TY - GEN
T1 - Practical Implementation of Machine Learning and Predictive Analytics in Cellular Network Transactions in Real Time
AU - Nestor, Dahj Muwawa Jean
AU - Ogudo, Kingsley A.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/13
Y1 - 2018/9/13
N2 - In order to keep a high revenue stream, Communication Service Providers in general, Network Mobile Operators specifically need to ensure a good level of customer satisfaction by assigning a big weight on the user's Quality of Experience (QoE). With billions of transactions done by customers on both voice and data daily, Communication Service Providers (CSPs) shift the focus in studying customer behavior and data patterns to pinpoint opportunities to improve customer services, service quality and predict when customers are likely to terminate contracts, to perhaps move to another CSP. CSPs have managed to build efficient IT infrastructures to store customer transactions. These exist in many forms such as file systems, databases, etc. In this paper, a simplified predictive analytics is done using the (Customer Relationship Management) CRM information records to classify potential customers likely to terminate their contracts, using logistic regression and random forest models. The paper describes the process to build a simple predictive models to apply on a telecoms dataset.
AB - In order to keep a high revenue stream, Communication Service Providers in general, Network Mobile Operators specifically need to ensure a good level of customer satisfaction by assigning a big weight on the user's Quality of Experience (QoE). With billions of transactions done by customers on both voice and data daily, Communication Service Providers (CSPs) shift the focus in studying customer behavior and data patterns to pinpoint opportunities to improve customer services, service quality and predict when customers are likely to terminate contracts, to perhaps move to another CSP. CSPs have managed to build efficient IT infrastructures to store customer transactions. These exist in many forms such as file systems, databases, etc. In this paper, a simplified predictive analytics is done using the (Customer Relationship Management) CRM information records to classify potential customers likely to terminate their contracts, using logistic regression and random forest models. The paper describes the process to build a simple predictive models to apply on a telecoms dataset.
KW - Artificial Intelligence (AI)
KW - CRM
KW - CSP
KW - Logistic Regression
KW - Machine Learning
KW - Predictive Analytics
KW - Random Forest
KW - Telecommunications
UR - http://www.scopus.com/inward/record.url?scp=85054673955&partnerID=8YFLogxK
U2 - 10.1109/ICABCD.2018.8465476
DO - 10.1109/ICABCD.2018.8465476
M3 - Conference contribution
AN - SCOPUS:85054673955
SN - 9781538630600
T3 - 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems, icABCD 2018
BT - 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems, icABCD 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems, icABCD 2018
Y2 - 6 August 2018 through 7 August 2018
ER -