Let’s talk about book discovery.
I’ve been thinking about this topic a lot lately since I was asked to participate as a speaker on the topic at Mini-TOC Vancouver later this month. I’ve been mulling around in my head the last few days about what I could realistically bring to the table in this conversation and what would be most relevant. Keeping with the spirit of TOC, I’ve decided to aim for that space beyond the now; really looking at what is possible and what this should be.
So, I started looking through a couple sites which, seemingly, offer “discovery” services to their users. The objective being that we can all find new and interesting books. Unfortunately they all fell flat. Every single one came up with an almost parroted list of the same bestsellers and top titles that we’ve all heard about before many times. On one site alone, I saw Outliers, The Lean Startup, the Jobs bio and all three Hunger Games titles. Not only are these major bestsellers, all of them, but they are also not the most recent bestsellers. How is this helpful? Or, let me phrase it this way: how is this discovery?
The discoverability problem in books is a challenge. It’s about connecting users to new and interesting titles, that they wouldn’t normally have seen. This last part bears repeating: …that they wouldn’t normally have seen.
Ultimately, the problem with all these discoverability sites is this: their algorithms (if they are even using an algorithm) are based on aggregate data in a one size fits all model. The more people who read something, the more often it shows up in your recommendations. But, that’s not discoverability. That’s the NYT bestseller list. That’s Nielsen Bookscan telling you the top sales of the week. Just because most of my friends are reading bestsellers (because, duh, whose aren’t? In fact, that seems to just reinforce the concept of the term “bestseller”) does that mean I should only be shown these titles?
Obviously, the answer is no. But, how do we get there?
Let’s take a step back. There are two distinct operations that must be executed in order for discovery to take place. The first is having a pool of books or products to pull from, ie an inventory of sorts; the second is connecting people to the right things in that inventory. (Here I don’t use inventory to mean having books sitting in a warehouse; rather, inventory as in a simple list of products.)
I argue that the first operation should and must be accomplished by humans. A curated list of products should be offered. To make discovery work, we have to rely on offering books that speak to a range of tastes, which is best elucidated by a group of individuals who make judgments. This is similar to the “staff picks” section of your local bookstore.
The second operation is where algorithms come into play. This is where user behavioral data coupled with data collected on customer preference (ie building a predictive model) allows us to connect those selected materials to the users in the system. Here a machine is better than a human and can provide for efficiency and scale. Here, users get better selections based on a range of preferences and are able to truly find new things. Here, we are not talking about selections based on aggregate, everyone is reading this so you should be too, data. We are talking about a less macro-level data set which makes connections.
Lately, I’ve started using the service offered by ClubW. If you are unfamiliar with ClubW, the concept is simple. Basically, it a new take on the concept of wine of the month club. A monthly selection of 12 wines are offered; the selection having been made by sommeliers on staff. First month, you answer 5 questions about your taste in food (do you like citrus? how do you take your coffee? etc.) and a selection of three bottles is made. You can go with their selection or choose other bottles. A box arrives with your three bottles of wine. You enjoy the wine. While or after enjoying the wine, you are able to rate each bottle individually. This informs the algorithm.
So there you have it. A human curated inventory, an opportunity to input user data, and an algorithm that is refined over time as users tell you more about what they liked and what they didn’t like. Did I mention that I have never heard of 90% of the wines offered? And I know a thing or two about wine. The point here is that I am trying and discovering things I normally wouldn’t. And they are not trying to sell me Two Buck Chuck because everyone I know is drinking it.
Back to books.
Clearly, this model implies a few things: A stronger set of metadata employed in such a way that allows for an algorithm of this sort to function (whispers strategic metadata), a group of individuals with a deep, or at the very least good, understanding of different markets to make selections (narrow and deep v. broad and superficial; hopefully individuals who are readers of these specific genres or verticals themselves; hopefully individuals like librarians and booksellers who are experts at making this connection already), and an infrastructure to accommodate.
This ideal carries implications on almost every step of the publishing process. Our workflows have to change. Our strategies may change. Our business models will look different.
The point is this: without involvement from publishers to accommodate this shared goal by changing how products are built and deployed, any “discoverability” tool will end up as they are now: bestsellers across the board while midlist titles, in many respects the foundational canon of actual discoverability, are nowhere to be found.
This is the basis for my talk at TOC. I hope to take these ideas to the next level by talking about some of the ins and outs of of how to get to where we’re going. I am squarely looking toward publishing in the future and refusing to accept the way things are done. Discoverability is part of the product. It’s not a marketing thing. It should be inherently included in the DNA of a book.
I leave you with this last thought: this is what’s best for the reader. It should always be about what’s best for the reader.
Update 10/10/12: Today, it was announced that Amazon launched Author Rank. A system which updates hourly and shows you the top authors in different categories. A prime example of bringing the bestsellers to the top and making those titles readily available. And another failure of real discoverability. Please add your comments below on what you think about this new development.