Choosing a wrong mobile communication price plan: An empirical analysis of predictors of the degree of tariff misfit among flat rate subscribers in Germany

Gerpott, Torsten J.; Meinert, Phil

doi:10.1016/j.tele.2016.08.003

Cited by 12 publications

(5 citation statements)

References 55 publications

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“…Their study proposed an associationbased rule mining technique to help telecom operators select optimal recharge combo deals that are appropriate for their clients. In another work, Gerpott and Meinert used hypotheses testing on data obtained from the German subsidiary of a large multinational MNO to analyze the degree of tariff plan misfit depending on the socio-demographic features [13].…”

Section: Related Workmentioning

confidence: 99%

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

Saha¹,

Tripathy²,

Masmoudi³

et al. 2022

IJACSA

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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.

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Section: Related Workmentioning

confidence: 99%

“…𝑇𝑃 𝑇𝑃+𝐹𝑃 (13) The sensitivity is the fraction of all positive samples that the classifier accurately identified as positive.…”

Section: 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =mentioning

confidence: 99%

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

Saha¹,

Tripathy²,

Masmoudi³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…However, as data revenue is compensating in part for the declining voice revenues of the mobile industry, firms within the industry must begin to consider all associated pricing problems as portfolio optimization. In doing so, all products of a mobile operator are priced as part of the entire portfolio, such that profits are maximized along with the achieved strategic objectives [35].…”

Section: Pricing Of Mobile Telecommunication Products and Servicesmentioning

confidence: 99%

A Fuzzy Logic Expert System for Pricing Digital Services: The Case of Price Adjustment for a Mobile Service Provider

Dairo¹,

Szűcs²

2020

IJFIS

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As connected smart devices and terminals continue to grow along with digital content, data traffic of mobile service providers is also growing, and the price war in mobile markets is driving traffic growth without a commensurable increase in revenue. As a result, network capital expenditure (CAPEX) investment, quality of experience, and customer experience are under enormous pressure. In a competitive mobile market, strategic pricing may play an essential role in managing this pressure only if appropriate tools are available for the service providers. In this paper, a fuzzy knowledge-based expert pricing system was developed with a focus on solving network traffic, price war, and business revenue challenges in a competitive mobile market. Its core lay in its ability to recommend digital-and data services-related price points within a competitive and price war mobile environment. The proposed pricing system was experimentally evaluated through a pilot conducted on a few segments of a mobile service provider's customer base in an emerging market and later scaled up to a broader base. Upon implementation, data services revenue increased, and overall gross margin increased with a reduction in data traffic, resulting in better throughput and network quality and, consequently, better customer experience with improved net promoter score.

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“…In the context of telecom, the inertia is not only behavioral but also due to the complexity of the array of service offerings which are difficult to compare [5]. Research on telecom subscription decisions reported the deviation from standard expectations such as resistance to switching MNOs [6], bill shocks [7], and selection of sub-optimal options [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Overcoming Choice Inertia through Social Interaction—An Agent-Based Study of Mobile Subscription Decision

Saha

Martínez-García

Bhattacharya

et al. 2022

Games

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Subscription decision in the telecom market is quite complex and cumbersome, invoking decision inertia in consumers and resulting in suboptimal choices. We implemented choice inertia and consumer interaction as an agent-based model to better understand the process. The model illustrates that with adequate peer interactions with active consumers, inactive consumers could overcome their inertia significantly and switch to a better alternative. Furthermore, the newly converted active consumers influenced their inert neighbors as a ripple effect. Active consumers contribute to firm profits and healthy market competition. Moreover, in environments with low neighborhood effects and a stronger inertia threshold, firms are able to maintain profits by retaining inert consumers. We show that apart from the attractiveness of market offerings, firms can benefit from understanding consumer inertia and devising means to reduce it.

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Choosing a wrong mobile communication price plan: An empirical analysis of predictors of the degree of tariff misfit among flat rate subscribers in Germany

Cited by 12 publications

References 55 publications

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

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

A Fuzzy Logic Expert System for Pricing Digital Services: The Case of Price Adjustment for a Mobile Service Provider

Overcoming Choice Inertia through Social Interaction—An Agent-Based Study of Mobile Subscription Decision

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