An empirical study to identify and rank CSFs in customer relationship management (CRM): A case study of oil products distribution
Bibliographic record
Abstract
Customer relationship management (CRM) is founded based on the value exchange between organization and customers and focuses merely on the value created in this connection.In this paper, the critical success factors are identified for a proper and effective implementation of CRM for an oil distribution company.The proposed survey of this paper identifies some important factors affecting the CRM implementation and determines the most important ones using a survey.The results indicate that there are twelve factors playing the most important roles on the success of CRM.There are CRM strategy, knowledge management in customer relationship, CRM technology, effective strategic committee, management of customer contact channels, customer information management, customer-oriented change management, training programs, strategic communication with staff, staff commitment, integration, and sectional implementation.We categorize the factors into two levels based on the level of their importance.The first level consists of the most important ones include only four items and the other eight items are categorized in level 2.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".