Three Implications for the Rise of E-Readers

For the first time ever, Amazon.com sold more electronic than printed books. In other news, Kindle e-readers are flying off the shelves and one article suggests Barnes and Noble’s saving grace will be their Nook reader.  What gives with this sudden transition to e-books and what are the implications of this e-reading trend?

An Economist article titled “Great Digital Expectations” highlights the rapid rise of e-books. And “rapid” is exactly the right word, as it was just in 2006 e-reader sales were a measly 100,000, whereas 25 million unitsare expected to be sold in 2011.

The Economist article mentions some startling ramifications of this trend towards electronic reading and publishing (paraphrased):

  • E-Books have higher profit margins
  • E-Romance novels are selling like hot-cakes
  • Digital piracy is a threat
  • Pricing is all over the map

As more people switch to e-readers, and the tablet craze really takes off, there are certainly some implications.

First, the Long Tail, will be much more of a selling force. In the past, publishers would rely on big box stores such as Borders or the like to prominently display their wares. In addition, publishers would expect discount stores and warehouse firms such as Costco, to move book volumes.  With digital publishing, it’s conceivable that more players will have a “fair shot” at publishing success as clustering algorithms on Amazon andBN.com suggest books based on our browsing history or past purchases.   For sure, blockbuster titles will continue to have a conspicuous display on Kindle and Nook homepage screens, but readers will discover more book options as expert recommendation engines suggest likely interests that can be purchased in seconds.

Second, pricing will take on added importance. Today, print publishers wrestle with initial price setting as they must deliver books to stores that will sell and also create profits. Pricing must be decided before printing, because each book has a printed list price on the back cover.

However with digital publishing, there is essentially no need to establish a “set in stone” price. In a virtual world, publishers (and online retailers) can experiment with pricing every day, perhaps setting different rates by country, discounting “on the fly” based on daily e-book sales, or offering deals to Amazon Kindle customers through their “Special Offers” Kindle. Amazon shoppers know it’s not uncommon to view a book, say “Harry Potter and the Deathly Hallows” one day at $21.24, and then come back to the site tomorrow and see it listed for $22.09.  Pricing experimentation will happen instantaneously based on near real time data analysis—without the need to change store signage and update retail POS systems.

Third, as e-readers take over the market, there is danger of increasing the digital knowledge divide between “haves” and “have not’s”. If a person requires a $100 e-reader to check out a digital library book, will this create a knowledge gap between socio-economic groups?

These are just three implications for rising e-reader ownership and there are certainly dozens more. Can you think of other implications for the rise of e-books?

Algorithms For Competitive Advantage

Analyst firm IDC predicts that by 2020, the amount of data generated each year will reach 35 zetabytes. Companies are fighting this deluge in numerous ways. Some archive data for analysis at a later point in time, some purge data as quick as they obtain them, while others capture, ingest, analyze, and use data for competitive advantage—sometimes in microseconds! And in a sea of plenty, it’s often the best algorithm that wins.

An algorithm is simply a step-by-step approach for solving a problem. Think of an algorithm like a formula; it can be complex, or relatively simple in design. Now add compute power from today’s super fast computers coupled with the know-how to design, build, and maintain these formulae and you have a winning combination! Companies across the globe use algorithms to make recommendations (think: If you like this product, you’ll probably also like this), choose optimum delivery routes for packages, and even route calls to agents that can best diagnose a particular problem.

How can an algorithm confer competitive advantage? Depending on the type of business you’re in, it’s easy to see how algorithms can reduce all available choices into the very best options. Take for instance, Google. In the February 22, 2010 issue of Wired Magazine writer Stephen Levy points out, “For years, (Google) has used its mysterious, seemingly omniscient algorithm to, as its mission statement puts it, “organize the world’s information.” Google’s algorithm is constantly tweaked, honed, tested, and improved to better interpret searchers’ requests, no matter how awkward or misspelled, says Levy. And this competitive advantage in its search algorithm has (so far) confirmed a 65% share of the search market for Google.

In a sea of data, algorithms can also help reduce choice overload. Online dating sites often use proprietary algorithms to divine appropriate partner matches based on user inputs such as preferences for race, religion, eye or hair color, and more.eHarmony’s algorithm for example, helps select potential partners based on a 258 question personality test. eHarmony’s algorithm takes too much choice (sea of available singles) and distills/simplifies millions of choices into much more manageable options.

And while companies like eHarmony rely on data input by a user, a new recommendation engine called Wings mines your social media “bread crumbs” left on various websites (including Facebook, Netflix, Twitter, Foursquare and others) to feed into its algorithm to pick a suitable dating partner. A MIT Technology review article on Wings says, “The idea is that the computer’s analysis of your behavior provides a richer analysis than you’d say about yourself.”

More data has been created in past three years than in past 40,000 years, says Teradata CTO Stephen Brobst. Indeed, today and into the near future, companies that can sort through, analyze and utilize this rich trove of data treasure faster (in some cases with blinding speed) than competitors will dominate over those enterprises slow to comprehend this critical transition.

Related: “Social Network Analysis: Hype or Help?” and “The Zero Latency Future is Now

Questions:

  • Are recommendation engines becoming more or less reliable? Think of a website you often use that uses recommendation algorithms. How “close to home” are its choices for you?
  • Do you think a computer can discern your tastes in romance better than you can?