Knowing What the Patron Wants: Using Predictive Analytics to Transform Library Decision Making

Ryan Litsey, Weston Mauldin

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Predictive analytics and machine learning are burgeoning areas of professional practice for large corporations especially businesses that offer products and services to customers. The power to better understand the movement of large amounts of data in a company and the capability to deploy that data to meet a customer's needs is invaluable from a services standpoint. Some in libraries have theorized that this type of data usage could possibly be used in a library service environment as well. In this article, we demonstrate how you can develop and use machine learning algorithms and predictive analytics to proactively understand library behavior. Although libraries are good at data collection, we often rely on statics or old data for assessment. Utilizing a machine learning system, called the Automated Library Information Exchange Network (ALIEN), we can better understand the movement of the items in the collection and better serve the needs of our customers the library patrons.

Original languageEnglish
Pages (from-to)140-144
Number of pages5
JournalJournal of Academic Librarianship
Volume44
Issue number1
DOIs
StatePublished - Jan 2018

Keywords

  • Access services
  • Collection development
  • Interlibrary loan
  • Machine learning
  • Predictive analytics

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