How Blockchain Could Impact the Publishing Industry

The publishing sector is now being influenced by blockchain and NFTs. NFTs are used by conventional publishers to create communities and interact with readers. Let's examine how blockchain has drastically altered the publishing sector.

In the realms of business and finance, blockchain technology is no longer a buzzword. The potential of blockchain technology has been brought to our attention throughout different business sectors because of the popularity of bitcoin. 

To unleash the potential of blockchains, numerous industry experts have recently analyzed the technology and developed use cases within their respective fields.

Technology has the ability to completely change how publishers, writers, distributors, and merchants conduct business in the publishing sector. The entire publishing supply chain will benefit from blockchain technology.

The digital transactions of peer-to-peer networks are recorded in blockchain, because it is a decentralized public ledger. It was initially created to act as the foundation for cryptocurrencies like Bitcoin, but it is currently widely used in a variety of applications.

One of the biggest and most significant industries in the global economy is publishing. It has been around for an extremely long time and is a multi-billion dollar market. With many of the largest brands adopting the internet, publishers have taken the lead in online product sales.

Some people in the industry believe that blockchain's advantages for publishing platforms could be the solution to a lot of the problems faced by the industry.

There are two significant ways how blockchain technology can transform publishing:

1. More Affordable Payment Alternatives for Content Producers
By eliminating the need for an intermediary in online transactions, blockchain technology increases the financial gain for authors, photographers, and editors. 'Smart contracts,' which automatically release funds when publishers approve the content, is also possible at the same time.

2. Assistance in Managing Digital Property
By granting intellectual property rights, such as the prohibition on readers being able to resell or even share a publication with another person, blockchain can help boost the popularity of digital publishing platforms.

Additionally, Blockchain can assist publishers in the following ways: 

• Identify unauthorized sharing and stop it.

• Maintain a certain set of rights and modify them in a way that will increase the possible profit from their current agreements.

• Better collaboration will result from their ability to monitor all workflows in real time.

• Improve digital advertising so that they can provide their audience with a reason to interact with their content.

Publishers should understand blockchain now, whether that is signing up for a social network or creating a cryptocurrency wallet. These are small efforts toward investigating this new, disruptive technology that may have a strong influence.

THE USE OF BLOCKCHAIN IN THE PUBLISHING INDUSTRY IS EXPANDING
Digital publication is likely to be disrupted by the blockchain. Simply by streamlining content distribution and enabling more efficient payments, this cutting-edge technology can address the problems that publishers have been dealing with for a very long time.

As of today, there are some forward-thinking initiatives using blockchain technology to build abrasive platforms for the publishing sector. The blockchain world is revolutionizing and several crypto pr service providers have changed the way we look at the publishing and distribution industry.

MORE ADVANTAGES FOR A PUBLISHING PLATFORM THAT IS USING BLOCKCHAIN Every sector of the economy, including publishing, might undergo a change, thanks to blockchain technology. Every industry that has embraced it has experienced success. The publishing sector may actually experience flexibility, integrity, and reliability thanks to its dynamic standard features.

SHARE OF PROFITS WITH STAKEHOLDERS
The advantages of blockchain technology will be experienced by all parties. Designers, editors, and other stakeholders will be compensated in accordance with the blockchain database regulations.

Blockchain technology allows for the permanent tracking and linking of a person's contributions to the advancement of publishing.

Smart contracts enable the digitization and automation of a wide range of processes. Without the need for manual assistance from human intermediaries, people and organizations can conduct business with one another.

It won't be necessary for someone to constantly fix all the bugs or mail checks, because contracts that are also on the blockchain will conduct and distribute sales among the stakeholders automatically.

BUILDING THE ECONOMY FOR PUBLISHERS AND AUTHORS
Authors must rely on third-party service providers to provide secure content delivery, banking transactions, or private internet activity.

The content market is significantly impacted by Amazon and App Stores. As a result, writers and developers don't get paid as much as they should for their efforts.

Blockchain technology, on the other hand, has the ability to deal with intermediaries.

Authors will be able to trace the dissemination of their content and establish an electronic cash system for users thanks to blockchain technology. Additionally, authors will take back control of their work, moving up from the position of content distributor to primary source.

ADMINISTRATION OF SUPPORTING PROJECTS
A book is not the only source of income for an author or publisher. Fan fiction, movie adaptations, and spin-offs can all generate money from a book that has been published.

Some of these supplementary activities are referred to as "accessory items," and they are employed to raise awareness of a book and maintain customers' interest in a brand's storyline. Authors and publishers will be able to secure these auxiliary projects for their benefit by using a blockchain.

Blockchain technology will make tracking easier because it is a decentralized ledger that allows any transaction to be carried out without seeking anyone's consent.

SMART WALLETS
By utilizing smart wallets, blockchain can provide a safe payment method for publishers and authors. These encrypted wallets will be able to work with cryptocurrencies or tokens that are already known to be utilized inside an ecosystem for a project.

Blockchain can also be used to develop encrypted smart wallets that are more secure than any other kind of wallet currently available. These wallets make it simpler than ever to make payments without dealing through an intermediary like Google Play or iTunes since they can handle tokens for specific projects or commercial settings.

BLOCKCHAIN CAN ASSIST IN THE MANAGEMENT OF DIGITAL PROPERTY
Blockchain's ability to grant intellectual property rights can serve as a catalyst for the digital ecosystem. E-books can't experience the same growth cycle as physical books due to cultural conventions. You aren't allowed to sell or even rent your electronic book under this method.

This makes it impossible for readers to recommend their favorite books to friends and family members and for authors to get royalties from secondhand sales and borrowing fees.

Store-bought books are a rare breed that are vanishing. By monetizing their use, e-books and blockchain provide a potential to increase their value beyond anything originally expected, yet this is just the tip of the oceanic plate.

Imagine if we had the option to freely sell or share our e-books with others, including libraries, schools, and institutions all around the world.

The most secure method for electronically storing data is now blockchain. A tale cannot be modified after it has been written without leaving a mark. Blockchain keeps track of every transaction for each copy of the book, making it impossible for someone to copy or steal your story and profit from your personal project.

READY FOR BLOCKCHAIN PUBLISHING!
Publishing could change its criteria, thanks to the groundbreaking technology known as blockchain. Authors would be able to profit from their work and own their intellectual property without having to worry about plagiarism or copyright violations.

Blockchain will play a significant role in the future in digital publishing.

CONCLUSION
It is evident that some publishers are apprehensive about engaging with technologies like AI and Blockchain. But having a basic understanding of these concepts will make them more adaptive and open to embracing these technologies, which will ultimately increase their chances of growth and diversification in the years to come.

How Top Publishers Use Keywords

In 2015, Kadaxis (with some help from Bowker, Firebrand and OnixSuite) set out to investigate whether publishers were adding keywords to book metadata. The conclusion was that of the 150,000 publishers reviewed, most weren't adding keywords, and of those publishers who did, the volume and quality of keywords was low.

Anecdotally, we've seen a significant shift in the past three years in the priority publishers give to keywords. We wanted to understand this change more deeply, but instead of taking a large sample of publishers, we adopted a qualitative approach and narrowed our analysis to 846 fiction titles with significant sales. By looking at how keywords are used on a publisher's most important titles, we can infer how important keywords are to a publisher and gain some insight into different keyword methodologies.

Findings

Keywords matter to the top publishers: of the 846 books we analyzed, 69% had keywords - a significant increase to three years ago.

Most publishers still target a 500 keyword character count. Of the books with keywords, 44% had a character count between 480 and 500 characters. See our investigation into why 500 keyword characters is not optimal for selling more books on Amazon:

Optimization savvy publishers are emerging though: 6.2% of books had keyword character counts above 500, while 2% had character counts above 1000 characters (many of these are Kadaxis clients).

Keyword Content

Almost all of the top fifty most commonly used keywords across all books in our set related to category names.

The keywords "fiction" (most common) and "fiction books" (6th most common) were seen throughout the sample set. Search engines are able to derive whether a book is fiction or not by examining the book’s categories, so adding these terms as keywords is often not necessary. Likewise, BISAC and browse node (Amazon only) category names are also indexed by search engines, so if a book is assigned to a category, repeating the category name as a keyword won’t increase the book’s visibility in search. Category and genre names are relevant and appear to make sense as keywords, but understanding how search engines index metadata can often mean a more efficient use of the keyword field.

Rule Breakers

One observation we've made from working with publishers, is that Amazon applies rules discriminately. Large accounts are generally afforded more relief from the rules, and it appears many publishers may be aware of this extra freedom. (See section "Keywords to avoid" from Amazon's rules for KDP authors).

Of our sample set with keywords, 4% of books included the term "bestseller", while 15% broke the "Subjective claim about quality" rule by stating that their books were the “best”, for example: “best horror books”, “best selling fiction author”, “best american novel” and “best fantasy series”.

We also found countless examples of keywords comprised of competitive author and title names, along with the the use of Amazon program names (e.g. "kindle" as a keyword). One publisher even tried to cash in on deal days with the keywords: “cyber monday deals” and “black friday deals”.

Amazon likely filters out prohibited terms, but one exception is the use of competitive title and author names which, when indexed, do improve search visibility.

Conclusion

Publishers with high sales volumes take keywords seriously and in most cases add keywords to their book's metadata. The quality and volume of keywords has improved significantly over the past three years as we've seen publishers move from rarely adding keywords to commonly adding keywords. While keywords were generally well considered, when assessing phrasing, term redundancy, volume and other characteristics used to assess keyword efficacy on Amazon, the trend highlights coverage as a priority for most publishers, ahead of the more involved specifics of optimization. The industry is evolving though, and gaining a more sophisticated understanding of how search works. Our prediction is that in our next review we’ll see an even greater incidence of highly optimized keywords, incorporating this growing body of knowledge.

Machine Learning and Bestseller Prediction: More Than Words Can Say

There’s been much recent conjecture on whether book sales can be predicted by text analysis alone. My company, Kadaxis, has dedicated the past few years to machine learning research and product development for the publishing industry. In our early days, we set out to build an algorithm to predict bestsellers, and tested it in the wild. In this post, I’ll share my perspectives on why the text alone isn’t enough.

If You Publish It, Will They Come?

To predict book sales, you need to account for the factors that influence book sales. The text of a book is core to the product, but many other factors, such as sales and marketing, influence whether a customer will discover and buy it. An algorithm predicting book sales using only the text as input will only work in a book market meritocracy, where the best-written books always sell the most copies.

Author platform (brand awareness) is one such non-text factor that influences sales, as in the following examples:

– The Cuckoo’s Calling hits the top of Amazon’s bestseller list only after Robert Galbraith is revealed to be J.K. Rowling.
– Amy Schumer’s memoir, The Girl with the Lower Back Tattoo, hits the New York Times bestseller list in its first week—an improbable feat without her strong personal brand.
– Dave Eggers publishes multiple books and receives several award nominations prior to releasing bestseller The Circle, and does so after having appeared on television numerous times.

Even amongst well-discovered books, the relationship between reader satisfaction and sales volume can be tenuous. Consider Harry Potter and the Cursed Child and Go Set a Watchman, books that have sold millions of copies each, but have achieved star ratings of 3.6 and 3.4 on Amazon, respectively—scores below the average indicator of satisfied readers.

Many other factors might also influence book sales, such as the editorial process, cover design, marketing budget, seasonal trends and book metadata. A machine, just like a human, needs to consider which of these factors will make a book sell more, to make an accurate prediction.

Machine Reading

Assume for a moment a linear relationship exists between reader satisfaction, discoverability and sales (i.e. the best written books are found the most often and sell the most copies). In this author’s utopia, we can reliably predict sales volume directly from a book’s text, as long as we can measure what’s important to readers. As products go, books are nuanced and complex, and the reasons why they resonate with us are also complex (compared to, say, a toothbrush). How do we uniformly distill the unique traits of a book into data?

This is, of course, where machine learning helps us. One approach, which is also the method used by the authors of the much-talked-about The Bestseller Code, is topic analysis (or latent Dirichlet allocation). This technique allows us to define a book in terms of how much of a topic it contains, such as “Homicide – 8.7 percent.”

If you’d like to see the data a topic model creates, you can view an example from our systems here (or upload your own book for analysis at authorcheckpoint.com). Topic modeling gives us a good snapshot of the content of a book, and allows us to make apples-to-apples comparisons between them. It is also useful data to use as input to training a predictive algorithm.

The Curse of Dimensionality

Our machine reader might define thousands of topics for each book we’re analyzing. While more data points might seem like a good thing, the more we add, the more books we need to read in order to make reliable predictions. If, for example, we had 2,500 different data points about a book, we’d likely need several tens of thousands of books to be confident our algorithm is accurate. Even 20,000 books (the data set used in The Bestseller Code) is likely far too few books, and puts us at risk of the curse of dimensionality.

(A quick tech side-bar: even with cross-validation we’re still likely overfitting our data, and hold-out is no guarantee against this, especially when using heavily unbalanced classes such as “bestsellers” for classification.)

Too many data points, and not enough books, means our algorithm will probably find patterns to say whatever we want them to say. The patterns exist in the data, but they aren’t representative of the real cause of what we’re trying to predict. In the world of black-box trading systems, this phenomenon is well-known.

So is there value in analyzing the intrinsic qualities of books such an algorithm might identify as selling well? It might be an interesting exercise, and the similarities the algorithm finds might make sense to a human observer. But you couldn’t reliably conclude that those similarities were the reason the books both sold well. In a contrived example, we might conclude that books with a red cover, 250+ pages in length and featuring a dog instead of a cat, will sell more copies than those without.

There is, of course, a simple way to prove the efficacy of any predictive model, and that is to apply it to new, unseen books before publication.

Predicting What’s Important

Even with access to enough books in our author’s utopia, we of course need a reliable metric to measure. Bestseller lists are a weak proxy for actual sales volume for many reasons, not least for the fact that they reflect “fast sellers,” meaning a book on a list may sell less overall copies, over time, than a book that isn’t.

But rather than searching for a magic formula to help move more copies of a book, a more valuable and attainable goal is to solve for reader satisfaction. By tying together data about the content of a book, with data capturing a reader’s reaction to it (beyond tracking where they stopped reading), we can begin to understand the true impact a book has on a particular audience and why. Armed with this insight, we can better match books to readers (recommendation systems) and books to markets.

This article originally appeared on the DBW blog September 28, 2016

Who Uses the Keywords in Metadata?

We often hear that keywords are important to help readers find and discover books. But what does that mean, and do keywords actually make a difference? In this post, we look at how keywords are used to search book websites (in particular, online booksellers), and their adoption by publishers. For this investigation, I had help from Pat Payton (Bowker) and Catherine Toolan (Firebrand). We set out to answer the following questions:

• Are publishers adding keywords to book metadata?
• Are they providing quality keywords?
• Do online booksellers use keywords in their search engines?

In this post, “keywords” refer to consumer-oriented terms to describe a book that are added to an ONIX feed and sent to third parties. These terms aren’t seen by the public and are primarily used for search indexing. Conversely, web search engines (such as Google) don’t make use of ONIX keywords, but analyze the text of public webpages to create search indexes. As book content isn’t public, search providers rely on metadata to help consumers locate books.

Keywords Help Consumers Find Books

Most retailers solve the simple use cases of finding a book by title, author or category. Many searches, however, are comprised of natural language queries that describe different elements of a book, such as its setting, characters, theme or an emotional response to its content. Keywords were designed to fill this gap, by allowing people knowledgeable of the book to specify additional terms by which to find it.

Books are multi-dimensional, complex products that are typically highly nuanced and represent multiple buy trigger points for different types of consumers. Books have much more depth than, say, a kettle or a toothbrush, and determining the best keywords is therefore proportionally complex.

Note that extracting keywords from the book’s text is a naïve approach to solving this problem. The most effective keywords relate to a reader’s experience with a book, and the language she uses to describe it.

Are Publishers Adding Keywords to Their Books?

Bowker analyzed the keywords added to ONIX files from roughly 150,000 publishers, which included reprint and self-publishing service providers to university presses, trade, school and audio publishers. Of these publishers, about 23,000 (15.3 percent) had added keywords to at least one book. And of these, smaller publishers (less than 100 titles) typically had a higher percentage of keyword coverage than did larger publishers.

Over the past 10 years, though, publishers have increased the number of titles with keywords from approximately 25,000 to approximately 114,000, in 2015. But this number is still a very small proportion of all books available.

How Sophisticated Are Publishers’ Efforts to Choose and Maintain Keywords?

While keywords have been part of the ONIX standard for many years, they definitely rose in importance around 2013. As publishers had whole backlists without keywords, obtaining coverage was (and still is) a resource-intensive task. In order to achieve high coverage of keywords across a catalog, many publishers undertook a stopgap approach, adding other metadata to keywords (from title/subtitle, subject codes, contributors, product format, and audience), which are already available to search providers, and therefore are unlikely to help with search visibility. To improve keyword quality and to recommend against practices such as keyword stuffing, the Book Industry Standard Group (BISG) published the “Best Practices for Keywords in Metadata,” in 2014, to guide publishers on choosing effective keywords.

Keyword quality is still low today, though. One example from Bowker shows the use of the keyword “audiobook” (relating to form, not content) in just about 12,000 of the approximately 114,000 titles sampled from 2015.

Do Online Retailers Use Keywords?

Every book search implementation is proprietary, so the exact use of keywords is generally not public knowledge. It is possible, however, to determine whether keywords, when used as search queries, return the books they’re associated with in ONIX.

Kadaxis tested 13 websites that consume ONIX and provide book search, and found that only Amazon showed books returned in search results for keywords attributed to the book in ONIX.

Keywords are central to Amazon’s search capability across all its product lines. The site receives keywords of wildly varying quality from a huge number of product suppliers (from individuals to large companies), which means its capability for filtering, cleaning and incorporating keywords into a search index and mapping these to consumer search queries is sophisticated.

As the quality of keywords provided by publishers is generally low, it is a challenging endeavor for other websites, without this history and experience, to use the data as extensively.

Are Keywords Worth the Investment?

From the research above, Amazon is the only online bookseller making use of keywords today. If increasing sales of books on Amazon is important, then investing in keywords may be worthwhile. As most publishers aren’t adding keywords to their titles (and of those that are, the quality is typically low), there also appears to be a window of opportunity in which publishers can gain a ranking advantage in Amazon’s search by adding keywords to titles.

Conclusion

While some publishers (see here and here) are quietly providing effective, consumer-oriented keywords, most aren’t investing significant resources. But doing so might represent a low cost, low risk investment for a potentially strong, recurring return. At least until a better solution is created, that takes the onus of keyword curation away from publishers and authors.

Additional thanks to Chris Saynor from OnixSuite.

This article originally appeared on the DBW blog March 4, 2016