Dynamic Pricing Models in Telecom: Implementation of Real Time, Dynamic Pricing Strategies through Artificial Intelligence
Bibliographic record
Abstract
This study investigates the deployment of real-time dynamic pricing strategies in the telecommunications sector using artificial intelligence (AI). The primary objective is to evaluate how AI techniques can optimize pricing models in response to fluctuating user behavior, network usage, and market dynamics. Using machine learning algorithms and big data analytics, telecom operators are able to collect and interpret real-time data to make informed pricing decisions. Key AI methods explored include reinforcement learning for adaptive pricing, clustering to segment user profiles, and predictive analytics for demand forecasting. The research includes case studies of telecom providers that have adopted AI-driven pricing frameworks, analyzing their outcomes in terms of revenue growth, customer retention, and network efficiency. The findings indicate that dynamic pricing enabled by AI significantly improves operational performance while delivering personalized customer experiences. However, the implementation process is challenged by issues such as data privacy, regulatory compliance, and the high computational demands of real-time systems. The study concludes with strategic recommendations for future adoption, emphasizing the need for ethical AI governance, algorithmic transparency, and ongoing performance monitoring to ensure sustainable and responsible use of dynamic pricing in telecommunications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".