This might be the paranoid data geek in me, but the first thing that came to mind when learning about Amazon's WhisperSync capability was the incredible data collection opportunity it presents. As it's advertised, WhisperSync is helpful to the Kindle user because it keeps your books in sync across multiple reading devices. Start a book on your Kindle, read to page 23, and put it down. Later while in line at the supermarket, open the Kindle app on your phone and it syncs to your last-read location, starting you right there on page 23.
Did you see what just happened?
For this to work, that means that every time you turn a page, a timestamped data point is logged and sent to Amazon. Over time, this means they amass an incredibly detailed reading profile of every Kindle/WhisperSync user. This profile would, presumably, extend beyond merely which books you read; the data will show which books you do or don't finish, and the relative speed with with you finish particular books, chapters, and sentences. General reading habits are there for the scraping too - are you a nighttime reader? Self-help books at lunch? Teen fiction on cross-country flights? Just as web cookies and advanced click tracking mechanisms have tilted the scales of web UX development from art towards science, WhisperSync brings an exciting new dimension to book retail.
Of course, I don't presume to think that I'm the first person to realize this. Amazon has buildings full of ridiculously smart engineers and marketing types, and it's a safe bet that they're doing something more interesting with this data than simply reminding you what page you were on and then sending it to /dev/null. But since these hypothetical uses weren't detailed in the literature that came with my Kindle, I thought it would be fun to speculate on a few things that Amazon could do with a few billion page numbers and timestamps.
Amazon is already the king of this and they wouldn't necessarily advertise it if they were doing so, but the data on how vigorously you consume one book vs. another would go a long way towards recommending what you should read next.
Example: Carson Parsons is an avid reader of business books. Martin Smartypants has written a hot new book on management techniques, and Carson's purchase history combined with the popularity of the author would suggest that the book should go high on his list of recommendations. Should be a good match, right?
Not so fast - Carson's reading history recorded in Amazon's new (hypothetical) AwesoSuggest system indicates that his pages-per-minute takes a significant hit when the reading difficulty level goes too high. In fact, for his last "difficult" book he attempted, Carson had a spirited start before slowing to a chapter a week, having to re-read several sections, then down to a few pages a night, and finally abandoning it completely before reaching the halfway point. Amazon would do better to recommend something more appropriate to Carson's reading level, say, "21 Ways to Liven Up Your Letterhead."
If for some reason Amazon couldn't get enough value out of this data on their own, imagine what a useful source of feedback it would be to authors and publishers to know the speed and manner in which readers get through their books.
Example: Mae Donahue authors a series of romance novels in which decent North American ladies fall inexplicably in love with alluring European men. For her newest novel, she tries something new, introducing her readers for the first time to the concept of unrequited love. Sales are sluggish and word of mouth is unfavorable, echoing the general sentiment of "it's just not as good as the last one." Worse yet, pre-sales for the next installment are way down. What can she do?
Enter Amazon's (hypothetical) ReaderHabits service. For a small fee, Ms. Donahue gets a rich breakdown of how her every book is consumed, down to the sentence. The report would show that among her normal readers, pages-per-minute was typical until page 37 when the lead character decides not to take the risk with the Spaniard whose English is "no so good", deciding instead to spend the next three weeks studying for her GRE. Reading pace slows to a trickle, with 1/3 of her readers giving up completely. Amazon can pinpoint exactly where things went wrong and how severely it turned readers off.
That, Mae Donahue, is quantitative feedback.
A/B Testing (AKA "Choose Our Own Adventure")
Here's where it gets interesting. It's one thing to measure the effects of the choices an author makes in telling a story, but how about testing story ideas on a real audience? Can you optimize a book?
Users unfamiliar with modern web product development might be surprised to learn that the green "Click Here" button they see on a website is actually a blue button for their neighbor who, by chance, saw a different iteration of a randomized trial. Websites optimize all the time by showing variations of a button, color scheme, page layout, messaging, etc., to different users until a large enough sample is taken to achieve significance and demonstrate a clear winner.
See where I'm going with this?
Example: Author Trent Dentley is struggling with the idea of killing off a major character in Chapter 4 of his adventure novel. The deadline approaching, he seeks guidance from his publisher. The publisher wisely suggests that Trent write three versions of the chapter and submit them to Amazon's new (hypothetical) OptimalNovel program. Early Kindle downloaders of the book will randomly see different versions of Chapter 4, and based on how they react to the plot development, (do they keep reading, do they speed up to see what happens next, do they stop altogether,) the most compelling version will be optimized and it will become the "official" version that all future readers get when they download the book.
The tested changes don't have to be this drastic, of course. The game breaks down when users realize that they are test subjects. A simpler example might be an author who is trying to create memorable characters. By testing different combinations of name, description, and backstory, the memorability of a character can be measured by watching how much readers have to flip back several pages or do a search to remind themselves who a particular character is when he reappears after an extended absence in the story.
Amazon, despite their massive library, Segway sales, and efficient packaging, is in the data business. Their established online presence gives them a stranglehold on the "where" and "how" of people purchasing consumable media, and their extended reach with WhisperSync (and more recently Cloud Player for music) now provides the feedback for what happens after consumers press that 1-Click.
How much did you enjoy your last book? Maybe you can't even put a finger on it, but the numbers won't lie. "Pageturner", once just an expression, is now quantifiable.
Follow me on Twitter for more speculative non-fiction paranoia: @wooswiff