Scientific Mapping for Customer Lifetime Value Research in Organizations Using Cluster Analysis Method
Bibliographic record
Abstract
The aim of this research is to analyze and map international scientific publications in the field of customer lifetime value. This study, which follows the interpretation paradigm, is a descriptive study conducted using a systematic review method. The search terms defined in the Web of Science database were used, covering the period from 1985 to 2024. After screening and qualitatively evaluating studies, the final analysis was performed on 639 articles. The in-depth analysis of the selected articles revealed that international research in this field has been growing. Researchers have paid increasing attention to the concept of customer lifetime value over the last twenty years. However, there has been a drop in research attention in certain years such as 2008, 2017, and 2023. There is a need for more research on customer lifetime value, customer segmentation, and their connection with the keyword "data mining," reflecting the importance of this technique in the field. Additionally, countries such as Iran, Canada, and Turkey have fewer than the average number of citations, while countries like the United States, France, and Germany have more than the average number of citations, indicating different co-authorship patterns among these countries. Paying attention to the most productive and least productive countries and researchers through scientometrics can reveal research opportunities in the field of customer lifetime value in businesses and illuminate the horizon for Iranian researchers to showcase their research results at the international level.
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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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.072 | 0.068 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".