A Publisher’s Dream

The publishing industry has been through many big changes in, especially with the rise in popularity of ebooks and buying books from Amazon. Customer data a very useful tool in the publishing industry. If I were a publisher, data about reader’s data would be the most effective data for the company.

Gathering readers’ data especially their behavior and interactions with the book and knowing what readers find engaging and what they do not can help us as publishers unlock previously hidden assets within our publishing lists. We have seen a lot of books that got rejected at first because the publisher did not think it would sell but later ended up on the bestseller list. This can happen when there is not enough data for the publisher to make an informed decision. Therefore, the reader’s insights can help publishers understand their readers better and thus make better new editions of books and improve the quality of the books taking user input into account. User data can give us more information about which authors and genres we should invest more time in. It also helps in gaining market insights by acknowledging which types of books are running out of steam; if there is any problem with a book itself, the reader’s data will help us identify exactly where it is. By knowing where and when they stopped and continued reading  It will give us opportunities to make a decision regarding the publishing content. This can help paint a detailed picture, allowing publishers to predict future book purchases and forecast sales and predict bestseller list–every publisher’s dream!

The main concern we have as publishers is getting customers’ data without breaching their privacy. As I always mention, transparency is the key. We should be very clear with our customers on how we are tracking and collecting their data. This model will allow us to retain customers and attract new ones. . Even if, as a publisher, we are not collecting the data ourselves and we receive it from another party (what we see in most cases in the publishing world), we should not resell or share any private information.

Collecting data is crucial for business survival, yet there is no clear way to implement it without breaching anyone’s privacy. Taking into consideration how recent the use of data in business models, it seems we are in the trial and error phase. Companies are trying to use data in many different ways, some are failing and others are succeeding. I think that the next phase will allow businesses to collect data in an easy manner while being honest with the customer. But for now, as publishers, we should take the initiative to be transparent with users by giving them the option to provide their data or refuse to do so.

Digging for Gold: Reader Analytics and Data Mining in Manuscripts

As a publisher, if I had an all access pass to book data I would concentrate on my authors, their writing and my editorial team. I’m not talking about producing blockbuster after blockbuster, but simply having more hits than misses. Plus, only so many people read so many books a year which means the amount of blockbusters is finite. If I only wanted to be producing blockbusters then I’d be putting out two or three books a year, and somehow having a drastically reduced field of competition. No, I don’t need to sell a million copies of my author’s latest work (although that would be nice) but I do want to give their book the best possible chance to make it. How would I do this? By using reader analytics and data mining of course. Other publishers have already acknowledged the advantages.

A perfected Jellybooks would be my tool of choice. Being able to pin point where a reader struggles or stops reading would be beneficial for both the editor and the author to know. If the majority of readers are calling it quits after chapter three then some changes need to be made in the writing. My editor knows this book is a winner since the ending is spectacular, reflective, and thought-provoking, except no one is going to know that unless they get to the end! If the book lulls and you lose your audience (who is far less trained to recognize real talent and art, the je ne sais quoi of good writing than my editors and their gut) then it doesn’t matter how good the potential of the book is. Maybe all it will take is a little tweak to keep readers hooked.

Wouldn’t the authors have a problem with this? Sharing their precious baby before its ready for the cold world when it still needs some time to incubate with their editor. Yes, writers are sensitive and having their work picked apart by a bunch of strangers certainly doesn’t seem appealing and there are mixed opinions on beta reading. I would encourage them to reconsider, and to look at it as an investment in beta testing and although it may be painful it would at least give their book the best chance it could get before being released to the real cold world. Wouldn’t they appreciate a test-flop before a real flop? At least they have the time to go back and tweak their manuscript some more.

Plus, there are only six basic emotional arcs of storytelling and by data mining the manuscripts my editors would make sure that they keep on track with patterns readers are familiar with. Of course, this doesn’t mean the stories can’t break rules, and it’s possible to build complex arcs by using basic building blocks in sequence to create something unique. If my editors are able to catch a dip or spike in an already established arc, then it would be easier for them to hone in on the problem area and adjust it accordingly. Data mining manuscripts offers editors a map to the potential problem areas, and the chance to dig in and use their editorial training to adjust these segments. Generally, a good editor would be able to find these problem areas and lulls regardless, but an algorithm speeds up the process and allows for more time dedicated to workshopping the section.

Data mining manuscripts and using reader analytics isn’t about removing the human element from editorial work, quite the contrary. Reader analytics is studying human behaviour with reading, while data mining manuscripts is simply expediting the grunt work editors would have to go through regardless. Editors can use these tools to streamline the process they need to take with the manuscript and combine it with their gut instincts and human experience to allow a book to reach its full potential.