Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study
Abstract Given the growing importance of customer behavior in the business market nowadays, telecom operators focus not only on customer profitability to increase market share but also on highly loyal customers as well as customers who are churn. The emergence of big data concepts introduced a new w...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-04-01
|
| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40537-020-00290-0 |
| _version_ | 1818958565379932160 |
|---|---|
| author | Wissam Nazeer Wassouf Ramez Alkhatib Kamal Salloum Shadi Balloul |
| author_facet | Wissam Nazeer Wassouf Ramez Alkhatib Kamal Salloum Shadi Balloul |
| author_sort | Wissam Nazeer Wassouf |
| collection | DOAJ |
| description | Abstract Given the growing importance of customer behavior in the business market nowadays, telecom operators focus not only on customer profitability to increase market share but also on highly loyal customers as well as customers who are churn. The emergence of big data concepts introduced a new wave of Customer Relationship Management (CRM) strategies. Big data analysis helps to describe customer’s behavior, understand their habits, develop appropriate marketing plans for organizations to identify sales transactions and build a long-term loyalty relationship. This paper provides a methodology for telecom companies to target different-value customers by appropriate offers and services. This methodology was implemented and tested using a dataset that contains about 127 million records for training and testing supplied by Syriatel corporation. Firstly, customers were segmented based on the new approach (Time-frequency- monetary) TFM (TFM where: Time (T): total of calls duration and Internet sessions in a certain period of time. Frequency (F): use services frequently within a certain period. Monetary (M): The money spent during a certain period.) and the level of loyalty was defined for each segment or group. Secondly, The loyalty level descriptors were taken as categories, choosing the best behavioral features for customers, their demographic information such as age, gender, and the services they share. Thirdly, Several classification algorithms were applied based on the descriptors and the chosen features to build different predictive models that were used to classify new users by loyalty. Finally, those models were evaluated based on several criteria and derive the rules of loyalty prediction. After that by analyzing these rules, the loyalty reasons at each level were discovered to target them the most appropriate offers and services. |
| first_indexed | 2024-12-20T11:27:46Z |
| format | Article |
| id | doaj.art-c0e530b9252b4a4fac87dd94bee7913e |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-20T11:27:46Z |
| publishDate | 2020-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-c0e530b9252b4a4fac87dd94bee7913e2022-12-21T19:42:19ZengSpringerOpenJournal of Big Data2196-11152020-04-017112410.1186/s40537-020-00290-0Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case studyWissam Nazeer Wassouf0Ramez Alkhatib1Kamal Salloum2Shadi Balloul3Faculty of Information Technology-Department of software engineering and information systems, Al-Baath UniversityFaculty of Applied Sciences, Hama UniversityFaculty of Information Technology-Department of software engineering and information systems, Al-Baath UniversityFaculty of Information Technology, Higher Institute for Applied Sciences and TechnologyAbstract Given the growing importance of customer behavior in the business market nowadays, telecom operators focus not only on customer profitability to increase market share but also on highly loyal customers as well as customers who are churn. The emergence of big data concepts introduced a new wave of Customer Relationship Management (CRM) strategies. Big data analysis helps to describe customer’s behavior, understand their habits, develop appropriate marketing plans for organizations to identify sales transactions and build a long-term loyalty relationship. This paper provides a methodology for telecom companies to target different-value customers by appropriate offers and services. This methodology was implemented and tested using a dataset that contains about 127 million records for training and testing supplied by Syriatel corporation. Firstly, customers were segmented based on the new approach (Time-frequency- monetary) TFM (TFM where: Time (T): total of calls duration and Internet sessions in a certain period of time. Frequency (F): use services frequently within a certain period. Monetary (M): The money spent during a certain period.) and the level of loyalty was defined for each segment or group. Secondly, The loyalty level descriptors were taken as categories, choosing the best behavioral features for customers, their demographic information such as age, gender, and the services they share. Thirdly, Several classification algorithms were applied based on the descriptors and the chosen features to build different predictive models that were used to classify new users by loyalty. Finally, those models were evaluated based on several criteria and derive the rules of loyalty prediction. After that by analyzing these rules, the loyalty reasons at each level were discovered to target them the most appropriate offers and services.http://link.springer.com/article/10.1186/s40537-020-00290-0TFMRFMCustomer loyaltyClassification algorithmsCustomer behaviorMachine learning |
| spellingShingle | Wissam Nazeer Wassouf Ramez Alkhatib Kamal Salloum Shadi Balloul Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study Journal of Big Data TFM RFM Customer loyalty Classification algorithms Customer behavior Machine learning |
| title | Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study |
| title_full | Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study |
| title_fullStr | Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study |
| title_full_unstemmed | Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study |
| title_short | Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study |
| title_sort | predictive analytics using big data for increased customer loyalty syriatel telecom company case study |
| topic | TFM RFM Customer loyalty Classification algorithms Customer behavior Machine learning |
| url | http://link.springer.com/article/10.1186/s40537-020-00290-0 |
| work_keys_str_mv | AT wissamnazeerwassouf predictiveanalyticsusingbigdataforincreasedcustomerloyaltysyriateltelecomcompanycasestudy AT ramezalkhatib predictiveanalyticsusingbigdataforincreasedcustomerloyaltysyriateltelecomcompanycasestudy AT kamalsalloum predictiveanalyticsusingbigdataforincreasedcustomerloyaltysyriateltelecomcompanycasestudy AT shadiballoul predictiveanalyticsusingbigdataforincreasedcustomerloyaltysyriateltelecomcompanycasestudy |