Strategic planning and customer intelligence in academic libraries

Abstract

Purpose: The paper refers to a current discussion on the effectiveness and efficiency of Bielefeld University Library and concentrates on requirements and conditions of implementing customer intelligence in academic libraries. Moreover, a conceptual framework for a library management information system based on a data warehouse that links external and internal data to support strategic planning processes is introduced. Design/methodology/approach: Content-related and technical aspects of customer intelligence in academic libraries are outlined and analogies are drawn to commercial enterprises to motivate the conceptual reflections. The paper closes with two examples that demonstrate how multifaceted the data pool for customer intelligence can be in librarianship. Findings: The paper sensitizes to the advantages of systematically generating customer knowledge in academic libraries for strategic planning and customer orientation. Practical implications: The suggested approach can serve as a basis for the development of data-based decision support systems focusing on the tracking of the usage of library services and customer preferences over time. Originality/value: Up to now the discussion of customer intelligence as a foundation of strategic planning in academic libraries has been almost a blank space in the literature. The paper contributes to fill this gap.

The strategic evaluation of academic libraries

“…Strategic planning has an important role in academic libraries (Adeyoyin, 2005; Huotari and Iivonen, 2005; Decker and Höppner, 2006). The libraries have developed networked cooperation to develop their activities.…”

Old wine in a new bottle: customer orientation in librarianship

“…That knowledge in turn can influence strategic planning and other long-term pursuits. In an application of this idea, Decker and Höppner (2006) explore the use of customer intelligence to inform decision making in strategic planning using data warehouses. A library staff with high CO embraces serving all customers and takes pleasure out of doing so.…”

Literature Review of Data Mining Applications in Academic Libraries

“…In the USA, it is important to highlight the study presented by Nicholson (2003a) that compares the effectiveness of four different data mining functions: logistic regression, memory-based reasoning, decision/classification tree and neural networks to discover Webbased scholarly research works. Moreover, in 2006, two out of four articles report a case study implementation in Germany, all implemented by Decker and colleagues (Decker & Hermelbracht, 2006;Decker & Höppner, 2006). Data analysis and characterization are the most used techniques, except for the years 2002-2004, in which more formal data mining techniques are implemented.…”

“…Specifically, association rules are employed to open up the relationship between pairs of material categories to predict the users' needs. Decker & Höppner (2006) apply an association rules-based approach to explore the use of customer intelligence to support strategic planning processes using data warehouse tools. Zhang & Wang (2013) report the implementation of association rules to mine transactional data generated in the process of library service.…”