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Towards CRM And ERP Integration: an AI-powered Solution for Revenues Prediction

Pietro Martino

Towards CRM And ERP Integration: an AI-powered Solution for Revenues Prediction.

Rel. Paolo Garza. Politecnico di Torino, Corso di laurea magistrale in Communications And Computer Networks Engineering (Ingegneria Telematica E Delle Comunicazioni), 2019

Abstract:

Enterprise Resource Planning is a subject of fundamental importance for companies of all sizes, allowing them to efficiently manage company assets. Nonetheless, a proper ERP process requires a non-negligible amount of up-to-date and reliable information in order to be able to effectively schedule enterprise resources. On the other hand, more and more companies start adopting Customer Relationship Management tools - mainly in the form of software solutions - to better handle their interactions with customers, making available a huge amount of data relative to the company's sales history. However, such data is usually just an historic collection and is not directly exploitable for ERP purposes. The solution we propose here aims to exploit CRM historical data to predict a company's revenues using machine learning methods in the context of a consulting firm. By looking at the past history of contract negotiations, we predict a winning probability for each contract opportunity and we subsequently aggregate them to have a single forecast of revenues. The goal of this research is to valorize CRM data and to extract from it valuable information for ERP purposes, such as a prediction of a company's revenues.

Relatori: Paolo Garza
Anno accademico: 2018/19
Tipo di pubblicazione: Elettronica
Numero di pagine: 86
Informazioni aggiuntive: Tesi secretata. Fulltext non presente
Soggetti:
Corso di laurea: Corso di laurea magistrale in Communications And Computer Networks Engineering (Ingegneria Telematica E Delle Comunicazioni)
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-27 - INGEGNERIA DELLE TELECOMUNICAZIONI
Ente in cotutela: TELECOM ParisTech (FRANCIA)
Aziende collaboratrici: HYPERCUBE RESEARCH
URI: http://webthesis.biblio.polito.it/id/eprint/10859
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