Authors:
Victor Hugo Ferrari Canêdo Radich
;
Tania Basso
and
Regina Moraes
Affiliation:
University of Campinas - UNICAMP, Limeira, Brazil
Keyword(s):
Lead Qualification, Sentiment Analysis, Opinion Mining, Machine Learning, CRM, Lead Scoring, NLP.
Abstract:
Lead qualification is one of the main procedures in Customer Relationship Management (CRM) projects. Its main goal is to identify potential consumers who have the ideal characteristics to establish a profitable and long-term relationship with a certain organization. Social networks can be an important source of data for identifying and qualifying leads, since interest in specific products or services can be identified from the users’ expressed feelings of (dis)satisfaction. In this context, this work proposes the use of machine learning techniques and sentiment analysis as an extra step in the lead qualification process in order to improve it. In addition to machine learning models, sentiment analysis, also called opinion mining, can be used to understand the evaluation that the user makes of a particular service, product, or brand. The results indicated that sentiment analysis derived from social media data can serve as an important calibrator for the lead score, representing a sign
ificant competitive advantage for companies. By incorporating consumer sentiment insights, it becomes possible to adjust the Lead Score more accurately, enabling more effective segmentation and more targeted conversion strategies.
(More)