The Social Life of Numbers

Increasingly, data analytics is becoming a major driver in many markets. This is largely in part due to the proliferation of data that is out there and the many sophisticated tools that people have developed for analyzing this data. Now, more than ever, businesses are able to make informed decisions, and conversely businesses are realizing that to ignore data would prove detrimental to their success. Publishing is seeing uptake of this mindset with initiatives such as Booknet, Nielsen BookScan, (now The NPD Group), and Bookstat, among others, which track book sales, and projects that attempt to mine the data of literature at more granular levels, such as plot and sentence structure. Other initiatives are aiming to crack the “blockbuster” code—that is, scan manuscripts using a sophisticated algorithm to determine whether or not this book could be the next big hit.

I support the gathering and usage of data at the point-of-sale level. This data can provide insights about the size and shape of the publishing industry, help publishers manage inventory and distribution, and can also be used to help predict sales, which can help publishers at numerous stages of the acquisition and production process. I believe that this kind of macro-level data can support the human decision making process without supplanting it, and it is for this basic reason that I object to the use of algorithmic data to scan manuscripts. I believe that data use in this way would fundamentally stifle innovation because the algorithm would essentially be backward-looking, because it was built using books already published. For this reason, I also feel like it may be unable to accomplish the task it was designed to do. Blockbusters are so successful partially because they are doing something new or fresh—readers are intelligent, and they know when they’re being sold something that they’ve seen before.

Where I feel that data could be used more meaningfully and beneficially in publishing is in the area of marketing and social media. Increasingly it seems to be the case that books live or die depending on their author’s social media platform and presence. I believe that this is owing to the ubiquity of social media—people are now able to be connected to almost everyone almost always, which has conditioned them to want this. Consequently, the figure of the author is becoming more and more central to a book’s success.

So, what if there was a way to analyze an author’s social media presence and reach in a streamlined way, and then apply that knowledge to knowledge of the social media market on a large scale, to help construct and plan a social media strategy to gain that author the greatest reach possible? An algorithm could be constructed based off of press campaigns for past books and authors, sales data, and social media reach before and after the campaign. Ideally, the algorithm could also look at market distribution to help publishers plan book launch tours based on where receptive audiences (according to interest, affiliation, etc.) cluster.

Essentially, I’m not comfortable using data to help shape the history of literature. I believe that that should be done with the human eye, to allow for and encourage innovation. I do, however, believe that we could be using data in a more meaningful and robust way to help market books once they have been selected for publication.



2 Replies to “The Social Life of Numbers”

  1. Hi Steph,
    Thanks for your feedback, you always have such catchy titles! Publishing is certainly looking at data more and more. I do like your point about using data within marketing and social media. I think this is where a lot of research is already happening and wouldn’t be impeding on people personal privacy even more. You definitely have a point that gathering data on hits as backward-looking. In doing so, we’re pretty much just repeating what is done.

  2. You make a clear argument here towards using data to promote books, but not for deciding what books should be published. But I wonder if you have considered that the same argument applies to marketing as well. What has worked in the past with marketing will not necessarily work in the future, and using the same kind of data analysis wont necessarily show when a new and different kind of opportunity arises. Except that I don’t actually believe that to be true. Data and algorithms are not perfect, but neither are they so simple as to only suggest that copies of the previous have worked before.

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