In previous posts about big data and targeted marketing, I have repeatedly pointed out that the elephant in the room is not Hadoop but issues involving privacy and access to consumer information. Results from a recent Consumer Insights Survey conducted by Ovum “reveals that 68% of the Internet population across 11 countries would select a ‘do-not-track’ (DNT) feature if it was easily available, suggesting that a data black hole could soon open up under the Internet economy.” [“Ovum predicts turbulence for the Internet economy, as more than two-thirds of consumers say ‘no’ to Internet tracking,” Ovum, February 2013] As you might imagine, these findings have created a flurry of articles on the subject. As the article explains, “This hardening of consumer attitudes, coupled with tightening regulation, could diminish personal data supply lines and have a considerable impact on targeted advertising, CRM, big data analytics, and other digital industries. In many ways, the very companies that created the Internet economy and depend on it to survive are the culprits that poisoned the well from which they drink (for more on that subject see Part 1 of this series). Mark Little, principal analyst at Ovum, stated, “Unfortunately, in the gold rush that is big data, taking the supply of ‘little data’ – personal data – for granted seems to be an accident waiting to happen. However, consumers are being empowered with new tools and services to monitor, control, and secure their personal data as never before, and it seems they increasingly have the motivation to use them.”
This is exactly what Doc Searls from Harvard University’s Project VRM predicted would happen. To learn more about where he sees the Vendor Relationship Management movement heading, read my post entitled Vendor Relationship Management: Making the Customer King. A litany of scandals involving privacy abuses has only accelerated this movement. Ovum reports:
“Recent data privacy scandals such as WhatsApp’s use of address books, and the continuing issues over privacy and data use policies on Facebook and Google websites have fueled consumers’ concerns over the protection of their personal data. Ovum’s survey found that only 14% of respondents believe that Internet companies are honest about their use of consumers’ personal data, suggesting it will be a challenge for online companies to change consumers’ perceptions. Ovum believes that Internet companies should introduce new privacy tools and messaging campaigns designed to convince consumers that they can be trusted. Improving the transparency of data collection and use will help to build trust, a commodity that will increasingly become a sustainable competitive advantage.”
Hopefully it’s not too late to build that trust; but, trust is difficult to regain once it has been lost. “Internet companies need a new set of messages to change consumers’ attitudes,” Little states. “These messages must be based on positive direct relationships, engagement with consumers, and the provision of genuine and trustworthy privacy controls. Most importantly, data controllers need a better feel for the approaching disruption to their supply lines, and must invest in tools that help them understand the profile of today’s negatively-minded users – tomorrow’s invisible consumers.” The prospect of “invisible consumers” should send shudders down the spine of anyone associated with targeted marketing. You can’t target what you can’t see.
Fortunately, people like MIT’s Professor Alex “Sandy” Pentland believe that companies can establish mutually beneficial relationships with consumers so that they willingly provide access to important marketing data. For more on his views, see my post entitled Big Data Dilemmas. If companies don’t act quickly, however, just the opposite could happen. Mike Wheatley writes, “Companies are increasingly taking their data for granted, treating it as some kind of free commodity, but this is a foolish assumption to make. Thanks to the growing availability and awareness of free tools, consumers now have the power to block companies from gathering data about them. At present, very few consumers use them, but if ‘anti-tracking’ tools were to catch on then the resulting ‘Big Data Black Hole’ would have a massive impact on big data analytics, targeted advertising, CRM and other digital industries.” [“Big Data Black Hole Looms As Consumers Say ‘No’ To Tracking,” SilconANGLE, 6 February 2013] He agrees with Little that “greater openness is the only way to win back consumer’s trust, and those that do so first will gain a serious competitive advantage over those that are slower to act.”
Grant Gross also believes that “Internet advertising networks and other companies that depend on the collection of personal data online should prepare for a ‘rebalancing’ of the relationship between themselves and web users.” [“Big Data privacy concerns will impact on internet economy,” CIO, 7 February 2013] Jeffrey Chester, executive director of the Center for Digital Democracy, told Gross, “‘Big Data is both a boon and a curse for users. Tens of thousands of data sources on individuals can be compiled in milliseconds.’ The profiles allow marketers, politicians and businesses to predict consumers’ futures, he said, ‘whether we will be a big and low-wage lifetime earner, how we may respond to medical concerns and whom we can be persuaded to vote for.'” Mark Little told Gross that he believed there are ways that companies can change from the current “data fracking” model to one that is more collaborative with consumers. Gross writes:
“Little … sees potential for a new business model in which consumers create personal data vaults that they control, giving consumers a choice about which companies they share their personal information with. A company called Personal is one company that has begun offering personal data vaults, he said. A move toward more consumer control of personal data won’t be all bad for internet companies, however, Little said. Personal data vaults will contain more accurate and forward-looking information than the current data collection methods can gather, he said. The change in relationship between consumer and data collectors will change slowly, and internet businesses shouldn’t change their da
ta collection practices immediately, Little said. Internet companies should ‘keep on riding the margins of regulation and consumer acceptance in order to maximize your data set, because that is just good business,’ he said. ‘But prepare for changes where consumers start to want more of a relationship with their own data and the people who are collecting it.'”
Saikat Sengupta believes that the big data sector may be in turmoil for the next few years while it gets things sorted out, but believes big data analytics and targeted marketing will eventually prevail. [“Do-not-track Tools Might Take A Toll On Digital Marketing Agencies,” WAT Blog, 7 February 2013] He concludes:
“From PPC remarketing to new age video advertisement, user data holds an important role in digital marketing. If Do Not Track Technology becomes stronger and stricter laws are introduced to honor consumers’ choices, then there will be a huge gap to fill up. After Google’s algorithm changes, digital advertising is struggling a lot to figure out the new strategies that will work. If DNT becomes more effective there will be another turmoil – but, as always, the industry will definitely figure out new ways to survive.”
I agree with Mark Little and Doc Searls that a tectonic shift is underway that will place more control over personal data in the hands of consumers. That makes it imperative for companies that want to benefit from big data analytics to figure out a way to strengthen their relationships with consumers and offer them some value added for access to their personal data. If the personal data vault concept catches on, it would provide a framework for a more collaborative quid pro quo relationship. The only caveat to such a framework is that the data in such vaults will only be valuable if millions of individuals opt to create one and deposit the right kind of personal information. The analytic value derived from data depends in large measure on the size of the sample. For most use cases, data sets need to be big.
