Classic writing transcends time. Novels written centuries ago still have resonance for readers; memoirs can provide compelling insights into how people lived in the past; poetry collections are eternal. But this notion of permanence does not always translate into effective marketing strategies. For publishers, once a book has been available for some period of time, they need to shift their attention to the next new release, in their efforts to appeal to readers and encourage them to keep buying new content.

The motivations of the marketing department might not align with the promise of the product though. Therefore, a new company is reaching back to the past, seeking out novels, nonfiction texts, and other formats that are terrific reads but that have gone out of print or simply have faded from popular attention. To support their efforts to bring the past into the present, it relies on technology of the future.

Specifically, Open Road Integrated Media leverages machine learning to scan the entire worldwide web for mentions of book titles in reviews, retail sites, or blog posts. When it identifies a title that has appeared at least a few times, the machine learning algorithm also proposes a creative marketing plan for the book, based on the content of the mentions already available. For example, if the reviews for a no-longer-familiar text constantly mention its thrilling content, Open Road might target audiences who have purchased more well-known thrillers. It also offers dynamic pricing suggestions and ideas for creative promotions on retailers’ websites to encourage wider dissemination.

In addition to these extensive technology-provided insights though, Open Road also relies on real, human consumers. It solicits input from approximately 3 million active readers who have signed up to receive its newsletters. These users might suggest additional titles for Open Road to reinvigorate with marketing efforts. But in addition, when they click on the recommendations linked through the newsletter, they help bump attention to existing titles, such that the retail algorithms (such as Amazon’s) are more likely to move those options higher on recommendation lists for their many other customers. All its titles are currently available only in digital form, though it has indicated some possibility of publishing physical versions in the future.

Noting the success of the titles that Open Road promotes—such that on average, the books’ sales double after its efforts—some publishers have entered into collaborative agreements with it, hoping to leverage its capacities to promote their own catalogs of content. In a more recent initiative, Open Road’s Re-Discovery Lit division works to identify texts that have gone out of print or for which the copyright has reverted to the author. Then it can reintroduce them in any way it deems best, based on machine learning and readers’ inputs. 

The effort seems driven, in equal parts, by utilitarian and nostalgic motives. By marketing old books to new audiences, Open Road earns revenue, as do publishers and authors. But in addition, as one publishing executive explained, “We have these great books that are waiting to be found by a new set of readers. But with so much focus on what’s next, some of those terrific titles get forgotten.”

Discussion Questions

1. Is affection or nostalgia for a great book a sufficient reason to devote marketing resources to it? On the other side, is identification by a machine learning algorithm sufficient?
2. What books, which you might have loved but that no one else seems to have read, would you recommend that Open Road promote?

Sources: Elizabeth A. Harris, “Decades Old? No Problem: Publisher Makes a Bet on Aging Books,” The New York Times, May 24, 2023; https://openroadmedia.com