The July article, “Your E-Book is Reading You,” published in the Wall Street Journal, is an excellent example of how marketers can use big data to learn more about what consumers are doing, thus tailoring what they offer the public.
“Barnes & Noble has determined, through analyzing Nook data, that nonfiction books tend to be read in fits and starts, while novels are generally read straight through, and that nonfiction books, particularly long ones, tend to get dropped earlier. Science-fiction, romance and crime-fiction fans often read more books more quickly than readers of literary fiction do, and finish most of the books they start. Readers of literary fiction quit books more often and tend skip around between books.”
While it’s clear that a primary objective of B&N (or any other company operating in this space) is to make more money by learning about consumer reading behavior, I see plenty of opportunity for this data to be beneficial to individual users, too. The article states that B&N reviews data in terms of groups of readers, not individuals, though it’s not clear whether they do so for consumer privacy or other reasons. Personally, were I offered the ability to view statistics and information about my own reading habits, I would happily opt in to a program as long as my data couldn’t be shared for the wrong reasons or without my permission. Here’s why.
1 – The software and devices have the ability to track how quickly I read. That’s interesting information to me. Assume an average reading pace was established for me, and I was reading a particular piece more slowly than normal. Perhaps that’s because the material is difficult to digest. I might really like to have new features enabled in the piece I’m reading, such as links to other related explanatory materials – word definitions, wikipedia entries, whatever. I can imagine this having educational applications, as well. iPad popularity has exploded in public education. Imagine content designed with features that could help educators track the skill level of readers and tailor assistance and interventions based on detailed data profiles for students.
2 – I am an avid reader, and though I don’t read on my iPad or Kindle all that often, I might increase the time I spend on those devices if I felt I’d get legitimately strong recommendations for further reading. Amazon is in an obvious position to capitalize on this, and you might argue they already do so via their primary e-commerce website. But imagine how much more relevant their recommendations might be if they could analyze data from within a book (the lines or phrases I highlight, the bookmarks I place, etc.), and not just based on the titles I’ve purchased with them.
3 – Similar to my point about recommending related or similar reading, there are other recommendations that could be relevant to specific kinds of readers. For instance, if I read multiple books by a particular author, I might want to know if that author is going to be speaking at a conference or signing books in my area. If I tend towards academic reading, I might want to know about related courses offered by local or online universities.
4 – Often we are measured for job opportunities based on degrees and job experience, and not much more. If I invested significant time reading and learning about topics that might be relevant to my career, or even a career change, imagine the benefit of being able to integrate information about what I’ve read into my LinkedIn profile. I can currently link to my GoodReads page if I want to, or incorporate an Amazon reading list – but these lists don’t show I’ve actually read any of the material.
There are probably plenty of additional ideas around how this data could be used not only for a seller’s benefit, but for a consumer’s benefit, and I believe success with Big Data is going to be “bigger” when both parties are served.