Abstract

The performance of customer behavior models depends on both the predictive accuracy and the cost of incorrect predictions. Previous research showed that including context in the customer behavior models can improve the accuracy. Improving the accuracy does not necessarily mean that the misclassification cost decreases. The aim of this paper is to understand whether including context in a predictive model reduces the misclassification costs and in which conditions this happens. Experimental analyses were done by varying the market granularity, the dependent variable and the context granularity. The results show that context leads to a decrease in the misclassification cost when the unit of analysis is the single customer or the micro-segment. The exceptions may occur when the unit of analysis is a segment. These findings have significant implications for companies that have to decide whether to gather context and how to exploit it best when they build predictive models.

Autore Pugliese

• Gorgoglione Michele , Panniello Umberto

Tutti gli autori

• Lombardi S , Gorgoglione M , Panniello U

Titolo volume/Rivista

EXPERT SYSTEMS WITH APPLICATIONS

Anno di pubblicazione

2013

ISSN

0957-4174

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

Non Disponibile

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile