A Machine Learning Model for Personalized Tariff Plan based on Customer’s Behavior in the Telecom Industry

Abstract

In the telecommunication industry, being able to predict customers' behavioral pattern to successfully design and recommend a suitable tariff plan is the ultimate target. The behavioral pattern has a vital connection with the customers' demographic background. Different researches have been done based on hypothesis testing, regression analysis, and conjoint analysis to determine the interdependencies among them and the effects on the customers' behavioral needs. This has presented us with ample scope for research using numerous classification-based techniques. This work proposes a model to predict customer's behavioral pattern by using their demographic data. This model was built after investigating various types of classification-based machine learning techniques including the traditional ones like decision tree, k-nearest neighbor, logistic regression, and artificial neural networks along with some ensemble techniques such as random forest, adaboost, gradient boosting machine, extreme gradient boosting, bagging, and stacking. They are applied to a dataset collected using a questionnaire in India. Among the traditional classifiers, decision tree gave the best result of 81% accuracy and random forest showed the best result among the ensemble learning techniques with an accuracy of 83%. The proposed model has shown a very positive outcome in predicting the customers' behavioral pattern.

Fuzzy Support Vector Machine based Fall Detection Method for Traumatic Brain Injuries

“…Sensitivity and specificity are calculated by (17) and (18), respectively. Accuracy could be defined by (19), which is the ratio of all samples that the classifier correctly classified [58]. The precision metric could be described by (20), which evaluates the number of correct positive predictions made.…”

Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field

“…Cluster 3: Customer behavioral Mainly positioned as a basic theme, it encloses studies that use predictive analytics, data mining, clustering, and FIGURE 20: Thematic Map through co-word analysis classification techniques to analyze and understand customer behavior, personalize tariff plans [74], predict customer engagement behavior in response to marketing posts [75] and incorporate opinion mining and sentiment analysis in advertising strategies [76]. They also apply machine learning models to identify patterns and insights from large datasets in the electricity [77] and water consumption [78] industries.…”

“…• Customer Behavior • Technology Adoption Green cluster (54 items): [64], artificial neural networks to enhance the understanding of economic decisionmaking processes [65], and data mining algorithms to personalize tariff plans and forecast customer engagement behavior [74] [75]. • On the other hand, behavioral finance-related topics, especially investor behavioral and stock market studies, are on the rise.…”