When you hear the phrase “readers’ advisory,” do you think of the single librarian recommending books to the individual user in the library? The three presenters at “Harnessing Research and Data to Advance Readers’ Advisory Services,” a program sponsored by the Reference and User Services Association at the American Library Association’s 2016 Annual Conference and Exhibition, challenged attendees to start thinking about readers’ advisory in a more holistic, aggregate, and data-informed way so that they could better serve their communities.
Barry Trott, special projects director at the Williamsburg (Va.) Regional Library, kicked off the session with a presentation that focused on using the data libraries already have to inform readers’ advisory and improve circulation, selection, and marketing. “We need to do it in a thoughtful way, not a numerical way,” said Trott. He urged those in attendance to use circulation records and demographic information available to their libraries to make “data-informed, not data-driven decisions.”
Trott also touted the benefits of form-based readers’ advisory—where an intake form is administered for those looking for reading recommendations—for the “wealth of data” it provides. “I invite you to go back and look at your readers’ advisory form,” he said, urging attendees to think about what questions their libraries could be asking to get a better profile of their borrowing communities. Williamsburg Regional Library’s own form is comprehensive. “We ask users to tell us what kind of tone, mood, and style they’re interested in reading,” said Trott. Ultimately, he said, data-informed and form-based readers’ advisory can shape marketing and collection building, improve staff training, and inspire library programming.
Building off of Trott’s presentation, Cindy Orr, digital collection advisor at OverDrive, explained how Big Data factors into readers’ advisory. “It’s incumbent on us to become more expert than our readers,” Orr said, and suggested that using predictive analytics and distant reading—where data and computers discover patterns across text—is a way to do that.
Orr provided examples of distant reading, including the “X of Y” research of Franco Moretti, which analyzed patterns in book titles, and the “clustering” research of Matthew Jockers, which used data to determine which writers were the most influential and original, as a means to show how Big Data can predict popular books and authors.
“Even if you don’t have the expertise, Big Data is really hot right now,” said Orr. “There are a lot of university students studying it, and maybe you can form a partnership—they would probably love a real case study.”
Michael C. Santangelo, electronic resources coordinator for BookOps, a technical services organization that serves Brooklyn Public Library and New York Public Library, concluded the session with a review of current research on reading and readers’ advisory.
Santangelo recommended the work of several researchers:
- David Beard and Kate Vo Thi-Beard, who take a look at the current limitations of readers’ advisory that focuses on “what” books people pick and not “why”;
- Keren Dali, who emphasizes that readers’ advisory should move “from book appeal to reading appeal”;
- Bill Crowley, who stresses that “our current models lack a sense of public purpose”; and
- Soheli Begum, who advocates for escapist reading and taking a more holistic view of readers’ advisory.
A question and answer period followed the presentations, and one attendee asked presenters if they thought privacy concerns are a big challenge to libraries looking to improve readers’ advisory.
“I think we need to have discussions as a profession as to what privacy means, vis-a-vis providing good service,” said Trott.
