Publication2021
Customer Profiling and Retention Using Recommendation System and Factor Identification to Predict Customer Churn in Telecom Industry
The record
- Type
- Publication
- Date
- 1 January 2021
- Level
- National
- Journal
- Studies in Big Data
- Volume
- 87
- Pages
- 155-172
- Published
- 1 January 2021
- DOI
- 10.1007/978-981-33-6518-6_9
- Scopus ID
- 85132905184
- ISSN
- 21976503
- Authors
- 4
- Open access
- No
- Citations
- 6
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
8.5 Achieve full and productive employment and decent work for all, with equal pay for work of equal value
Machine learning churn prediction and customer retention strategies optimize telecom business productivity and employment through data-driven decision-making.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-2.3 Services and the knowledge economy
Customer analytics and recommendation systems enhance e-commerce and services sector efficiency through machine learning optimization.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Customer Profiling and Retention Using Recommendation System and Factor Identification to Predict Customer Churn in Telecom Industry”. Kortex.