Innovation@50+ is a one day pitch competition for emerging startups in the healthy living space with a focus on caregiving. At the pitch competition, 10 finalist companies will present their business focus on stage in a rapid 3 minute presentation to a panel of industry expert judges, most of whom are venture capitalists and angel investors focused on the aging health tech space.Should be interesting. More "VC" stuff. They've invited me to attend on a press pass. And, I'm an AARP member, Geezer-in-Training that I am, so it's of intrinsic interest.
Representing 38 million members, the power of AARP takes this pitch event up a notch. Only Innovation@50+ creates a dual-pitch event that also provides an audience of 100+ actual intended end user consumers who listen to the pitches and share feedback in real time, providing the companies absolutely invaluable market data on the spot.
This year’s event will be held at:
Plug and Play Tech Center
440 N. Wolfe Road
Sunnyvale, CA 94085
BTW, my current book.
Yikes. Stay tuned for the review on this one. Another great read, as were my last two, cited here, and here.
For now, a little taste of Rushkoff:
...Amanda Palmer is not some monopoly company, or even a superstar performer exploiting her fans; she’s one midlist singer trying to make a living in a winner-takes-all landscape intentionally designed to prevent her from forging real relationships or exchanging value with her listeners. Her mix of barter, money, and gift is actually much more compatible with the tangled, ambiguous nature of real human relationships and hearkens back to the best qualities of the preindustrial economy.You need to read this book as well.
Digital platforms from social media to crowdfunding allow us to reclaim some of these community dynamics and apply them to our own business pursuits. Those of us who have become aware of the way some corporations exploit or hide their tactics may have a knee-jerk reaction against people who appear, at least on the surface, to be doing the same thing. But the relationships that small-business people are forging with their constituencies online are direct, transparent, and peer-to-peer; they are explicit, fee-for-service, and social.
They are relationships between real people.
THE BIG DATA PLAY
The value exchange between users and social networks, or fans and giant media properties, is entirely less direct and most intentionally covert. Digital networks simulate the very same human social dynamics fueling the communities of artists like Palmer in order to generate goodwill and mass excitement for their corporate clients.
It’s a one-sided, highly controlled relationship in which, invariably, the platforms and companies with which we engage learn more about us than we ever learn about them. Social marketing creates the illusion of a natural, nonmarketed groundswell of interest and, more importantly, provides marketers with a map of social connections and influences. These social graphs, as they’re called in the industry, are the fundamental building blocks of big data companies’ analyses.
Big data is worth more than the sum of its parts. It is the technology for solving everything from terrorism to tuberculosis, as well as the purported payoff for otherwise unprofitable tech businesses, from smartphones to video games. Like pop stars, these health, entertainment, and content “plays” will make no money on their own— but the data they can glean from their users will be gold to marketers. So they hope.
Indeed, it seems as if every startup is a “big data play.” Yet when we take into account the fact that the revenue supporting big data apps must presumably come out of that same constant 5 percent of the GDP associated with marketing and advertising, it becomes clear that such a payout can’t possibly come to pass. In fact, our increasing dependence on big data solutions may actually limit the growth it’s supposed to be stoking.
Reducing people to manageable sets of numbers is nothing new to digital technology. It began long before digital spam, when the high cost of printing and mailing physical pieces of paper motivated marketers to limit their offerings to those homes that might actually be interested. They gathered publicly available data, such as tax records and mortgage information. They stored this information on physical notecards— one for each household— and then manually selected a range of cards to include in a mailing.
With the advent of computers, statisticians began categorizing people into increasingly sophisticated demographic and psychographic groups, giving rise to the first data-driven market research firms. With upwards of seventy different categories in which to put us, researchers at Acxiom, for example, could arm marketers with psychological profiles of their target audiences, helping them to match their pitches to the particular social aspirations of their customers. 32
But they soon realized that their data offered more possibilities than this: it could predict our future choices. Using more sophisticated computers and methodology, researchers began connecting seemingly unrelated data points and became capable of determining who among us was about to go to college, who was probably trying to get pregnant, and who was likely to have a particular health problem. More than merely knowing our likely receptiveness to a pitch, they became capable of calculating, with alarming accuracy, what we human beings were going to do next. They had no idea why such a prediction might be true, and didn’t really care. This was the beginning of what we now call big data.
What makes big data different from traditional market research is that it depends on correlations that make no outward human sense. That’s the truly creepy part. Privacy is the red herring. Most people are still concerned about surveillance on the actual, specific things they are doing. That’s understandable enough. So when both the NSA and corporations assure consumers that “no one is listening to your conversations” and “no one is reading your e-mail,” at least we know that our content is supposedly private. But content is the least of it. As anyone working with big data knows, the content of our phone calls and e-mails means nothing in comparison with the metadata around it. What time you make a phone call, its duration, the location from which you initiated it, the places you went while you talked, and so on, all mean much more to the computers attempting to understand who you are and what you are about to do next. Facebook can derive data from how long your cursor hovers over a particular part of a Web page. Think of how many more data points there are in that single act than there are in the price of your car or the subject of your phone call.
The more data points statisticians have about you, the more data points they have to compare with those of all the other people out there: hundreds of millions of people, each with tens of thousands of data points. Researchers don’t care what any particular data point says about you— only what it reveals when compared to the corresponding data point in everyone else’s profiles.
Combine this with the ability of the Web to keep track of individual users and you get a true one-to-one marketing solution. Instead of buying ads that every visitor to a Web site sees, advertisers can limit their ad spend to the browsers of their target consumers. It’s the same technology that lets marketers hit us with ads for products we may have recently browsed on e-commerce sites— only now, instead of using our browsing histories, they use our big data profiles.
The same sorts of data can be used to predict the probability of almost anything— from whether a voter is likely to change political parties to whether an adolescent is likely to change sexual orientation. It has nothing to do with what they say in their e-mails about politics or sex and everything to do with the seemingly innocuous data. Big data has been shown capable of predicting when a person is about to get the flu based on their changes in messaging frequency, spelling autocorrections, and movement as tracked by GPS. 33
For marketers looking for an edge, however, mere prediction isn’t enough, and this is where they tend to get in the most trouble. Big data is simply a set of probabilities. Usually, it’s hard for analysts to get more than about 80 percent certainty about a future human choice. So, for example, big data analysis may reveal that 80 percent of the people who share three particular data points are about to go on a diet. That’s a pretty good indication of where to direct their ads for diet products.
But what about the other 20 percent, who may have chosen to do something other than go on a diet? They get sent messages along with everyone else, aimed at convincing them that they need to think about their weight. Feeling fat today? If they weren’t already on the path to considering a diet, now they will be. And it’s not even human beings making the decisions about who to send which ads— it’s algorithms programmed to extract the most purchases out of consumers by exploiting their data sets. The algorithms use trial and error to see what works, iterating again and again until that 80 percent probability goes up to 90 percent. Fewer people find alternative paths as they are corralled toward the limited outcomes of their statistical profiles. Companies depending on big data must necessarily reduce the spontaneity of their customers, so that they are satisfied with what amounts to fewer available choices.
It’s a digitally complexified version of the one-size-fits-all values of industrialism.
On the surface, the increase in customers for a product looks like growth. But it’s a limited, zero-sum game, in which the reduction in new possibilities cuts both ways. Many of the companies I’ve visited have been cutting back on expensive, unpredictable research and development (R & D) and spending resources instead on big data analysis. Why ideate in an open-ended fashion, they argue, when they’ve already got the data on what consumers are going to want next quarter? It’s virtually risk free. What they don’t get is that using big data to develop new products is like looking in the rearview mirror to drive forward. All data is necessarily history. Big data doesn’t tell us what a person could do. It tells us what a person will likely do, based on the past actions of other people.
The big rub is that invention of genuinely new products, of game changers, never comes from refining our analysis of existing consumer trends but from stoking the human ingenuity of our innovators. Without an internal source of innovation, a company loses any competitive advantage over its peers. It is only as good as the data science firm it has hired— which may be the very same one that its competitors are using. In any event, everyone’s buying data from the same brokers and using essentially the same analytics techniques. The only long-term winners in this scheme are the big data firms themselves.
Paranoia just feeds the system. Becoming more suspicious of the data miners— as we do with each new leak about government spying or social media manipulation— only increases the value of data already being sold. The more restrictive we are with what we share, the more valuable it becomes and the bigger the market that can be made. We might just as easily go the other way— give away so much data that the data brokers have nothing left to sell. At least that would put them all in the same boat as the rest of us.
Rushkoff, Douglas (2016-03-01). Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity (pp. 39-44). Penguin Publishing Group. Kindle Edition.
More to come...