental experience of providing nurturing advice to their younger colleagues, supported by the team’s Human Resources function. The assistant coach was clearly on the short list for Coaching Innovation of the Year. And the New Orleans Saints fans got a winning football team after years of futility, allowing the entire city to regain its pride after Katrina (well, sort of).
- New value for the club owner. The bounty system produced a highly motivated work force that fully dedicated itself to the task at hand, ultimately winning the Super Bowl. Absenteeism was at an all-time low. Career progression was rapid. The bounty system had no cost to the owner since everything was financed by the players. The system did have a tremendous revenue impact in terms of gate attendance and media revenue. What else could one wish for as an owner?
The bounty system was such a perfect example of co-creation and produced an ever-expanding win for all parties (except for a few injured parties along the way, but doesn’t there have to be some Schumpeterian creative destruction?). The Saints bounty system could have become the new Facebook, the new Google or the new Groupon. Will regulators ever learn?
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.
She’s young. She’s passionate. She’s an environmentalist. Her ecological ideas feel a bit out of place in the conservative bank where she works, but she doesn’t care. That’s her passion. Every Thursday night, she goes to the local movie theater to participate in the meetings of the sustainability group she’s part of. She wants to change the world, just from where she is.
She’s also a triathlete. She can swim, run and bike faster than anyone around her. She shrugs her shoulders when the conversation moves to the local soccer team and the thousands of people they attract every week. “Have you ever attended a track and field meeting she asks us?” None of us has. She describes the excitement of having so many things going on in the stadium at any one time: watch a race, then focus on a pole-vaulter clearing the bar or watch a javelin fly across the field. I feel like signing up.
I ‘m having lunch with a group of bank advisors, between a morning and an afternoon workshop session. I’m sitting next to this young bank advisor, and I feel young again. She operates in a small town that does not quite fit the community-building scheme we have devised, so we make lame excuses about not having enough resources to support her in her small town. She just ignores our objections. We rapidly understand that any resistance is futile. She wants to get involved in this co-creation project, and we will help her.
So we agree to run a workshop with the customer she’s identified. I am long gone by the time the workshop happens, but I get an account a couple of weeks later. Between her client and herself, they have mapped out what the community should do, how the bank should get involved and how everybody would benefit. The community is building itself. One of my colleagues calls me at the end of her day in Europe to give me the news. No need for deep analytics, or motivational speech. All we have to do is watch her run. It’s hard to know who’s more excited — the customer, the young bank advisor, the consultants or the banks’ management.
Why didn’t it happen before? She was there all along. All we did was give her a platform. Her passion is now channeled through her job. She’s become a business activist. I know she’ll run far and drag thousands of people with her. I will be rooting from afar.