Scientific Mapping for Customer Lifetime Value Research in Organizations Using Cluster Analysis Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it