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Who do we think you are?
Recurso electrónico / Electronic resource
MARC record
Tag12Value
LDR  00000cab a2200000 4500
001  MAP20200039662
003  MAP
005  20220911211057.0
008  201221e20201201gbr|||p |0|||b|eng d
040  ‎$a‎​MAP‎$b‎​spa‎$d‎​MAP
100  ‎$0‎​MAPA20200024149‎$a‎​Bond, Chantal ‌
24510‎$a‎​Who do we think you are?‎$c‎​Chantal Bond, Kai Zhu
520  ‎$a‎​Marriage and birth rates continue to decline worldwide, and home ownership rates have plummeted in a number of developed economies. A traditional life insurance consumer segmentation approach, which seeks to focus on the socioeconomic and demographic drivers for life events that lead to insurance purchases, will begin to lose its relevance in this context. At the same time, insurers have access to a rapidly growing pool of data about consumers but few have managed to really get to grips with it. How can this data be used to enhance understanding of consumer needs and therefore gain insights that lead to better outcomes for insurers and customers? We will demonstrate a data analytics approach to consumer segmentation that uses machine learning techniques such as k-mean clustering and random forest classification, which can be applied to a variety of data sources. In this example, we will use data from a consumer needs survey commissioned by the IFoA Life Asia Sub-committee to identify and describe three distinct consumer segments based on their responses to the survey questions.
650 4‎$0‎​MAPA20080624019‎$a‎​Comportamiento del consumidor ‌
650 4‎$0‎​MAPA20170005476‎$a‎​Machine learning ‌
650 4‎$0‎​MAPA20080578848‎$a‎​Análisis de datos ‌
650 4‎$0‎​MAPA20080550592‎$a‎​Encuestas ‌
7001 ‎$0‎​MAPA20200024170‎$a‎​Zhu, Kai ‌
7730 ‎$w‎​MAP20200013259‎$t‎​The Actuary : the magazine of the Institute & Faculty of Actuaries‎$d‎​London : Redactive Publishing, 2019-‎$g‎​01/12/2020 Número 11 - diciembre 2020 , p. 32-34