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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Hierarchical Agglomerative Method for Improving NPS
verfasst von : Jieyan Kuang, Zbigniew W. Raś, Albert Daniel
Erschienen in: Pattern Recognition and Machine Intelligence
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Abstract
The paper proposes a new strategy called HAMIS to improve NPS (Net Promoter Score) of certain companies involved in heavy equipment repair in the US and Canada - we call them clients. HAMIS is based on the semantic dendrogram built by using agglomerative clustering strategy and semantic distance between clients. More similar is the knowledge extracted from two clients, more close these clients semantically are to each other. Each company is represented by a dataset which is built from answers to the questionnaire sent to a number of randomly chosen customers using services offered by this company. Before knowledge is extracted from these datasets, each one is extended by merging it with datasets which are close to it in the semantic dendrogram, have higher NPS, and if classifiers extracted from them have higher FS-score. Action rules are extracted from these extended datasets and used for providing recommendations to clients of how to improve their businesses.