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Data mining in telecommunications industry
- 1. Data Mining in Telecommunications Industry Prepared by: Alhassan Hammoud, Issa Memari, Soha Yazji
- 2. Outline Introduction Types of Telecommunication Data Call Detail Data Customer Data Data Mining Applications Marketing/Customer Profiling Customer Segmentation Customer Churn Prediction
- 3. Introduction Telecommunications industry generates a tremendous amount of data Details of every call Details of every customer Billing details Services Mine the data for profitable knowledge Difficulties with mining telecommunications data Scale Rarity Raw data
- 4. Types of Telecommunications Data: Call Detail Data Descriptive information about every call is saved as a call detail record MTN generates about 110 call detail records daily for every 100 customers Call detail records include sufficient information to describe the important characteristics of each call Originating phone number Terminating phone number Date and time of the call Duration of the call Not directly used for data mining Extract knowledge at customer level
- 5. Types of Telecommunications Data: Call Detail Data Summarize call detail records associated with a single customer Summary variables (over some time period P) Average call duration Percentage of no-answer calls Percentage of calls to/from a different area code Percentage of weekday calls (Sunday - Thursday) Percentage of daytime calls (9am – 5pm) Average number of calls received per day Average number of calls originated per day Number of unique area codes called during P
- 6. Types of Telecommunications Data: Customer Detail Data Telecommunications companies maintain a database of information on their customers Name Address Service plan Credit Billing and payment history Customer data is often used in conjunction with other data to improve results
- 7. Data Mining Applications: Marketing/Customer Profiling Information mined from customer detail and call detail data can be used for marketing purposes Syriatel’s SHABABLINK offer Reduced calling fees for calls to people in one’s calling circle Add entire circles of customers Establishing and marketing international calling plans Privacy concerns
- 8. Data Mining Applications: Customer Segmentation Customer segmentation is often approached with cluster analysis K-means clustering is commonly applied to customer profile data Class Characteristics 1 High values for international call minutes and data usage 2 High values for SMS and data usage 3 High values for all call variables 4 High values for all variables 5 Average values for all variables
- 9. Data Mining Applications: Customer Churn Prediction Customer churn involves a customer leaving one telecommunication company for another Significant problem for telecommunications companies Example: companies offering incentives for signing up Mine historical data to predict customer churn Take action
- 10. Data Mining Applications: Customer Churn Prediction Binary classification problem Commonly used data mining techniques Naïve Bayes classifiers Multilayer perceptron classifiers Decision tree classifiers Evaluation criteria Highly imbalanced dataset F-measure
- 11. Data Mining Applications: Customer Churn Prediction Confusion matrix Percentage of positive cases caught: recall = 60/100 Percentage of correct positive predictions: precision = 60/200 F-measure: 𝐹 = 2 × 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛×𝑅𝑒𝑐𝑎𝑙𝑙 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑅𝑒𝑐𝑎𝑙𝑙 Predicted Negative Predicted Positive Negative Cases TN: 9760 FP: 140 Positive Cases FN: 40 TP: 60
- 12. References AlOmari D., Hassan M.M. (2016) Predicting Telecommunication Customer Churn Using Data Mining Techniques. In: Li W. et al. (eds) Internet and Distributed Computing Systems. IDCS 2016. Lecture Notes in Computer Science, vol 9864. Springer, Cham. Weiss, G.M. (2005) "Data mining in telecommunications" Data Mining and Knowledge Discovery Handbook. Springer US.