Titre :

Big data and open-source computation solutions, opportunities and challenges for marketing scientists. Applications to customer base predictive modeling using RFM variables

Auteur(s) :

Moulins, Jean-Louis [Auteur]
Centre de Recherche sur le Transport et la Logistique [CRET-LOG]
Francis, Salerno [Auteur]
Lille économie management - UMR 9221 [LEM]
Calciu, Michel [Auteur]

Titre de la manifestation scientifique :

Marketing Trends

Ville :

Venise

Pays :

Italie

Date de début de la manifestation scientifique :

2016-01

Mot(s)-clé(s) en anglais :

Big data
Map Reduce
HPC
predictive modeling
RFM

Résumé en anglais : [en]

Marketing researchers and analysts, confined in classical transactional marketing paradigms where customer knowledge was limited to sample based surveys and panels, have somehow been overwhelmed by the avalanche of behavioral ...
Lire la suite >Marketing researchers and analysts, confined in classical transactional marketing paradigms where customer knowledge was limited to sample based surveys and panels, have somehow been overwhelmed by the avalanche of behavioral data coming from new digital and relationship marketing techniques. This occurred up to a point where a part of what belonged to their core competencies has been overtaken by computer scientists. The relatively recent open-source solutions that form an ecosystem around the most elegant statistical system, R and the distributed computation system Hadoop (some kind of Linux for computer clusters) democratize BigData calculations and offer excellent opportunities to marketing analysts to operate huge computing factories. An illustration of the implementation of the evoked solutions will be presented, applications to customer data base predictive modeling using RFM variables will be developed and performance gains will be tested.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Lille Économie Management (LEM) - UMR 9221

Source :

Harvested from HAL