@article {Lamrhari2022,
	title = {A social CRM analytic framework for improving customer retention, acquisition, and conversion},
	journal = {Technological Forecasting and Social Change},
	volume = {174},
	year = {2022},
	note = {cited By 4},
	abstract = {Social Customer Relationship Management (social CRM) has become one of the central points for many companies seeking to improve their customer experience. It comprises a set of processes that allows decision-makers to analyze customer data, in order to launch an efficient, customer-centric, and cost-effective marketing strategy. Nonetheless, the inclusion of social media data in CRM introduces new challenges, as it requires advanced analytical approaches to extract actionable insight from such a huge amount of data. Thus, in this paper, we propose a social CRM analytic framework, which includes various analytical approaches, aiming at improving customer retention, acquisition, and conversion. This framework has been tested on various datasets and extensively evaluated based on several performance metrics. The obtained results suggest that the proposed framework can effectively extract relevant information and support decision-making processes. From an academics perspective, the study contributes to an understanding of customers{\textquoteright} experiences throughout their engagement on social media and focuses on long-term relationships with customers. From a managerial perspective, companies should leverage the insight generated through every customer engagement on social media to drive effective marketing strategies. {\textcopyright} 2021},
	keywords = {Acquisition, Conversion, Cost effectiveness, Customer acquisition, Customer conversion, Customer relationship management, Customer retention, Customer satisfaction, Customers{\textquoteright} satisfaction, Decision making, Digital storage, Machine learning, management, marketing, Public relations, Sales, Sentiment analysis, Social CRM, social media, Social networking (online), Strategic planning},
	doi = {10.1016/j.techfore.2021.121275},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118116967\&doi=10.1016\%2fj.techfore.2021.121275\&partnerID=40\&md5=10ffdb7f551bf1e135c01fb362d1a559},
	author = {Lamrhari, S. and Ghazi, H.E. and Oubrich, M. and Faker, A.E.}
}
@conference {Lamrhari2020,
	title = {Random Forest-based Approach for Classifying Customers in Social CRM},
	booktitle = {2020 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2020},
	year = {2020},
	note = {cited By 0},
	abstract = {Social Customer Relationship Management (social CRM) has become one of the central points for many companies seeking to improve their customer experience. It comprises a set of processes that allows decision-makers to analyze customer data in order to launch an efficient customer-centric and cost-effective marketing strategy. However, targeting all potential customers with one general marketing strategy seems to be inefficient. While targeting each potential customer with a specific strategy can be cost demanding. Thus, it is essential to group customers into specific classes and target each class according to its respective customer needs. In this paper, we develop a Random Forest- based approach to classify potential customers into three main categories namely, prospects, satisfied and unsatisfied customers. The proposed model has been trained, tested, and compared to some state-of-the-art classifiers viz., Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) based on several metrics including accuracy, sensitivity, specificity, false positive rate, and false negative rate. The reported results were satisfactory with an accuracy of 98.46\%, a sensitivity of 97.69\%, and a specificity of 98.84\%. {\textcopyright} 2020 IEEE.},
	keywords = {Commerce, Cost effectiveness, Customer experience, Customer relationship management, Decision making, Decision trees, False negative rate, False positive rates, Industrial management, K nearest neighbor (KNN), Marketing strategy, Nearest neighbor search, Neural networks, Potential customers, Public relations, Random forests, Sales, State of the art, Strategic planning, Support vector machines},
	doi = {10.1109/ICTMOD49425.2020.9380602},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103738578\&doi=10.1109\%2fICTMOD49425.2020.9380602\&partnerID=40\&md5=c672980200a36b34b0ae6df0842ef948},
	author = {Lamrhari, S. and Elghazi, H. and El Faker, A.}
}
@conference {Lamrhari201863,
	title = {Enhancing social network communication through dynamic clustering balance},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {2018-March},
	year = {2018},
	pages = {63-69},
	doi = {10.1145/3177148.3180090},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047165145\&doi=10.1145\%2f3177148.3180090\&partnerID=40\&md5=1341cf27a9a6e4281eb6d209f37ec6ea},
	author = {Lamrhari, S. and Elghazi, H. and Tigani, S. and Faker, A.El. and Sadiki, T.}
}
