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  • The objective of the paper is to demonstrate the abilities and possible approaches to classification of set of objects using self-organizing maps. As the objects, clients of an insurance company that made an agreement regarding mandatory insurance of motor vehicles were selected. The opinions of the clients and their overall satisfaction reflected in responses to presented answers. The clients were classified into three groups. The first two contained satisfied clients (i.e. good clients for the company), the last group contained clients that could potentially switch to the competitors. Subsequent analysis enabled discovering the reasons of low customer satisfaction and critical factors of losing the least satisfied clients. For the analysis of the responses (one hundred fifty-one) and the insurance company, experimental model of self-organizing map realized at the Department of informatics was used. Used experimental model has proved very effective software tool.
  • The objective of the paper is to demonstrate the abilities and possible approaches to classification of set of objects using self-organizing maps. As the objects, clients of an insurance company that made an agreement regarding mandatory insurance of motor vehicles were selected. The opinions of the clients and their overall satisfaction reflected in responses to presented answers. The clients were classified into three groups. The first two contained satisfied clients (i.e. good clients for the company), the last group contained clients that could potentially switch to the competitors. Subsequent analysis enabled discovering the reasons of low customer satisfaction and critical factors of losing the least satisfied clients. For the analysis of the responses (one hundred fifty-one) and the insurance company, experimental model of self-organizing map realized at the Department of informatics was used. Used experimental model has proved very effective software tool. (en)
Title
  • Evolution of insurance company service quality survey, using self-learning neural network
  • Evolution of insurance company service quality survey, using self-learning neural network (en)
skos:prefLabel
  • Evolution of insurance company service quality survey, using self-learning neural network
  • Evolution of insurance company service quality survey, using self-learning neural network (en)
skos:notation
  • RIV/62156489:43110/11:00171223!RIV12-MSM-43110___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6215648904)
http://linked.open...iv/cisloPeriodika
  • 2
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 198490
http://linked.open...ai/riv/idVysledku
  • RIV/62156489:43110/11:00171223
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • classification; representation in plane; insurance company; self organizing; neural network; class representative (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • CZ - Česká republika
http://linked.open...ontrolniKodProRIV
  • [93918A45D986]
http://linked.open...i/riv/nazevZdroje
  • Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 2011
http://linked.open...iv/tvurceVysledku
  • Dvořáková, Dana
  • Konečný, Vladimír
  • Trenz, Oldřich
http://linked.open...n/vavai/riv/zamer
issn
  • 1211-8516
number of pages
http://localhost/t...ganizacniJednotka
  • 43110
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