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Searching for the one: Customer relationship management software selection
Author(s) -
Cricelli Livio,
Famulari Federico Maria,
Greco Marco,
Grimaldi Michele
Publication year - 2019
Publication title -
journal of multi‐criteria decision analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.462
H-Index - 47
eISSN - 1099-1360
pISSN - 1057-9214
DOI - 10.1002/mcda.1687
Subject(s) - exemplification , customer relationship management , computer science , selection (genetic algorithm) , adaptation (eye) , software , hierarchy , process (computing) , fuzzy logic , knowledge management , analytic hierarchy process , customer needs , process management , customer satisfaction , marketing , operations research , business , artificial intelligence , engineering , market economy , philosophy , physics , epistemology , economics , optics , programming language , operating system
In a competitive environment that increasingly awards a clever approach to customer relationship management (CRM), firms need to systematize the way they interact with their customers. The relationships that often lay in the hands of managers and salespeople need to be thoughtfully organized to maximize both customer satisfaction and the effectiveness of the marketing efforts. CRM software packages can be an answer to organize and systematize the management of such commercial relationships. However, decision makers may not have the time and the competencies to identify the most suitable solution for their needs, among the hundreds existing, and may ultimately resort to an external expert. Since the existing methods to select a CRM software package suffer from several limitations, this article introduces a novel four‐step method allowing to actively involve the decision makers in the CRM software package selection, simultaneously minimizing the effort requested to them and maximizing the extent to which the final choice suits their specific needs and preferences. The method resorts to a coordinated use of the analytic hierarchy process and of its fuzzy adaptation. The article also presents an exemplification of the method in a small Italian firm.