A relational classification of online banking customers
Bibliographic record
Abstract
Purpose This paper aims to classify online banking customers using demographic and relationship‐based variables and describe their profiles. Design/methodology/approach A total of 421 panellists of a large Canadian polling firm self‐administered a web‐based questionnaire. A two‐step analysis was performed using SPSS 18.0. 421 panellists of a large Canadian polling firm self‐administered a web‐based questionnaire. A two‐step analysis was performed using SPSS 18.0. Findings Six groups emerged from the analysis, four of which have higher relationship levels and two that have lower levels. Practical implications This study provides a better understanding of online banking consumer segments and offer financial institutions relevant descriptive information on each profile. This information should help the implementation of tailored marketing strategies to improve the development and maintenance of online relationships with each of the six customer segments. Originality/value This paper contributes to knowledge advancement in both the fields of relationship marketing and that of e‐commerce by providing an overview of the characteristics of relational customers in the e‐banking industry.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".