[Uusia kirjoja]

Kirjallisuusarvostelu

Agile CRM System Implementation Project

The application of information technology (IT) in customer relationship management (CRM) is growing rapidly as many companies implement CRM systems to support their numerous customer facing activities. However, failure rates of CRM projects remain notably high as they deliver scant solutions and poor user acceptance. As a consequence, it is justified to study previously researched CRM success factors and apply them to CRM system implementation. The aim of this master’s thesis was to get acquainted with relevant academic theories, frameworks and practices concerning CRM and agile development, and use them to generate a modified CRM project strategy to support the successful execution of the case company’s, Process Vision Oy, CRM implementation project. The empirical CRM system implementation project was conducted simultaneously with writing this thesis. Its theoretical findings could be transferred into practice through active participation in the CRM system development and deployment work. The project’s main goal was to produce and take into use a functioning CRM system. The goal was met, since at the time of printing this thesis the first system release was successfully published to its users at Process Vision’s marketing and sales departments. The key success elements in the CRM project were cyclic, iterative system development, customer oriented approach, user inclusion and flexible project management. Implying agile development practices ensured being able to quickly respond to changes arising during the progress of the CRM project. Throughout modelling of the core sales process formed a strong basis, on which the CRM system’s operational and analytical functionalities were built. End users were included in the initial specification of system requirements and they provided feedback on the system’s usage. To conclude, the chosen theoretical CRM roadmaps and agile development practices proved as beneficial in the successful planning and execution of the agile CRM system implementation project at Process Vision.

Comparison Structure Analysis

This study presents an automatic, computer-aided analytical method called Comparison Structure Analysis (CSA), which can be applied to different dimensions of music. The aim of CSA is first and foremost practical: to produce dynamic and understandable representations of musical properties by evaluating the prevalence of a chosen musical data structure through a musical piece. Such a comparison structure may refer to a mathematical vector, a set, a matrix or another type of data structure and even a combination of data structures. CSA depends on an abstract systematic segmentation that allows for a statistical or mathematical survey of the data. To choose a comparison structure is to tune the apparatus to be sensitive to an exclusive set of musical properties. CSA settles somewhere between traditional music analysis and computer aided music information retrieval (MIR). Theoretically defined musical entities, such as pitch-class sets, set-classes and particular rhythm patterns are detected in compositions using pattern extraction and pattern comparison algorithms that are typical within the field of MIR. In principle, the idea of comparison structure analysis can be applied to any time-series type data and, in the music analytical context, to polyphonic as well as homophonic music. Tonal trends, set-class similarities, invertible counterpoints, voice-leading similarities, short-term modulations, rhythmic similarities and multiparametric changes in musical texture were studied. Since CSA allows for a highly accurate classification of compositions, its methods may be applicable to symbolic music information retrieval as well. The strength of CSA relies especially on the possibility to make comparisons between the observations concerning different musical parameters and to combine it with statistical and perhaps other music analytical methods. The results of CSA are dependent on the competence of the similarity measure. New similarity measures for tonal stability, rhythmic and set-class similarity measurements were proposed. The most advanced results were attained by employing the automated function generation – comparable with the so-called genetic programming – to search for an optimal model for set-class similarity measurements. However, the results of CSA seem to agree strongly, independent of the type of similarity function employed in the analysis.