As I watch my two kids shop, I feel envious of how they make their decisions today compared with what I did 35 years ago. The increase in electronic collaboration has created a new breed of sophisticated consumers. These consumers are far more analytical, far savvier at using statistics, and far more dexterous at seeking collaboration from near-strangers to rapidly collect and collate opinions from others.

I have been leisure traveling extensively for over three decades, and it took me months each time to plan the vacation. Thirty years ago, my usual process of searching for the best places to visit and restaurants at which to eat was based on American Automobile Association (AAA) brochures. As the Internet gave me the flexibility to search destinations online, I mechanized my searches, without significantly changing the process. I used to scour the Internet looking for brochures, read their descriptions, and guess which restaurants would be closest to our liking. In these years, as my kids grew, they laughed at my archaic ways of shopping. In a spirit of their showing me how it works, I finally gave up control over our travel to Italy for a family vacation in 2007. My kids were living at opposite ends of the United States, but were able to collaborate with each other, their friends, and others to organize us, using a new set of tools, without paying a single cent to AAA for membership or Barnes & Noble for expensive travel books. They went through sites like Yelp and TripAdvisor and sought help from their friends (online, obviously) to find rare places that were both inexpensive and served delicious food. We were never lost for directions, always found ethnic foods I could never have located through my old ways, and paid a fraction of what I would have paid for food. We ate almost like locals in Rome and Florence, and by throwing a couple of Italian phrases into the mix, we almost acted like ones, too. Yet while I admired my kids’ way of working, it is relatively harder for me to adopt their style of travel planning. I was organizing a trip to Rajasthan, India, in December 2013, and my gut reactions were still the same. My daughter, who had never been to Rajasthan, had already figured out the best restaurants in Jaipur, the mode of transportation to the Taj Mahal, and the best tourist spots. The comparisons were provided not by marketers, but by other travelers like her.

The information collected by travelers was organized at a fraction of the cost of collating, organizing, and publishing travel knowledge in proprietary ways. It was the result of a gigantic collaboration. Some of the data was very biased and very incorrect. However the data tsunami took care of the biases. My daughter explained to me that the true value of high scores was only valid when there was large number of recommendations. “If a restaurant has four or five stars with less than ten reviewers, it may be fabricated reviews from family and friends" she said. “You should only go for recommendations where the number of reviews is large. And, do read the reviews, as there is a lot more data hidden in the unstructured text" she added. While this was common sense, it was interesting to see how social media sites had put that information in the hands of consumers on their devices. It was a lot of unstructured data, but it gave us much more than simple quantitative scores. Not everyone was looking for the same product, but by reading the reviews, we could get so much more information.

How did so many people collaborate so effortlessly in Yelp? How is the data organized so easily and so well? How do we have a taxonomy in which I can type any small ethnic community food type and find half a dozen restaurants in Southern California? These sites are mostly crowd- sourced, with strong governance and a well-maintained taxonomy that is changed based on how the users of the community search. Yelp has a way of rewarding its best reviewers. They are called elite reviewers:

The Yelp Elite Squad is our way of recognizing and rewarding yelpers who are active evangelists and role models, both on and off the site. Elite-worthiness is based on a number of things, including well-written reviews, great tips on mobile, a fleshed-out personal profile, an active voting and complimenting record, and playing nice with others. Members of the Elite Squad are designated by a shiny Elite badge on their account profile.10

Today’s customers can shop around the globe, find out more than ever before about the organizations they are dealing with, and share their views with hundreds of thousands, if not millions, of fellow customers. Their expectations—be they consumers, citizens, or business customers—are soaring. And they can make or break brands overnight!

Society has always played a major role in our evaluation process. However, the Internet and social networking have radically altered our access to information. I may choose to “like” a product on Facebook, and my network now has instant access to this action. If I consider a restaurant worth the money, Yelp can help me broadcast that fact worldwide. If I hate the new cell phone service from a telco, I can blog to complain about it to everyone.

We have to be careful in using these powerful tools on big data, however, as they are likely to reveal private facts. The Wall Street Journal reported the case of a student who joined a gay community. The community, in turn, invited her to join a Facebook page where the community was posting its events and sharing friends. 1 1 Unfortunately, this student had not disclosed her gay status to her parents. To her shock, an invitation went from her community to her entire friends circle, including her parents, to join the community. As Facebook fine-tunes its privacy preferences and policies for posting and broadcasting information, such stories are grim reminders of the complexity in our social structure and how social media must weave its designs to meet these requirements.

On a positive note, Barack Obama’s presidential campaign is a great example of how a political campaign started at the grass-roots level, connected with its constituencies, respected them for their individual differences, and focused a multichannel campaign using a gigantic voter data model. The campaign built a detailed data model and kept track of individual political views and preferences, encouraging participation at the grass-roots level and constantly using these activities to update the data model. The campaign then aggregated the data from the individual level to formulate clusters and directed the rest of the channels, including the venue for President Bill Clinton’s speeches and television spots, to align with the pockets of voters requiring the most attention. The email campaign continued even after the election. As a participant, I receive well-written emails from the president himself or the First Lady, sharing with me his activities that pertain to my wish list of issues and encouraging me to participate in the political process by continuing to voice my opinion. The big data analytics for the campaign were choreographed by Dan Wagner, and have been extensively discussed by political and technical journals.

The significance of Wagner’s achievement went far beyond his ability to declare winners months before Election Day. His approach amounted to a decisive break with twentieth-century tools for tracking public opinion, which revolved around quarantining small samples that could be treated as representative of the whole. Wagner emerged from a cadre of analysts who thought of voters as individuals and worked to aggregate projections about their opinions and behavior until they revealed a composite picture of everyone.12

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