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Friday, November 4 • 3:35pm - 4:15pm
DDA Management with Predictive Modeling

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Demand Driven Acquisitions (DDA) programs have become an integral part of academic libraries' collecting strategies. While DDA programs provide an effective way to build a just in time collection, it can be difficult to anticipate how many titles will be triggered for purchase and what the financial impact will be. This presentation will describe a project to build a predictive model to flag DDA titles that are likely to be triggered for purchase within the first year of being added to the catalog. By implementing a predictive model, collections and acquisitions departments can better plan the yearly DDA budget. In addition, titles with a high probability of being triggered for purchase can be purchased if they become ineligible for DDA. We will discuss how we combined text analytics and structured data as inputs to the model using a combination of SAS and Python. In addition to the benefits of implementing a predictive model, we will also discuss the drawbacks and limitations involved.

avatar for John Vickery

John Vickery

Analytics Coordinator & C&R Librarian for Soc.Sci., North Carolina State University Libraries
I've been with the NCSU Libraries since 2005 and programming in SAS since 2009. I also work in Python. I'm interested in applying analytical methods to library data for better organization in matters such as collections and service optimization.

Friday November 4, 2016 3:35pm - 4:15pm EDT
Laurens Room, Francis Marion Hotel 387 King Street, Charleston, SC 29403