Understanding users: from man-made typologies to computer-generated clusters

Abstract

Purpose – The aim of this article is to identify the main approaches and discuss their perspectives, including their strengths and weaknesses in, especially, public library contexts. The purpose is also to present and discuss the results of a recent – 2014 – Danish library user segmentation project using computer-generated clusters. Compared to traditional marketing texts, this article also tries to identify users segments or images created by the library profession itself. Segmentation of users can help libraries in the process of understanding user similarities and differences. Segmentation can also form the basis for selecting segments as target users and for developing tailored services for specific target segments. Thus, several approaches and techniques have been tested in library contexts. Design/methodology/approach – Basically, the article is built upon a literature review concerning different approaches to user segmentation in, especially, public library context from approximately 1980 till now (May 2014). Findings – The article reveals that – at least – five different principal approaches to user segmentation have been applied by the library sector during the past 30-35 years. Characteristics, strengths and weaknesses of the different approaches are identified, discussed and evaluated. Practical implications – When making decisions on future library user surveys, it is certainly an advantage, concerning the ability to make qualified decision, to know what opportunities that are at hand for identifying important segments. Originality/value – Some of the approaches have been treated individually in the library literature; however, it is probably the first time that the professions own user images and metaphors are dealt with in a user segmentation context.

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